a467500 report molecular materials modeling

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WTEC Panel Report on

APPLICATIONS OF MOLECULAR AND MATERIALS MODELING

Phillip R. Westmoreland (Panel Chair)Peter A. Kollman (Panel Vice Chair)Anne M. ChakaPeter T. CummingsKeiji MorokumaMatthew NeurockEllen B. StechelPriya Vashishta

January 2002

International Technology Research InstituteR.D. Shelton, Director

Geoffrey M. Holdridge, WTEC Division Director and Series Editor

4501 North Charles Street

Baltimore, Maryland 21210-2699

International Technology Research InstituteWorld Technology (WTEC) Division


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WTEC PANEL ON APPLICATIONS OF MOLECULAR AND MATERIALS MODELING

Sponsored by the National Science Foundation, the Department of Energy, the National Institute of Standards andTechnology, the Defense Advanced Research Projects Agency, the Air Force Office of Scientific Research, and the

National Institutes of Health of the United States Government.

Phillip R. Westmoreland (Panel Chair)Department of Chemical EngineeringUniversity of Massachusetts Amherst159 Goessmann, 686 N. Pleasant St.Amherst, MA 01003-9303

Keiji MorokumaDepartment of ChemistryEmory University1515 Pierce DriveAtlanta, Georgia 30322

Peter A. Kollman (Panel Vice Chair; deceased)Department of Pharmaceutical ChemistryUniversity of California San FranciscoSchool of PharmacySan Francisco, CA 94143

Matthew NeurockDepartment of Chemical EngineeringUniversity of VirginiaCharlottesville, VA 22903-2442

Anne M. Chaka (previously at Lubrizol, Inc.)National Institute of Standards and TechnologyPhysical and Chemical Properties Division (838)Chemistry Building (222), Room A155100 Bureau Drive, Stop 8380Gaithersburg, MD 20899-8380

Ellen B. StechelFord Research Laboratory, Ford Motor CompanyScientific Research Laboratory, 2101 Village RoadMail Drop 3083SRL/Rm. 3313Dearborn, MI 48124-2053

Peter T. CummingsDepartment of Chemical Engineering,University of TennesseeOak Ridge National LaboratoryUniversity of TennesseeKnoxville, TN 37996-2200

Priya VashishtaDepartments of Physics & Astronomyand of Computer ScienceNicholson HallLouisiana State UniversityBaton Rouge, LA 70803-4001

Special thanks to Sharon C. Glotzer (NIST, now at Univ. Michigan) Karl K. Irikura (NIST), Raul Miranda (NSF, now at DOE),Randall S. Jones (Loyola College), and Anna Tsao (Center for Computing Sciences), who contributed site reports to this volume.

INTERNATIONAL TECHNOLOGY RESEARCH INSTITUTE

World Technology (WTEC) Division

WTEC at Loyola College (previously known as the Japanese Technology Evaluation Center, JTEC) provides assessmentsof foreign research and development in selected technologies under a cooperative agreement with the National ScienceFoundation (NSF). Loyolas International Technology Research Institute (ITRI), R.D. Shelton, Director, is the umbrellaorganization for WTEC. Elbert Marsh, Deputy Assistant Director for Engineering at NSFs Engineering Directorate, is NSFProgram Director for WTEC. Several other U.S. government agencies provide support for the program through NSF.

WTECs mission is to inform U.S. scientists, engineers, and policymakers of global trends in science and technology in a

manner that is timely, credible, relevant, efficient and useful. WTEC assessments cover basic research, advanceddevelopment, and applications. Panels of typically six technical experts conduct WTEC assessments. Panelists are leadingauthorities in their field, technically active, and knowledgeable about U.S. and foreign research programs. As part of theassessment process, panels visit and carry out extensive discussions with foreign scientists and engineers in their labs.

The ITRI staff at Loyola College help select topics, recruit expert panelists, arrange study visits to foreign laboratories,organize workshop presentations, and finally, edit and disseminate the final reports.

WTEC has now been spun off to a private, nonprofit corporation that will conduct all future WTEC studies, while

continuing to assist in dissemination of older WTEC reports. See http://www.wtec.org.

Dr. R. D. SheltonITRI DirectorLoyola CollegeBaltimore, MD 21210

Mr. Geoff HoldridgeWTEC Division DirectorLoyola CollegeBaltimore, MD 21210

Dr. George GamotaITRI Associate Director17 Solomon Pierce RoadLexington, MA 02173


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WTEC Panel on

APPLICATIONS OF MOLECULAR AND MATERIALS

MODELING

FINAL REPORT

January 2002

Phillip R. Westmoreland (Panel Chair)Peter A. Kollman (Panel Vice Chair)

Anne M. ChakaPeter T. CummingsKeiji MorokumaMatthew NeurockEllen B. StechelPriya Vashishta

This document was sponsored by the National Science Foundation (NSF), the Department of Energy, the NationalInstitute of Standards and Technology, the Defense Advanced Research Projects Agency, the Air Force Office ofScientific Research, and the National Institutes of Health of the U.S. Government under NSF Cooperative AgreementENG-9707092, awarded to the International Technology Research Institute at Loyola College in Maryland. Thegovernment has certain rights to this material. Any opinions, findings, and conclusions or recommendations expressedin this material are those of the authors and do not necessarily reflect the views of the United States Government, theauthors parent institutions, or Loyola College.


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ABSTRACT

This report reviews the development and applications of molecular and materials modeling in Europe and

Japan in comparison to those in the United States. Topics covered include computational quantum chemistry,molecular simulations by molecular dynamics and Monte Carlo methods, mesoscale modeling of materialdomains, molecular-structure/macroscale property correlations like QSARs and QSPRs, and relatedinformation technologies like informatics and special-purpose molecular-modeling computers. The panelsfindings include the following: The United States leads this field in many scientific areas. However, Canadahas particular strengths in DFT methods and homogeneous catalysis; Europe in heterogeneous catalysis,mesoscale, and materials modeling; and Japan in materials modeling and special-purpose computing. Major

government-industry initiatives are underway in Europe and Japan, notably in multi-scale materials modeling

and in development of chemistry-capable ab-initio molecular dynamics codes. In European and U.S.

assessments of nanotechnology, it was also concluded that to advance the field most quicklyand

competitivelythe need is acute for applying new and existing methods of molecularly based modeling.Additional findings are outlined in the panels executive summary.

International Technology Research Institute (ITRI)R. D. Shelton, Principal Investigator, ITRI Director

World Technology (WTEC) Division(Staff working on this study)

Geoffrey M. Holdridge, WTEC Division Director and Series Editor

Bobby A. Williams, Financial OfficerRoan E. Horning, Head of Information Technologies

Aminah Grefer, Global Support Inc., Europe Advance ContractorGerald Whitman, ENSTEC, Inc., Japan Advance Contractor

Hiroshi Morishita, WTEC Japan Representative

IN MEMORY OF PETER A. KOLLMAN1944-2001

The WTEC panelists, staff, and sponsors wish to express their profound sadness at the passing of panel ViceChair Peter Kollman before the publication of this report. We wish to recognize Peters lasting contributionsnot only to this report, but to the field of molecular modeling in general. His leadership of thepharmaceuticals applications side of this study, and of the study in general, was exemplary.

It was an honor to have Peter as a member of this panel and the WTEC extended family. We will miss himterribly.

