Synthesis and Metrology of Combinatorial Materials Libraries

in discussion with combinatorial materials science community, computational materials science community

Ichiro TakeuchiUniversity of Maryland

Supported by DOE, DOD (ARO, AFOSR, ONR), NSF

Evolution of combinatorial approachesOvercoming challenges

Throughput trade-off

High-throughput synthesis

High-quality synthesis

Rapid characterization

Throughput trade-off

On-the-fly real-timeanalysis

Integration with theory, databases,standards, etc.

Accurate quantitativecharacterization

20 years ago (1990s):                                

Superconductor library (Xiang/Schultz)

Dielectric composition spread (van Dover)

Combinatorial electrochemistry (Mallouk)

the renaissance of the high‐throughput approach

Library synthesis under epitaxial growth conditions

F. Tsui (UNC)H. Koinuma, M. Lippmaa, T. Chikyow (COMET) 

Materials are of the same quality as single composition depositions

J.‐C. Zhao (OSU)

Diffusion multiples

J. Vlassak(Harvard)

Combinatorialmicrocalorimetry:

Glass transition,

latent heat, et.

Combinatorial alloy development

Combinatorial arc melting hearth (2014)

J. Cui (PNNL)

Spin-offs from combinatorial approachesCharacterization tool: microwave microscope

Xiang, Wei (LBNL, 96)(US Patent 5821410)

Dielectric mapping (LBNL, 98)

1”

Atomic resolution microwave microscope

(UMD & Intematix, 2010)

(Agilent, 2008)N9416S Scanning Microwave Microscopy

10 years ago: we all had ideas about combinatorial workflow

Where are they now? Do we (still) use them? If not, why not?

PI: A. Zaban (Bar Ilan)

Major combinatorial projects around the world

DOE Hubs:

Combinatorial approaches are integral part of major programs

JCESR

3.84 M Euro for 3 years

prepared by RIST

E. Amis, M. Fasolka, K. Beers (NIST)

Advanced microfluidics were developed

NIST Combinatorial Methods Center(1998 – 2007)

MGI and integration of high‐throughput computations and experiments

• Combinatorial experiments are the natural counterpart to computational efforts

• Accelerated experimental validations of computed results

• But how do we couple the efforts in a meaningful way?

• How do we close the gap between theory and experiments?

“ Ichiro, there is no communication gap between theorists and experimentalists. We just need to go drinking more often”    ‐ famous MGI theorist

Integration of theory and experiments in high-throughput materials science:

w/ S. Curtarolo, Duke

Step 1 Step 2 Step 3

Integrated materials discovery engine

Synchrotron diffraction set up at SSRL The entire 3” wafer (300 spots) can now be measured in 2 hrs

Rapid structural mapping of combinatorial wafers at synchrotron:moving from demonstration experiments to routine measurements

XRF carried out simultaneously

Each wafer produces: 300 MB to 2 GB of image data

Reflection set up with in-situ heaterTransmission set up

w/ J. Gregoire A. MehtaM.J. Kramer, Ames

In-situ reaction experiments

Non‐negative matrix factorization

Other techniques

• Multidimensional scaling with k-means

• Spectral clustering

• New graphical model and independent component analysis techniques

Fast and computationally inexpensive

Ensemble approach

FCC Fe22%

BCC Fe41%

FCC FePd31%

Machine learning techniques for automated XRD analysis (C. J. Long, A. G. Kusne)

Clustering: Mean shift theory

Each point on the ternary phase diagram is one X‐ray spectrum (expt or simulated) 

Points on binary lines are simulated spectra from ICSD

They are rapidly mined/analyzed  together 

Integrating databse (ICSD) with combiXRD data

Fe

Fe0.6Pd0.4

Fe0.6Ga0.4

A. G. Kusne (NIST)

Algorithm used: mean shift theory

XMCD (X-ray magnetic circular dichroism)

E. Arenholz, ALSNext: high-throughput ARPES (angle-resolved photoelectron spectroscopy)?

Element sensitive magnetic characterization

Combinatorial investigation of hard/soft magnetic coupling

Data mining (predictions) for novel materials:Need experimental databases

Problem: no appropriate databases exist

Solution: create our own databasefrom literature

Multi volume compilation of raw published data in forms of figures (graphs and tables) from articles

Excel sheet (compiled by 3 undergrads over 2 years)

Experimental Materials Databases- Exist in many fields, but often separately and in disparate

forms

- What you really need does not exist in databases or is not accessible

- Data-mining of experimental databases is valuable

- Some exceptions are crystallographic databases (ICSD 150,000 entries) and the NIMS databases

http://www.cmdnetwork.org/content/cmdnetwork/about.jsp organized by ASM

Data mining for novel multifunctional materials

Problem: no appropriate databases exist

Solution: create our own databasefrom literature

Multi volume compilation of raw published data in forms of figures (graphs and tables) from articles

Excel sheetSuperconductor database

Decades of tedious manual labor

Machine learning/reading techniques can be used to automatically go through PDF files

Automated knowledge discovery from multilingual science PDFs

w/ Synthesis Partners, LLC

Successfully demonstrated on 200 selected journal articles: Table of key magnetic parameters

So what are the challenges and opportunities now?

Combining combinatorial experiments with (high‐throughput) computational approaches can greatly help facilitate accelerated materials discovery

Handling large amount of data: need infrastructure: software development; need to do boring things like database curation

More advanced characterization tools are out there: XMCD, ARPES 

Problem:lack of funding for concerted efforts

Can we establish a (virtual) combinatorial/high-throughput materials synthesis center?

Many combinatorial synthesis and characterization techniques exist in a distributed way throughout the world (some duplications are needed!)

A Center would allow/force meaningful development of (centralized) data repository, management system(s) – solid foundation for theory/experiment integration also with databases

A. Mehta

(similar proposal being developed in China – Yalin Lu (USAFA)

Dec 2013

Combi2014

8th International Workshop on Combinatorial Materials Science and Technology

6 – 8th October 2014         CAIRNS, AUSTRALIAhttp://www.csiro.au/Combi14