The Open Quantum Materials Database (OQMD): Features …...The Open Quantum Materials Database...
Transcript of The Open Quantum Materials Database (OQMD): Features …...The Open Quantum Materials Database...
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The Open Quantum Materials Database (OQMD): Features and Applications
(paradigms of materials discovery using prototypes and machine learning)
The Wolverton Research GroupDepartment of Materials Science and Engineering
Northwestern University
May 2, 2016
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The OQMD: a HT DFT database § A “high-throughput DFT” database of materials
Structure Sources
DFT calculations
Thermodynamic Analysis
Query-able Database
The ICSD, Prototypes
Clusters, Supercomputers
The qmpy framework
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The Open QMD§ Entire database accessible at www.oqmd.org
[[ a convenient online interface for querying the database ]]
§ The entire database can be downloaded, along with qmpy!
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The Open QMD§ If you are so inclined, you can even tweet at it!
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What data does the OQMD contain?Structure sources:§ The Inorganic Crystal Structure Database (ICSD)
[[ 40,000+ compounds with (currently) <40 atoms per unit cell ]]
§ Decorations of known prototype crystal structures
[[ 400,000+ compounds from elemental, binary, ternary prototypes ]]
The number of compounds is constantly growing!
B2 (CsCl) structure
→ combinatorial replacement with all elements in the periodic table
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What properties are calculated?§ At a given composition:
List of all compounds at the composition
Spacegroup
Formation energy
Number of atoms per cell
Structure sources
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What properties are calculated?§ For a each compound in the database:
Total energy, volume, magnetic moment, band gap calculated using DFT
Density of electronic states Fully (DFT) relaxed structure,
available for download
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What properties are calculated?§ For a given chemical system, the convex hull (0 K phase diagram):
e.g., Ti-Al-Ni
green = stablered = unstable
For example, Ti2AlNi is predicted to decompose into TiAlNi2 and Ti3Al
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qmpy: automating HT-DFT, thermodynamic analyses
§ Symmetry analysis (interfaced with python-based spglib)
§ Calculation of/plotting reaction voltages
§ Chemical potentials to estimate synthesis conditions
§ Multistep screening of prototypes for discovering materials, e.g.,
• How many stable, tetragonal compounds of the form X2YZ, with a
bandgap between 0.5 eV and 1.5 eV, made of elements not heavier than Pb,
and with lattice constant (a) close to that of Si, that have been synthesized
experimentally are there in the OQMD?
• Applications include searching for
1. new cathode materials for batteries,
2. new materials for thermochemical water splitting,
3. new solar cell materials,
4. new strengthening precipitates in alloys, and others…
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An example of prototype screening: new strengthening precipitates in alloys!
Kirklin, S., Saal, J. E., Hegde, V. I., & Wolverton, C. “High-throughput computational search for strengthening precipitates in alloys”, Acta Materialia, 102, 125-135.
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Machine Learning & OQMD
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Motivation: The Prototype Search12
CommonMethod: PrototypeSearch1. Selectacrystalstructure
2. Evaluateall possibilitieswithDFT3. Selectonlystableones
Challenge:ComputationalcostPossibleSolution: GuidewithML
DFT
ML Δ𝐻# = 𝑓
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Route #1: Composition-Based Models
Ref:Meredigetal.PRB.(2014)094104
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Goal:FindcompositionsofundiscoveredphasesMethod:1. TrainML,heuristicmodelonOQMDdata2. Rankunseencompoundsbasedonstability3. Predictcrystalstructuresofbestentries
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Route #1: Predicted Compounds
Ref:Meredigetal.PRB.(2014)094104
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Brighterregions:Morelikelytohaveundiscoveredcompounds
Intotal:>4500Predictedmaterials
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Route #1: Validated Predictions
Ref:Meredigetal.PRB.(2014)094104
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Route #2: Structure-Based Models 16
Goal:PredictcompositionandstructureApproach:
1. ComputeVoronoitessellation2. Assessatomicenvironments3. LinktoΔ𝐻# withML
Ref:Wardetal.,inpreparation
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Route #2: Ranking Unseen Compounds17
Test:TrainonICSD,rankprototypesbyΔ𝐻#
𝝉 = 0.7 ->85%ofcorrectranking!
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§ The OQMD: a high-throughput DFT database of materials
§ Open access at www.oqmd.org; qmpy available on GitHub
§ Discover new materials by screening prototypes and using ML
§ ML tools for materials science: Magpie
(https://bitbucket.org/wolverton/magpie)
§ Materials property predictor
(http://oqmd.org/static/analytics/composition.html)
→ platform for accelerated materials discovery and design!
Summary
For more details about OQMD:
J. E. Saal et al., JOM 65, 1501-1509 (2013) [ featured on JOM cover! ]
S. Kirklin et al., npj Computational Materials 1, 15010 (2015) [ featured on the website! ]