Geoff Holdridge

WTEC Director

2002 to the printed work by Kluwer Academic Publishers except as noted. Copyright to electronic versions byLoyolaCollege in Maryland and Kluwer Academic Publishers except as notedherein. This work relates to NSF CooperativeAgreement ENG-9707092. The U.S. Government retains a nonexclusive and nontransferable license to exercise allexclusive rights provided by copyright. This report is printed for the government under the above license. All WTECreports are distributed on the Internet at http://www.wtec.org. A list of available JTEC/WTEC reports distributed on

paper by the National Technical Information Service (NTIS) of the U.S. Department of Commerce, and information onordering them, is included on the inside back cover of this report. This report will be published by Kluwer AcademicPublishers in hardback form later in 2002.


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i

TABLE OF CONTENTS

Table of Contents................................................................................................................................................iList of Figures....................................................................................................................................................vList of Tables ....................................................................................................................................................vi

Executive Summary .......................................................................................................................................vii

1. IntroductionPhillip R. Westmoreland

Goals and General Approach...............................................................................................................1Reasons for the International Study.....................................................................................................2Scientific and Technological Scope.....................................................................................................3Scope Beyond the Science of Molecular Modeling.............................................................................4Organization of the Analysis ...............................................................................................................5

2. Science: Electronic Structure, Thermochemistry and KineticsKeiji Morokuma

Introduction .........................................................................................................................................7Semi-empirical MO Methods .............................................................................................................. 8Ab Initio Methods ................................................................................................................................8Electronic Density-Functional Theory (DFT).................................................................................... 11Hybrid Methods.................................................................................................................................12Thermochemical Calculations ...........................................................................................................13Kinetics Calculations.........................................................................................................................14Summary............................................................................................................................................ 15References .........................................................................................................................................15

3. Science: Molecular Simulations and Mesoscale Methods

Peter T. Cummings

Introduction .......................................................................................................................................17Molecular Simulation Methods .........................................................................................................19Issues .................................................................................................................................................25Acknowledgement ............................................................................................................................. 29References .........................................................................................................................................29

4. Science: Information TechnologiesPeter A. Kollman

Introduction .......................................................................................................................................35Computer Graphics............................................................................................................................ 35Distance Geometry ............................................................................................................................ 35QSAR/QSPR .....................................................................................................................................35Using Simulations in Structure Determination and Refinement........................................................36Docking into Biological Targets........................................................................................................ 36Bioinformatics and Chemical Informatics .........................................................................................37Computer Science..............................................................................................................................37References .........................................................................................................................................37


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Table of Contentsii

5. Applications: The Chemical IndustryAnne M. Chaka

Introduction ....................................................................................................................................... 39Chemical Industry Trends and Drivers: Impact of and on Modeling ............................................... 40What is the Role of Molecular and Materials Modeling in the Chemical Industry? ......................... 41

Needs of the Chemical Manufacturing Industry................................................................................ 42Present State of the Art: Methods..................................................................................................... 43Applications ...................................................................................................................................... 45How Modeling Technology is Transferred to Industry ..................................................................... 46Challenges and Needs........................................................................................................................ 46Summary ...........................................................................................................................................49References ......................................................................................................................................... 49

6. Applications: Pharmaceuticals and Life SciencesPeter A. Kollman

Introduction and Historical Overview ............................................................................................... 51The Approaches to Molecular Modeling in the Pharmaceutical and Biotechnology Industries........ 52

Structure and Duties of the Modeling Groups in Pharmaceutical and Biotech Companies .............. 53Hardware and Software in Modeling Groups in Pharmaceutical Companies.................................... 53Relationship of Modeling to Other Technologies.............................................................................. 54Overview of Role, Successes and Failures of Modeling Groups....................................................... 55

Needs for the Future.......................................................................................................................... 55International Competitiveness in the Pharmaceutical Industry ......................................................... 56References ......................................................................................................................................... 57

7. Applications: Physical and Electronic MaterialsPriya Vashishta

Introduction ....................................................................................................................................... 59Methods............................................................................................................................................. 60Applications ...................................................................................................................................... 63

Future Directions............................................................................................................................... 67Comparison of Science (Materials Simulations of Metals, Semiconductors, Ceramics, andCarbon Systems) and Industrial Application of Science in the United States, Europe and Japan..... 70Conclusions ....................................................................................................................................... 74References ......................................................................................................................................... 74

8. Applications: CatalysisMatthew Neurock

Introduction ....................................................................................................................................... 77Methods............................................................................................................................................. 79Present Applications.......................................................................................................................... 85Panel Findings ................................................................................................................................... 94

Needs and Limitations....................................................................................................................... 99

Future Directions............................................................................................................................. 103References ....................................................................................................................................... 104

9. Infrastructure Issues for Applying Molecularly Based ModelingEllen B. Stechel

Introduction ..................................................................................................................................... 107Problem Solving .............................................................................................................................. 108The Meaning of Successful Application.......................................................................................... 109

Necessary Attributes for Success .................................................................................................... 110Closing Remarks ............................................................................................................................. 113


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Table of Contents iii

10. International ComparisonPhillip R. Westmoreland and Peter A. Kollman

Introduction .....................................................................................................................................115International Activities in Molecularly Based Modeling.................................................................115

Government Activities in Molecularly Based Modeling .................................................................147Non-Government, Multi-lateral Activities.......................................................................................158Summary of International Comparison............................................................................................159

11. Summary and VisionPhillip R. Westmoreland

Overall Conclusions......................................................................................................................... 163Major Findings: Applications ..........................................................................................................163Major Findings: Process of Effective Application..........................................................................166Vision ..............................................................................................................................................167References .......................................................................................................................................171

APPENDICES

A. Biographical Sketches of Panel Members and other Traveling-Team Members.....................173

B.1. Site ReportsEurope

Air Liquide ......................................................................................................................................180BASF AG ........................................................................................................................................183Bayer AG.........................................................................................................................................188Belgium: University of Antwerp, Janssen Pharmaceutica, Solvay..................................................191BG Technology Ltd. (now Advantica Technologies Ltd.) ..............................................................194Center for Atomic-Scale Materials Physics (CAMP Denmark) ...................................................... 196Centre Europen de Calcul Atomique et Molculaire (CECAM)....................................................199Centre National de la Recherche Scientifique (CNRS) ...................................................................201Daresbury Laboratory......................................................................................................................204

Degussa-Hls AG (now Degussa) ...................................................................................................213DSM Research.................................................................................................................................215EniChem S.p.A. ............................................................................................................................... 219EniTecnologie SpA..........................................................................................................................222GdR 12090 Project ..........................................................................................................................224Glaxo Wellcome plc (now GlaxoSmithKline).................................................................................226Hoffmann-La Roche Ltd..................................................................................................................228

Novartis Pharmaceuticals AG..........................................................................................................228IBM Zrich Research Laboratory....................................................................................................231Institut Franais du Ptrole (IFP).....................................................................................................235The Netherlands, including TU Eindhoven, Royal Dutch Shell, Philips .........................................241Rhne-Poulenc Industrialisation SA (now Rhodia).........................................................................247Royal Society of Chemistry Molecular Modeling Group ................................................................250

SmithKline Beecham Pharmaceuticals (now GlaxoSmithKline).....................................................256TotalFina (now TotalFinaElf)..........................................................................................................258Unilever ........................................................................................................................................... 260

