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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
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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
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20 years ago (1990s):
Superconductor library (Xiang/Schultz)
Dielectric composition spread (van Dover)
Combinatorial electrochemistry (Mallouk)
the renaissance of the high‐throughput approach
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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
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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)
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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
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10 years ago: we all had ideas about combinatorial workflow
Where are they now? Do we (still) use them? If not, why not?
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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
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prepared by RIST
E. Amis, M. Fasolka, K. Beers (NIST)
Advanced microfluidics were developed
NIST Combinatorial Methods Center(1998 – 2007)
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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
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Integration of theory and experiments in high-throughput materials science:
w/ S. Curtarolo, Duke
Step 1 Step 2 Step 3
Integrated materials discovery engine
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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
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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
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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
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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
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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)
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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
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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
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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
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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
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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
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Combi2014
8th International Workshop on Combinatorial Materials Science and Technology
6 – 8th October 2014 CAIRNS, AUSTRALIAhttp://www.csiro.au/Combi14