Computer-Aided Molecular Modeling of Materials

Instructor: Yun Hee Jang (yhjang@gist.ac.kr, MSE 302, 2323)TA: Eunhwan Jung (leonz@gist.ac.kr, MSE 301, 2364)Web: http://mse.gist.ac.kr/~modeling/lecture.html

Reference:- D. Frenkel & B. Smit, Understanding molecular simulations, 2nd ed. (2002)- M. P. Allen & D. J. Tildesley, Computer simulation of liquids (1986)- A. R. Leach, Molecular modeling: principles and applications, 2nd ed. (2001)- and more

Grading:- Homework: reading + 0.5-page summary - Exam or Term report: Mid-term & Final- Hands-on computer labs (report & presentation)- Presence & Participation (questions, answers, comments,

etc.)

Why do we need a molecular modeling (i.e. computer simulation at a molecular level) in

materials science?

N (number of atoms) or L (size) of a system of interest)

Diffi

cu

lty (

cost

, ti

me,

manpow

er,

in

acc

ura

cy)

Molecular simulation in virtual space

Experiment in real space

Traditional (Past)Materials scienceN~1023, L~10 cmExperiment didn’t need simulation.

too hard

easy

Emerging (future)Materials scienceN~102, L~10 nmSimulation will lead.

easy

hard

• 1918 – Physics – Max Planck – Quantum theory of blackbody

radiation

• 1921 – Physics – Albert Einstein– Quantum theory of photoelectric

effect

• 1922 – Physics – Niels Bohr – Quantum theory of hydrogen spectra

• 1929 – Physics – Louis de Broglie – Matter waves

• 1932 – Physics – Werner Heisenberg – Uncertainty principle

• 1933 – Physics – Erwin Schrodinger & Paul Dirac – Wave equation

• 1945 – Physics – Wolfgang Pauli – Exclusion principle

• 1954 – Physics – Max Born – Interpretation of wave function

• 1998 – Chemisty – Walter Kohn & John Pople

• 2013 – Chemisty – Martin Karplus, Michael Levitt, Arieh Warshel

Nobel Prize History of Molecular ModelingQ

uan

tum

M

ech

an

ics

Quantum Chemistry

Classical Molecular Simulation

Review of Nobel Information 2013 Chemistry

- Simulation or Modeling of molecules (in materials) on computers

- Classical (Newtonian) physics vs. Quantum (Schrodinger) physics

- Quantum description of atoms and molecules: electrons & nuclei

- Strength: applicable to describe electronic (photo)excitation- Strength: interatomic interactions described “naturally”- Strength: chemical reactions (bond formation/breaking)- Weakness: slow, expensive, small-scale (N < 102), @ 0 K- Classical description of atoms and molecules: balls & springs- Strength: fast, applicable to large-scale (large N) systems- Strength: close to our conventional picture of molecules- Strength: easy to code, free codes available, finite T - Weakness: interatomic interactions from us (force field)- Weakness: no chemical reactions, no electronic excitation- Application: structural, mechanical, dynamic properties

Quantum vs. Classical description of materials

With reasonable amount of resources, larger-scale (larger-N) systems can be described with classical simulations than with quantum simulations.

Quantum simulation in virtual space

N (number of atoms) or L (size) of a system of interest)

Diffi

cu

lty (

cost

, ti

me,

manpow

er,

in

acc

ura

cy)

Experiment in real space

easy

hard

Classical simulation in virtual space

First-principles Quantum MechanicsQM

MDLarge-scale Molecular Dynamics

- Validation: DFT + continuum solvation - Reaction: solvent molecule + CO2 complex

- Validation: Interatomic potential (Force Field)- Viscosity, diffusivity distribution: bulk solvent

Monte Carlo Process Simulation MC

- Grand Canonical (GCMC) or Kinetic (KMC) - Flue gas diffusion & Selective CO2 capture

Example of multi-scale molecular modeling:CO2 capture project

solvent (PzH2)

PzH2+-CO2

-

PzH-CO2H

PzH2 (regener)PzH2

+CO2-

PzH3+

PzHCO2-

+CO2

+PzH2

+PzH2

PzH3+

Pz(CO2)22-

PzH3+-CO2

PzH+-2CO2-+CO2

+CO2

Piperazine

PzH3+

HCO3-

HN NH

10.6 kcal/mol(MEA)

7.8O

HN

C

7

Step 1: Quantum:Reaction

Quantum simulation Example No. 2: Pd 촉매 반응 , UV/vis spectrum 재현 , 유기태양전지 효율 저하 설명

-50

-40

-30

-20

-10

0

10

Rel

ativ

e fr

ee e

nerg

y (k

cal/m

ol)

TS1 I1 TS2 I2 TS3 I3Pd+ 22BI

08.9

-32.4

-18.4

-34.7-29.1

-51.3

-26.5

Pd+ +2BI

gone!