B.2. Site ReportsJapan

Asahi Chemical Industry Co., Ltd. .................................................................................................. 261The Doi Project Nagoya University ............................................................................................264Eisai Co. Ltd. Tsukuba Research Laboratories................................................................................268Fujitsu Ltd. and Fujitsu Laboratories...............................................................................................270Institute for Molecular Science (IMS) .............................................................................................275Institute for Solid State Physics (ISSP) ...........................................................................................280


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Table of Contentsiv

Japanese government organizations funding molecular and material modeling: ............................ 286Joint Research Center for Atom Technology (JRCAT)................................................................... 288Angstrom Technology Partnership (ATP) ....................................................................................... 288

National Institute for Advanced Interdisciplinary Research (NAIR) .............................................. 288Tsukuba Advanced Computing Center (TACC) ............................................................................. 288

Mitsubishi Chemicals, Yokohama Research Center........................................................................ 292NEC Tsukuba Laboratories ............................................................................................................. 296Nagoya University........................................................................................................................... 301RIKENThe Institute of Physical and Chemical Research ........................................................... 303Taisho Pharmaceutical Co., Ltd. ..................................................................................................... 307Takeda Chemical Industries Ltd...................................................................................................... 309Toshiba Corporation, Research & Development Center ................................................................. 311Toyota Central Research & Development Laboratories, Inc........................................................... 314Short reports from other Japanese companies:

Fuji Photo Film Co., Ltd.Japan Polyolefins, Ltd. (Showa Denko K.K.)JSR CorporationMtsui Chemicals

Nippon Zeon Co., Ltd.Sumitomo Chemical Co., Ltd.UBE Industries, LTD. .................................................................................................................. 315

B.3. Site ReportsUnited States

3M, Inc. ........................................................................................................................................... 318Air Products and Chemicals, Inc. .................................................................................................... 322BP p.l.c. (Amoco)............................................................................................................................ 327CambridgeSoft.com......................................................................................................................... 337Chevron Corporation....................................................................................................................... 339The Dow Chemical Company ......................................................................................................... 341Dow-Corning Corporation............................................................................................................... 345DuPont Pharmaceuticals (now part of Bristol-Myers Squibb) ........................................................ 348E. I. du Pont de Nemours and Company ......................................................................................... 352

Eastman Chemical Company........................................................................................................... 356Ford Motor Company...................................................................................................................... 358General Electric Corporation........................................................................................................... 363HRL Laboratories, LLC .................................................................................................................. 365Louisiana State University .............................................................................................................. 367Lucent Technology / Bell Laboratories ........................................................................................... 374Marathon Oil Company................................................................................................................... 378Merck & Co., Inc............................................................................................................................. 379Molecular Simulations, Inc. (MSI; now Accelrys).......................................................................... 382Motorola, Inc. .................................................................................................................................. 387

National Institute of Standards and Technology (NIST) ................................................................. 390Owens Corning, Inc......................................................................................................................... 393Pharmacopeia, Inc. .......................................................................................................................... 394

Phillips Petroleum Company/Chevron Phillips Chemical Company .............................................. 397Rohm and Haas Company............................................................................................................... 400Sandia National Laboratories .......................................................................................................... 403Solutia Inc. ...................................................................................................................................... 408University of Minnesota Supercomputing Institute......................................................................... 410

C. Observations on Funding Estimates for Molecular Modeling................................................... 415

D. Glossary.......................................................................................................................................... 419

E. Index of Companies ....................................................................................................................... 426


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v

LIST OF FIGURES

2.1. Stationary points on a potential-energy surface. .....................................................................................72.2. Hierarchy of methods and basis functions to approach a full, exact solution to the Schrdinger

equation...................................................................................................................................................92.3. Illustration of the concept of a conical intersection...............................................................................10

3.1. Molecular modeling methods................................................................................................................183.2. Schematic of NEMD simulations of planar Couette flow .....................................................................223.3. Schematic of GEMC .............................................................................................................................23

7.1. Results of a DD simulation of plastic flow localization in irradiated copper ........................................647.2. Si(001)-SiO2interface structure from first-principles molecular dynamics. .........................................657.3. An (8,0) C nanotube before collapse, and after collapse.......................................................................677.5. Moores law for molecular dynamics. The number of atoms in classical MD simulations

(circles) has doubled every 18 months in the past 36 years, while that in quantum mechanicalMD simulations (squares) has doubled every 13 months in the past 15 years. Petaflop

computers anticipated to be built in the next ten years should maintain these rates. ............................687.6. A scientist immersed in an atomistic model of a fractured ceramic nanocomposite.............................69

8.1. Illustration of single-site metallocene complex.....................................................................................798.2. Structure of different forms of crystalline catalytic materials ...............................................................808.3. Comparison of calculated and experimental chemisorption energies and vibrational frequencies

for surface adsorbates............................................................................................................................818.4. Three approaches and examples for modeling chemisorption and reactivity on surfaces.....................818.5. The elementary steps in the classic Cossee-Arlman mechanism for olefin polymerization over

single-site catalysts................................................................................................................................858.6. Activation barrier predictions for CHx insertion for olefin polymerization..........................................868.7. Metal design for metallocene olefin polymerization catalysis. .............................................................878.8. Ligand structures used in the modeling approach of Union Carbide.....................................................888.9. Current simulation software is used to predict structure as well as sorption properties in zeolites.......898.10. Overall catalytic cycle for methanol to methyl-tertiary-butyl-ether catalyzed by acid catalysis in

a zeolite .................................................................................................................................................918.11. Activation of N2on the Ru(0001) terraces and step edges ....................................................................918.12. The effect of gas-phase oxygen partial pressure on the stability of the surface structure .....................928.13. Snapshot of the DFT-based kinetic MC simulation for the synthesis of vinyl acetate from

ethylene and acetic acid over PdAu alloy .............................................................................................93


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vi

LIST OF TABLES

ES.1 Comparative Status of Molecular and Materials Modeling in Europe, Japan, and theUnited States ..........................................................................................................................................ix

5.1 Examples of Companies Involved in the Chemical Industries..............................................................405.2 Comparison of Necessary Problem-Solving Skills in Industry and Academia. .................................... 425.3 Properties Accessible to Molecular Modeling and Simulation Techniques (properties that may

be required in process design are highlighted)......................................................................................44

8.1 Critical Areas for Molecularly Based Modeling of Catalysis ...............................................................788.2 Summary of the Basic Molecular Simulation Methods Used in Modeling Homogeneous and

Heterogeneous Catalysis.......................................................................................................................848.3a European-based Activities In Molecularly Based Catalysis Modeling, Industrial and

Government Sites..................................................................................................................................948.3b Japanese-based Activities in Molecularly Based Catalysis Modeling, Industrial and

Government Sites..................................................................................................................................95

8.3c U.S.-based Activities in Molecularly Based Catalysis Modeling, Industrial and GovernmentSites.......................................................................................................................................................958.4 General Industrial Computational Needs............................................................................................100

10.1 Recent and Ongoing Modeling Activities in Europe ..........................................................................11610.2 Recent and Ongoing Modeling Activities in Japan............................................................................. 13310.3 Modeling Activities in Africa, Australia, and Asia Outside Japan .....................................................13810.4 Recent and Ongoing Modeling Activities in North and South America Outside the

United States .......................................................................................................................................14010.5 Recent and Ongoing Commercial Modeling Activities in the United States ......................................14210.6 Estimates of NSF Funding Involving Molecularly Based Modeling, FY98 ($ million) ..................... 15710.7 Comparative status of Molecular and Materials Modeling in Europe, Japan, and the

United States .......................................................................................................................................160


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vii

EXECUTIVE SUMMARY

Phillip R. Westmoreland1

INTRODUCTION

From its purely theoretical origins, molecularly based modeling has moved to extensive practicalapplications. Molecularly based modeling here refers to the following:

! Computational quantum chemistry

! Molecular simulations by molecular-dynamics and Monte Carlo methods

! Mesoscale modeling of material domains! Molecular-structure / macroscale property correlations like QSARs and QSPRs

! Related information technologies like informatics and special-purpose molecular-modeling computers

This report finds strong evidence that in the next ten years, these approaches will profoundly affecthow chemistry, biology, and materials physics are understood, communicated, and transformed totechnology, both in intellectual and commercial applications. In the past five to ten years, suchcomputational approaches have taken hold. Growth in applications has been slow and sometimes fitful, oftenfor nonscientific reasons. However, these approaches have brought powerful new means to understandchemical reactions and materials physics, and they are playing a crucial role in orienting applied life sciencestoward biology's chemical underpinnings.

Indeed, one sign of impact is that specialists have now been joined by general users of these methods.

Whether in a journal, a classroom, or a boardroom, presentations of three-dimensional molecules havebecome widespread and can be extremely effective. They are made possible by simple methods ofgenerating and representing them. Similarly, new employees are used to having and using basic molecular-modeling software. As a linked consequence, experts can be more effectively employed in tasks that demandtheir particular skills, especially when general users understand both their own capabilities and limitations.

This study focuses on commercial applications of molecularly based modeling, examining successes,failures, and the causes of each. Advances depend not only on the right science, but also in the way thescience is applied. For industry or anyone else to choose these methods, useful impact has to be proved. Ifnew tools seem valuable, people rush to adopt them. If new tools fail, people rush away from them, oftenfailing to ask why. It is interesting that most failures of molecular modeling tools prove to be basedultimately on non-technical causes. The process of application then is a vital part of successful application.

Even in an era of instant global communications, there are clear regional strengths. These strengths arerooted in geographical centers of intellectual achievement. The origin of these centers is individuals andsmall teams of gifted researchers. These strengths have evolved because government, industry, or individualcommitment to specific ideas and approaches has fostered intellectual centers, and they expand regionally

because technology is transferred best by the movement of knowledgeable people. Each region has benefitedby people spending careers or extended periods across international borders, but Japanese organizations have

1Representing the consensus of all the panel members.


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Executive Summaryviii

been among the most conscientious and successful about doing so. European countries have been especiallyeffective at creating multi-national research and educational programs.

These observations and conclusions are based on the expertise of an eight-person panel and on informationthey collected in a 1999-2001 study. In February 1999, a team of Federal agencies proposed a study to

evaluate how molecularly based modeling is being applied throughout the world, building on previous U.S.-focused studies and giving special attention to where U.S. activities stand. Requesting agencies included theNational Science Foundation (Directorate for Engineering, Directorate for Mathematical and PhysicalSciences/Division of Materials Research), Department of Energy (Basic Energy Sciences/Office of Science,Office of Industrial Technology), Department of Defense (DARPA and AFOSR), National Institutes ofHealth, and National Institute of Standards and Technology. As a key part of the study, the panel made 53site visits in Europe, Japan, and the United States, mostly to commercial organizations, choosing to rely on

personal knowledge and the open literature for including academic and government activities. Detailedreports were prepared on activities at a total of 91 organizations (see Appendix B). The study was managed

by the World Technology (WTEC) Division of the International Technology Research Institute, based atLoyola College in Baltimore.

The following chapters and the case-study reports (Appendix B) give details of the science and technologies,as well as an evaluation of international activities in this field. Related funding of these activities by the

United States government is estimated at $200 million, as summarized in Appendix C. Also, a glossary isprovided in Appendix D.

Findings may be grouped into two categories: (1) applications and (2) the process of effective application.Table ES.1 presents a distillation of this panel's opinions, which are cheerfully recognized to be imperfect.While the members of this panel recognize that there are significant activities in other parts of the world, thetable restricts itself to reflections upon activities in the U.S., Europe, and Japan. Certain broad findings standout, and they are summarized here.

FINDINGS: APPLICATIONS

(1) Molecular modeling methods have gained acceptance as practical tools in a variety of industries.

From modest beginnings, typically a single specialist in a technical services or research role, manycompanies have developed effective molecular modeling activities. Noting a few example companies,activities include development of products and processes involving the following:

! Bio-active materials like pharmaceuticals (Merck, Novartis, Takeda Chemicals) and crop-protectionchemicals (DuPont, Sumitomo Chemical)

! Polymers, glass, and structural materials (Asahi Chemicals, Owens Corning, Rhne Poulenc / Rhodia,W.R. Grace)

! Electronic and photonic materials (Motorola, Toshiba, Lucent)

! Homogeneous and heterogeneous catalysts (Ford, Haldor Topse, Ube Industries)

! Sorbents for gas separations (BG Technologies / Advantica Technologies Ltd., Air Liquide, Air Productsand Chemicals)

! Personal-care, food, and consumer products (Colgate Palmolive, Unilever, Kellogg, 3M)! High-volume chemicals and materials (Dow, BASF, Rohm & Haas)

! Dyes and pigments (Bayer, Mitsubishi)

! Films and imaging (Fuji Photo Film, Xerox)

! Fuels and automotive chemicals (Chevron, TotalFina / TotalFinaElf, Lubrizol)

! Commercial software and hardware for calculations (MSI / Accelrys / Pharmacopeia, Gaussian,COSMOlogic, Fujitsu)


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Phillip R. Westmoreland ix

Table ES.1Comparative Status of Molecular and Materials Modeling in Europe, Japan, and the United States

Technology Europe Status Japan Status U.S. Status

Methods

- Quantum Mech & Hybrid Methods, Molecular **** *** *****- Quantum Mech. & Hybrid Methods, Condensed Matter ***** **** ****- Molecular Simulations

Biological systems **** *** ***** Fluids ***** *** ***** Solids, devices *** **** *****

(devices) Polymers ***** ***** **** Kinetics Monte Carlo *** ** *** Mesoscale ***** *** ***

- Solvation ***** **** *****

- Informatics & Optimization **** *** *****- Hardware, Operating Systems, Math Methods **** ***** *****Applications

- Chemical Process Development **** **** ****- Chemical Process Design and Manufacturing ** ** **- Chemical Product Design Fuels, specialty chemicals, coatings, surfactants

**** **** ****

- Reaction Thermochemistry & KineticsNoncatalytic **** *** *****

Homogeneous catalysis *** ***** ***** Heterogeneous catalysis (mostly academic) **** (QC) *** **** (sim)

- Physical and Electronic Materials

Metals, semiconductors *** ***** ***** Ceramics, glasses **** ***** **** Polymers ***** ****" *****

- Biomolecules and Biologically Active Materials Pharmaceuticals **** *** ***** Agricultural products ***" ? ***"

- Application to Analytical Measurements Chemical analysis ***" ? ****" Material analysis ***" **" **" Biological analysis **** *** *****

Transfer of Science to Technology **** ***** ***

Key: # of stars indicates level and quality of activity in each region;

0 = no activity, no quality; 5 = very large amount of activity or high level of quality;up arrows () indicate increasing level of activity(2) The methods are being adopted because they are proving their value in big ways.