PCE 3.1%

PCE 0.4%

-3.26

-5.22

-1.96

EX23.26 EX1

1.96

-2.12

-5.11

EX12.99

NN NN PdPd

H

H

NN

Pd

H

+NN

Pd+

NN NN PdPd

H

H

NN

Pd

H

+NN

Pd+

200 400 600 800 10000.0

0.5

1.0

1.5

2.0

2.5

Osc

illa

tor

stre

ng

th (f)

SS

NS

N

n1

What quantum/classical molecular modeling can bring to you: Examples.Reduction-oxidation potential, acibity/basicity (pKa), UV-vis spectrum, density profile, etc.

J. Phys. Chem. B (2006)

J. Phys. Chem. A (2009, 2001), J. Phys. Chem. B (2003),Chem. Res. Toxicol. (2003, 2002, 2000), Chem. Lett. (2007)

cm-1J. Phys. Chem. B (2011), J. Am. Chem. Soc. (2005, 2005, 2005)

Step 2: Classical: 2-species (AMP and PZ) distribution in water

Which one (among AMP and PZ) is less soluble in water?Which one is preferentially positioned at the gas-liquid interface?Which one will meet gaseous CO2 first? Hopefully PZ to capture CO2 faster, but is it really like that?Let’s see with the MD simulation on a model of their mixture solution!

제일원리 다단계 분자모델링

► 물질구조 분자수준 이해 ► 선험적 특성 예측 ► 신물질 설계 ► 물질특성 향상

2. 고전역학 분자동력학 모사 ( 컴퓨터 구축 102~107 개 원자계의 뉴턴방정식 풀기 )

- 전자 무시 , ball ( 원자 ) & spring ( 결합 ) 모델로 분자 /물질 표현 ( 힘장 )- Cheap ► 대규모 시스템에 적용 , 시간 /온도에 따른 구조 /형상 변화 모사

1. 양자역학 전자구조 계산 ( 컴퓨터 구축 101~103 개 원자계 슈레딩거방정식 풀기 )

- 정확 , 경험적 패러미터 불필요 , 제일원리계산 , but expensive ► 소규모 시스템

MULTISCALE

MODELING

MDatomistic molecular

QMelectronic structure

KMCcharge- transpor

t

CGMDcoarse- grained

FF

snapshotCG-FF

nanoscalemorphology

transport parameter

understandingnew designprediction

testvalidation

EXPERIMENTsynthesis

fabricationcharacterizati

on

First-principles multi-scale molecular modeling

I. 2013 Spring: Elements of Quantum Mechanics (QM) - Birth of quantum mechanics, its postulates & simple examples

Particle in a box (translation) Harmonic oscillator (vibration) Particle on a ring or a sphere (rotation)

II. 2013 Fall: Quantum Chemistry - Quantum-mechanical description of chemical systems

One-electron & many-electron atoms Di-atomic & poly-atomic molecules

III. 2014 Spring: Classical Molecular Simulations of materials - Large-scale simulation of chemical systems (or any collection of particles)

Monte Carlo (MC) & Molecular Dynamics (MD)

IV. 2014 Fall: Molecular Modeling of Materials (Project-oriented class) - Application of a combination of the above methods to understand structures, electronic structures, properties, and functions of various materials

Lecture series I-IV: Molecular Modeling of Materials

P

T

A typical experiment in a real (not virtual) space

1. Some material is put in a container at fixed T & P.

2. The material is in a thermal fluctuation, producing lots of different configurations (a set of microscopic states) for a given amount of time. It is the Mother Nature who generates all the microstates.

3. An apparatus is plugged to measure an observable (a macroscopic quantity) as an average over all the microstates produced from thermal fluctuation.

P

T

How do we mimic the Mother Nature in a virtual space to realize lots of microstates, all of which correspond to

a given macroscopic state?

How do we mimic the apparatus in a virtual space to obtain a macroscopic quantity (or property or

observable) as an average over all the microstates?

P

T

microscopic states (microstates)or microscopic configurationsunder external

constraints (N or , V or P, T or E,

etc.) Ensemble (micro-canonical,

canonical, grand canonical, etc.)

Average over a collection

of microstates

Macroscopic quantities (properties, observables)• thermodynamic – or N, E or T, P or V, Cv, Cp, H, S,

G, etc.• structural – pair correlation function g(r), etc.• dynamical – diffusion, etc.

These are what are measured in true experiments.

they’re generated naturally from thermal fluctuation

In a real-space experiment

In a virtual-space simulation

How do we mimic the Mother Nature in a virtual space to realize lots of microstates, all of which correspond to

a given macroscopic state? By MC & MD methods!

it is us who needs to generate them by QM/MC/MD methods.