Big successes naturally attract the most attention, both from within companies and from competitors. Thestudy identified three major success areas:

! Drug discovery has been the most prominent success. Docking calculations and modeling of proteincrystallography by molecular simulations have been important in developing both promising leads and


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Executive Summaryx

successful drugs. Commercial examples are the HIV protease inhibitor Agenerase, discovered byVertex Pharmaceuticals (Cambridge MA) using structure-based drug design (FDA approval 1999), andAricept, an ACE inhibitor used as an Alzheimer's disease medication from Eisai Co., Ltd. (a Japan-

based global company).

! The profound impact on developing homogeneous catalysts has been less well known but has been

nearly as significant. In 1975, polyolefins were projected to fall to 20% of the polymers market by 1995;instead, in 1995 they were at 85%, largely because of improvements in polymer properties due tohomogeneous catalysts. Promising leads and commercial catalysts have been developed or screened bycomputational quantum chemistry at a large number of companies, including Asahi Chemical, BASF,Bayer, BP (Amoco), Degussa, Dow Chemical, DSM, DuPont, Enichem, IFP, Mitsubishi, PhillipsPetroleum, Statoil, Totalfina, and Union Carbide.

! Computational thermochemistryhas proved to be a key deliverable in many companies, mostly for ideal-gas thermochemistry. These numbers are widely used in evaluation of reaction equilibria, processdesign, and process safety analysis. In a 1996 conference presentation, a Dow Chemical speaker toldhow the heat of formation for a molecule of interest would cost about $70,000 to be measuredcalorimetrically but $20,000 for a value of comparable accuracy by a G2 quantum chemistry calculation.At the start of 2000, their comparison had changed to $100,000 or more for the experimental approachversus $2,000 for G3 calculation with comparable accuracy.

(3) Many companies now see such modeling as necessity, while others still consider it a luxury.

Management and employees may view these activities as necessary, on trial, or merely of possible long-rangevaluea luxury that can be eliminated in times of cutbacks. Management and old-line employees often stillreject or suspect the value of molecularly based modeling until their own company or competitors scoresubstantial successes. When they do buy in, there can be unrealistically high expectations, too.

Like any other tool, though, when people find a tool or skill or service that can make a difference, they seekit out. New graduates also create internal demand for specialists and for the tools themselves. Increasingly,they are familiar with the usefulness of these methods. They expect that these capabilities will be availableas a matter of routine, just as they expect to have gas chromatographs or other analytical equipment.

Variations occur between and within industrial sectors:

! In pharmaceuticals and agricultural chemicals, all major companies now appear to include molecularmodeling as a given necessity for identifying leads and analyzing binding sites. Modeling specialists areroutinely on the product development teams.

! There are small, successful drug discovery companies like Pharmacopeia (Princeton NJ), MillenniumPharmaceuticals (Cambridge MA), and Vertex Pharmaceuticals (Cambridge MA) whose businesses arecentrally built around a close interplay between modeling and experiment.

! However, most of pharma is not conscious of modeling value for their separations and other process oranalytical needs. This appears to be due to pre-occupation with modeling's capabilities for productdevelopment, overlooking its potential in other aspects of the companies' needs. There has been someactivity in these areas, for example at DuPont Pharmaceuticals.

! In the fuels and chemical process industries, including polymers, most companies have one to a few

specialists. Some of these modeling groups have been created only recently and are thus on somewhatprobationary status. Many others have established their value and are stable or growing.

! Large groups of specialists clearly imply that management sees modeling as a necessity. For example,the largest groups appear to be at Unilever and Sumitomo Chemical, each of which has 25 to 30specialists.

! Likewise, the elimination or absence of modeling specialists in a company clearly implies that itsmanagement sees modeling as an unnecessary activity for the company's economic future. That may ormay not be an absolute valuation by the company. In the case of Eastman Chemical, most research anddevelopment was eliminated in November 1999, including the two molecular-modeling specialists. By


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Phillip R. Westmoreland xi

contrast, when Amoco dramatically reduced its molecular modeling staff in the early 1990s, the decisionappears to have been linked to dissatisfaction with their progress in modeling heterogeneous catalysis.

! In the hard condensed-matter industries (like electronic materialsandstructural materials), companieslike Motorola, NEC, Toshiba, Fujitsu, and Corning have significant activity. This is a busy area ofresearch. In most cases, it is a commitment to the future, while such work has established its value in a

few companies. Process thermochemistry and kinetics are some of the properties being calculated, butmost materials modeling of the materials themselves still uses continuum modeling with atomisticmodels. Band-gap theory is a notable exception to the latter.

(4) The principal role of molecular and materials modeling in the chemical industry is to speed product

development and guide experiment.

For the discovery stage, this is most commonly and effectively done in the following ways:

! Developing mechanistic hypotheses and a rational, strategic approach to problem solving

! Providing understanding and design rules

! Stimulating new ways of thinking

! Ranking properties for molecules, materials, and formulations (best if in a QSPR model which can be

queried)! Aiming for predictive results, rather than just descriptive results

! Eliminating of dead endsinvaluable!

! Maximizing utility of data

! Broadening patents

For the manufacturing and process development stages, molecularly based modeling plays a key role inhelping deliver accurate themochemical and kinetic data and in improving the yield, selectivity, or wastereduction for a given process.

(5) Science and methodology have made great strides, and successful applications encourage more.

! Quantum-chemistry accuracy is coming to large molecules by virtue of faster computers with morestorage, new methodologies like hybrid methods and gradient-corrected DFT functionals, andsophisticated correlation-corrected quantum chemistry like G3, CBS, and ONIOM.

! Development and use of ab initiomolecular dynamics enables better modeling of catalysis and materials(e.g., Car-Parrinello molecular dynamics, the T.U. Vienna code VASP, Nrskov's DACAPO code,Japanese CAMM/CAMP consortium codes).

! There are rapid improvements in modeling solvation and nonideal mixing properties throughcombinations of electronic-structure calculations with continuum solvation models (e.g., Tomasi's PCMand subsequent models, the COSMO-RS method developed at Bayer and spun off to a start-up company,COSMOlogic).

! Theories and modeling for mesoscale structures in fluids and solids are advancing (e.g., DissipativeParticle Dynamics from Unilever, the MesoDyn project originally from BASF and Groningen).

! Obtaining accurate kinetics and rate constants remains a challenge.

! More accurate force fields are needed than are presently available in today's commercial codes.

! An intersection is evolving with QSAR/QSPR correlations, informatics, combinatorial chemistry, andhigh-throughput screeningnot just in pharmaceuticals.

! Few computer codes are available for phase equilibrium. Almost all from academia, and almost none arevendor-supported codes.


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Executive Summaryxii

(6) Molecular modeling is being used to complement and interpret analytical measurements.

A remarkable number of properties are now calculable. For a wide range of experimental analyticaltechniques, predictions have become powerful aids to interpretation. Examples include:

! Ideal-gas thermochemistry (!Hof,298, So298 and Cp

o)Quantum chemistry. In some cases, predictions

now have less uncertainty than the calorimetric measurements, effectively replacing them.! Spectra for gas phase and solution phase, infrared and RamanQuantum chemistry. DFT calculations

are the current methods of choice to help predict and assign frequencies.

! NMR shifts for molecule structure analysisQuantum chemistry.

! Crystal structures from crystallography dataMonte Carlo simulations with Rietveld refinement.

! Initial successes have been achieved in predicting retention times for gas chromatography by molecularsimulations, predicting circular dichroism for optimal isomers using quantum chemistry, and predictingion fragmentation patterns for mass spectrometry using reaction theory.

(7) Striking similarities exist among needs of different industrial sectors.

Different industrial sectors share many common technical interests in methods and in applications. Modeling

solvation is a common and unresolved problem for the chemical and pharmaceutical areas. Modeling realmixtures is an issue for all sectors, whether in fluids, alloys, or at interfaces like grain boundaries. Likewise,the focus of drug and agricultural-chemical discovery is matching chemicals to the biochemical basis of a

biological behavior, which will be the basis for predictive chemical toxicology. With predictive toxicology,chemical products could be designed that have a desired function but with less hazard than alternatives.

(8) The top needs required by the chemical industry can be summarized as bigger, better, faster,

more extensive validation, and multiscale techniques.

! Bigger means being able to do larger systems with greater complexity. Two examples are multi-reference wavefunction quantum mechanical methods involving complex transition-metal species andcalculations of mesoscale behavior in multi-phase and composite systems. Better reflects the need forgreater accuracye.g., activation energies, thermochemistry in condensed phases, and weak van derWaals interactions. Faster enables the simulation of rare-event processes such as predicting the

thermal and oxidative stability of a plastic under a wide variety of weathering and the effect of a dirtparticle on the fracture mechanics of a polymeric / inorganic filler composite.

! More extensive validation is required because of the complexity and range of modeling and simulationtechniques required to solve real-world industrial problems. A modeling expert trained in one field needsto have extensive validation studies available to evaluate what level of theory is appropriate to model agiven system, as well as to understand what the underlying assumptions and limitations are when using anew method in another field.

! Multiscale modeling is at the heart of technological or engineering modeling. Modeling methods thatare essential at one scale may be useless at another. For example, all chemical reactions happen at amolecular level and require information from quantum chemistry, while neither force-field-basedmolecular simulations nor finite-element models are suitable. On the other hand, a dynamic physical

process like conductive heat transfer can use molecular simulations effectively, convective heat transfer

requires continuum-scale physics, and designing a bridge to carry a certain load in summer or winterrequires a combination of engineering statics and materials properties which may only be measured. Assoon as critical material properties are involved, property measurement is vital, yet understanding andextrapolation of those properties are tying ever more effectively back to the molecular level.

(9) Looking ahead, molecularly based modeling is the key to modeling a vital technology for the future:applying nanostructure and nano-scale devices.

Nanotechology is the technology of molecular-scale materials and processes. A typical atom has a van derWaals diameter of a few tenths of a nanometer; molecules and macromolecules are nanometer-sized andlarger. All reactions and many macroscopic properties have their origins at this scale, linked to the


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macroscale by statistical mechanics and solid-state physics. At the same time, the properties may bedominated by electronic structure, non-bonding interactions, or meso-scale, supermolecular behaviors. Eachof these domains is the province of some type of molecular modeling, and it is increasingly possible tocouple these domains to each other and the macroscale.

FINDINGS: INFRASTRUCTURE AND THE PROCESS OF APPLICATION

(1) The greatest economic value of molecular modeling is often not to create new substances and

processes by itself, but rather to steer experimental development.

Give-and-take between modelers and experimentalists early in the development process has proven potent.In company after company, specialists argued that it is unrealistic to expect that molecular modeling wouldgenerate new catalysts or polymers, as unrealistic as expecting the same thing from NMR. At the same time,use in development teams has yielded big payoffs. At Dow, an internal meeting was held on developinghomogeneous polymerization catalysts. To address questions and proposals made during the meeting, animpromptu session was held around an SGI workstation that had been quickly brought in, running simplercalculations with Spartan software and 3-D glasses. Successful patent filings for new catalysts came out ofthe meeting.

(2) Eliminating dead ends can be especially valuable.

In pharmaceutical discovery, modeling guides development by focusing attention in productive directions. Arecent use is to help construct chemical libraries for combinatorial high-throughput screening, more

promising subsets of the almost unlimited range of chemicals that might be examined. Likewise, a polymersexample from Rhne-Poulenc was termination of an experimental elastomers development program, basedon calculations that the approach could not succeed. Experimentalists were re-directed to more productive

projects, and modelers were given internal credit for significant savings in that fiscal year.

(3) Failure to couple modeling sufficiently with experiments can lead to frustration, delays, and

irrelevance.

In a case at Eastman Chemicals, a new modeler was assigned to get a mechanism for acid-catalyzed acyl

transfer in alcoholysis of carboxylic anhydride. This was assumed to be the rate-limiting step for an ongoingprocess, and faster production was desired. No suitable transition state could be found. By probing moreclosely, the modeler found that operators added enough base that the reactant mixture was basic. A transitionstate was then found quickly. It was fast enough that equilibrium solubility instead proved to be limiting. Itwas then addressed separately.

(4) A healthy division has developed among specialists, non-specialist users, and internal clients.

Companies as different as Asahi Chemicals, Air Products, and Merck have initially tried to build a large userbase for modeling. Eventually, they all decided that complicated or forefront problems benefit fromspecialists' expertise, but that the general user benefits most from low-level calculations and visualization.

Non-users often make excellent use of calculated results, needing enough knowledge and motivation tocontact and work with modelers but not doing the modeling themselves.

(5) Using molecular modeling for intellectual property extends beyond discovery.

There are numerous examples of patent positions and proprietary knowledge developed in whole or primarilywith molecular and materials modeling. However, it is also used as follows:

! To extend initial discoveries, often broadening basic coverage by testing structural variations. Onecompany modeled a small set of industry surfactants, then correlated the key property with a QSPR,tested and verified the result with a larger set, and ultimately discovered a new class of molecules for the

job. Developing homogeneous metallocene catalysts is another good example.


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Executive Summaryxiv

! To defend intellectual property. Rohm and Haas successfully defended its patent for a urethanethickener by proving that the other company could not avoid making the Rohm and Haas thickener in its

process.

! To build offensive patents, where a foothold of intellectual property is established in the market of acompetitor.

(6) Computing platforms are shifting toward commodity processors, even for much high-performance

computing.

Because of the focus on high accuracy for the biggest possible models, supercomputers have played animportant role in past modeling. Since the late 1980s, workstations have been valuable for low-levelcalculations and visualization.

Workstations and Beowulf clusters, increasingly based on commodity processors, now are taking over eventhe largest tasks. Powerful single- and dual-processor PCs now achieve computational speeds faster thanmost previous workstations, and disk and memory costs are much lower. They are taking over many routinecalculations. For large problems that benefit from parallel computation, the Beowulf approach is rapidly

being adopted by companies worldwide. Multiple Intel Pentium or Compaq Alpha processors are coupled bya fast Ethernet switch and often powered by the Linux operating system, giving impressive parallel-computing performance for much less expense than a dedicated parallel supercomputer. Japanesesupercomputer manufacturers noted that there will still be a significant market for ultra-powerfularchitectures, including supercomputer clusters. However, quantum chemistry codes do not necessarily

parallelize well, and not all parallel problems are suited to this architecture.

(7) Software development must avoid becoming narrowly focused on the drug discovery problem.

These methods give extra intellectual power to the study and exploitation of life sciences. The visionarypossibilities of precision and customized drugs, combined with the high profit margins presently available forsuccesses in pharmaceuticals, have been powerful stimuli for development of new modeling and informationtechnologies. At the same time, this has driven a consolidation of software companies and a relative neglectof software for other sectors like chemicals and materials. These sectors show growing demand for use ofmolecularly based modeling and for better software with more functionality. One consequence is that new

software companies are beginning to appear again to fill this need.

(8) Technology-oriented modelers and modeling-savvy technologists are needed.

Many companies have begun using molecular modeling with a theoretical chemist and a technologist(chemical engineer or industrial chemist) working together. The mix of expertise is still valuable.

Over time, it has gotten easier but is still challenging to find theoretical chemists or physicists for theindustrial environment. Likewise, new non-theoretical chemists are now better educated about thesemethods. Knowledgeable chemical engineers are especially needed. They are often well prepared inmolecular thermodynamics, but relatively few have experience with molecular simulations andcomputational quantum chemistry.

CHALLENGES FOR THE SCIENCE AND APPLICATION OF MOLECULAR MODELING

For future successes, both science and its implementation are important. It is not only necessary to identifyor develop the right science for a given application, but new tools and suitable infrastructure must be builtwithin companies and scientific communities. Specific topics can be cited:

1. Algorithms. Improved computational algorithms are needed to cope with larger molecules or sets ofatoms. As we can tackle bigger problems, we invariably need to tackle even bigger ones. One measureof the problem size is the number of atoms, N. Scaling of problem size byN3to N7 quickly limits thefeasible targets of ab initio calculations, and a molecular-dynamics simulation of 1000 atoms for a


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Phillip R. Westmoreland xv

second might as well be infinity when computed in femtosecond time steps. More degrees of freedommean more complicated optimizations, as in protein configuration analysis, self-consistent-fieldsolutions, or transition-state searches.

Introducing new science may necessary in much the same way that electronic density functional theorywas adapted from its base in solid-state physics. Recent advances in solvation, one of the central

problems for technological applications, have come from redefining the reference condition from idealmixtures of non-interacting molecules to a perfectly screened molecules in perfect conductors.However, new algorithms can drastically affect solution of the basic quantum and statistical mechanics.Consider the QM/MM methods.

2. Exploiting parallel computation. Parallelization has great promise for large systems, yet conventionalapproaches to quantum chemistry have few parallelizable features to exploit compared to molecularsimulations. Methods and software are needed to parallelize problems and to develop parallel code moreeasily. For example, Lester and co-workers have shown that a Quantum Monte Carlo method can beused to evaluate the integrals used to solve the Schroedinger equation with great accuracy. On single-cpu computers, the method is quite slow, but with hyper-parallel computers, it could deliver fast,accurate calculations even for quantum chemistry calculations on large molecules.

Likewise, parallelizable problems are still limited by memory usage or message passing. Dropping the

dimension of the problem from N3to N1shrinks the problem a million-fold for a problem involving 10oligomers of 100 atoms each. Advances like the Order(N) methods could have great impact.

3. Automating the choice as well as the methods.A different issue is automation of method choice. Forexample, quantum chemistry calculations may be carried out in a hierarchy of increasing accuracy(Pople's model chemistry approach) and cost, and molecular simulations may be carried out with forcefields of increasing detail and accuracyand cost. In both cases, there is a trade-off. BASF developedand tested a semi-automatic selection algorithm based on the desired property and accuracy(CrunchServer), finding requested information in the database or delivering the request for humanassessment of choices. It was discontinued because of unresolved challenges, but it foreshadowsinformation management methods for the future.

4. Computing hardware and operating systems. Faster CPUs, more memory, and parallel configurationsoffer new power and new challenges. Different approaches to molecular modeling place different

demands on hardware and operating systems. So do the varying needs of users. Base-level platformsmust be integrated with the computing and visualization necessary to solve smaller but importantproblems effectively. User interfaces must be transparent windows on the task, methods, and results,despite use on diverse, constantly evolving platforms. Similarly, it is increasingly desirable to carry outcode generation, translation, and documentation using cross-platform visual programming tools andtechniques like literate programming.

5. Data management. As computational power increases, the desire to probe the real complexities alsodemands more sophisticated management and exploitation of data, both computed and measured. Whilesome problems may reduce to a single key result (e.g., a heat of formation), most also have a largeamount of accompanying detail. Polymer modeling is a good example. Consider the normal-modeanalysis of frequencies for 100,000 MW polyethylene (C7143H14288). For any configuration, each of the21,431 atoms has 3 time-dependent position coordinates, simplified to 3 coordinates and 3 componentsof an oscillation motion vector. It gets worse. These motions may be nonharmonic, the polymer may be

highly branched, the polymer will exist in some molecular-weight distribution, and analysis of a singlechain will be inadequate to capture the amorphous or crystalline morphology of the material.Furthermore, the interest may be in small-molecule diffusion in a polymer, a polymer melt,homopolymers of more complicated monomers than ethylene, block or random co-polymers, polymerswith plasticizers and other additives, solvated polymers, stereochemistry, radical or ionic orcondensation or metal-catalyzed polymerization, or polymer degradation. Biopolymers add anotherlayer, as do comparisons with noisy data. The task seems impossible, yet even now polymer modeling is

powerful, and as complex a behavior as protein folding can be attacked by crucial simplifications.


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Executive Summaryxvi

6. The experiment-modeling interface. Pure correlations like QSPR and QSAR (quantitative structure-property or structure-activity relations) already allow prediction and interpretation of practical propertieslike toxicity or octanol-water partition coefficients. Prediction of measurable properties is crucial, but sois measurement of predictable properties. A challenge for molecular modeling is to keep up with the fastand effective approach of combinatorial chemistry, which in principle is quite Edisonian. Already,

computational chemistry and information technologies have become important for systematizing thearray of possibilities to be tested experimentally (e.g., see site report on Pharmacopeia, Inc.).Combinatorial chemistry can be a powerful way to avoid the limitations of imagination, but the bestcombinatorial chemistry also builds on relevant chemical principles. Computations need to aid thedesign and interpretation of such experiments, leveraging the appropriate time/accuracy balance ofdifferent theoretical approaches.

7. Problem analysis. Successes in industrial application have rested on identifying the crucial issues orquestions. The Eastman Chemicals acylation problem, noted above, is a good example of the need formodelers and non-modelers to analyze the problems together. Understanding what these methods canand cannot do is crucial to successful problem analysis.

8. Personnel infrastructure. Two personnel issues have been crucial to initial successes in industrialapplication of computational chemistry: (1) identifying people who know or are willing to learn both thecomputational chemistry and the applications, and (2) having advocates among management or clientswho recognize the appropriate uses of the methods. The largest industrial groups of specialist molecularmodelers consist of 25-30 people, and most are much smaller in size. In the chemical process industries,it is common to find only one or two computational chemists in a company, frequently paired with amore technologically oriented chemist or chemical engineer. At the outset, they educate each other. Astime proceeds, they educate others in the possibilities and limitations of the computational tools, mostfruitfully by aggressive participation in development teams at the earliest stages. In contrast, use oftechnical service providers and consultants has had limited success, but has sometimes led to eliminationof the activity when internal clients failed to take advantage of tools they didn't know about or didn'ttrust.

9. Validation. Method validation is often an unstated but crucial need. Comparisons by individualresearchers and by software vendors like Gaussian, Inc. are useful for general guidelines. Other, morecomprehensive comparisons will be valuable. For example, NIST is building a Computational

Chemistry Comparison and Benchmark Database (http://srdata.nist.gov/cccbdb/) and a Sicklist Databaseof known problems for ab initio methods (http://srdata.nist.gov/sicklist/). As a multinational effort,IUPAC has a task group for Selected Free Radicals and Critical Intermediates: ThermodynamicProperties from Theory and Experiment, led by Dr. Tibor Berces of the Hungarian Academy ofSciences' Chemical Research Center.

10. Credibility. A final key need is development of substantive credibility. Successes within anorganization are the ultimate criteria, but awareness of outside successes has proved to be a powerfulspur to using these methods. Two dangers have been the desire for overwhelming successes and thestrong impact of attractive visuals. There are cases of computational chemistry groups being eliminated

because of undue expectations from management and colleagues. These expectations had beenheightened by promises to succeed at strictly long-term tasks like de novocatalyst design, reinforced byrealistic-looking visual images. In contrast, Dow researchers note that their successes in modeling

polymer properties were made possible in part by their shorter-term successes in calculating needed

ideal-gas thermochemistry.

VISION

There are two kinds of speculation about the future: Things we reasonably expect (near-term) and thingswe only dare imagine (long-term). For example, in 1970 it was reasonable to expect computing would bemore important by the beginning of the twenty-first century. On the other hand, it would have been aluxurious vision to suppose that individuals might have their own computers. It would have been even morelike science fiction to suppose that nontechnical businesspeople and schoolchildren alike could have their


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Phillip R. Westmoreland xvii

own powerful palm-sized computers with wireless connections to a spiderweb of computers, information,and people around the world. On the other hand, it was 1929 when Dirac pronounced that all the physicallaws underlying chemistry were knownalthough their application was hindered by the exact equations

being unsolvable.

For molecularly based modeling, advancing computer power drives the near-term promise:! The value of computed results will be greatly enhanced when they can be generated near-instantly, but

they become invaluable when their meaning is clearly linked to the application's or developer's need.

! Conventional methods of ab initiocalculation will be much faster for a given problem, although theywill still be unable to handle much bigger problems so long as high-accuracy methods increase in cost asa function ofNatoms

6orNatoms7.

! Parallel supercomputing will be increasingly available, offering great advantages to molecularsimulations that parallelize well.

! Experimentalists and management will not only become used to accepting the use of molecularmodeling, but they will expect it.

! There is so much recognition of the need for validation and testing of methods that expert systemsshould become available to guide the novice or intermittent user in making the best calculations.

! Vast amounts of numerical data and qualitative information will demand more reliance on correlation,mining, and visualization of results.

Long-term, we can speculate about science, applications, and infrastructure separately, but the complicatingfactor is that present paradigms will be turned on their heads by the ubiquity of computing processors.Already, changes in computing power and architectures outpace the development of software that takesadvantage of them. Raw power will make calculations of unthinkable size thinkable, but the real surprises liein the unimagined. When computers can be printed or grown, it may become easier to design a newcomputer for a problem than to design software, eliminating the idea of operating systems. Stochasticmethods will dominate if all of parameter space can be rapidly examined. Ironically, physicochemical datawill become limiting; how can one confirm that a method's uncertainty is better than an experiment's if theexperiment is the standard? At the same time, great advances are being demanded in knowing andcontrolling properties and behaviors of individual atoms and small groups of atoms, whether for

nanostructured materials or gene manipulation or quantum computing. Understanding in turn demandsmodels that are consistent with physical reality; they are the molecularly based models that are the subject ofthis analysis.

PERSPECTIVE

The United States leads this field in many scientific areas. However, Canada has particular strengths in DFTmethods and homogeneous catalysis; Europe in heterogeneous catalysis, mesoscale, and materials modeling;and Japan in materials modeling and special-purpose computing. Major government-industry initiatives areunderway in Europe and Japan, notably in multi-scale materials modeling and in development of chemistry-capable ab-initiomolecular dynamics codes. In European and U.S. assessments of nanotechnology, it wasalso concluded that to advance the field most quicklyand competitivelythe need is acute for applyingnew and existing methods of molecularly based modeling.

Company activities and challenges are generally the same around the world. Paradoxically, quantummechanics and statistical mechanics are well-established fields, but their quantitative applications have beenheld back by the computer technology for using them. Success and failure always depend on the right peopleworking on the right problem with the right tools, and activities in molecular modeling have been vulnerable.The process and organization of these activities have proven equally important to the science and technicalimplementation.

Raw computing power can't buy success, but advances in computing power, visualization, and informationtechnology have made many new applications of molecular modeling feasible. Growth in parallel


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Executive Summaryxviii

computation ability has not kept pace with advances in parallel computer hardware, largely because methodslag for construction and optimization of user codes. Ironically, using specialized parallel hardware (SGIOrigins, for example) appears to be slowing as the economics of commodity CPUs drive rapid increases intheir use (e.g., in Beowulf clusters). User interfaces with software remain a problem, especially forgeneration of thermochemical and kinetics parameters.

Advances in practical applications during the 1990s were remarkable, though. The head of corporateresearch at one chemical company in 1999 went so far as to say that it was clear that the company's successin applying molecular modeling would be a central basis of its success ten years from now. Not allmanagement feels so strongly, but the present widespread penetration of these methods speaks for their

perceived and demonstrated importance.

In education and technical communications, both for students and for practicing scientists and technologists,knowledge of molecularly based phenomena and modeling is becoming imperative. Continuum models ofmaterials and of macroscopic behaviors will continue to be important. However, all reaction chemistryoccurs at the molecular scale, molecular interactions are the basis of continuum properties, and life sciencesare becoming ever more clearly linked to their chemical underpinnings. Molecular theories and modeling,including electronic-structure theories and modeling, are likely to become the lingua francafor advances inmuch of science and engineering.


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1

CHAPTER 1

INTRODUCTION

Phillip R. Westmoreland

GOALS AND GENERAL APPROACH

The goals of this analysis were (1) to assess state of the art in molecular modeling, from quantum chemistryto empirical molecular simulation of macroscopic systems, and (2) to assess how effectively and extensivelytheoretical advances are being transferred to applications in the chemicals, life sciences, and materialsindustries. A small committee of experts with diverse expertise was chosen to gather information from sitevisits, interviews and the literature:

! Phillip R. Westmoreland, Chemical Engineering, University of Massachusetts, Amherst (Chair)

# Measurement and quantum-chemical modeling of reaction kinetics for combustion and polymerdevelopment

! Peter A. Kollman, Pharmaceutical Chemistry, University of San Francisco (Vice Chair)

# Development and application of computational approaches to understand structure, energies andreactions of organic and biochemical molecules, for example the AMBER molecular simulationscode and force-fields

! Anne M. Chaka, Lubrizol Inc. (at NIST from May 2001)

# Industrial computational chemistry and physics, notably applied to problems of lubricants andcorrosion

! Peter T. Cummings, Chemical Engineering, University of Tennessee and Oak Ridge NationalLaboratory

# Methods for predicting physical properties in systems of industrial interest such as supercriticalaqueous solutions, alkane fluids, and polymer solutions using molecular simulations and massively

parallel processing

! Keiji Morokuma, Chemistry, Emory University

# Methods and applications of computational quantum chemistry, including the IMOMO and ONIOM

methods and their applications in combustion chemistry and homogeneous catalysis! Matthew Neurock, Chemical Engineering, University of Virginia

# Modeling of heterogeneous catalysis using electronic density functional theory and kinetic MonteCarlo methods

! Ellen M. Stechel, Chemistry and Environmental Science Department, Ford Motor C

a467500 report molecular materials modeling

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