text and data mining for material synthesis - olivetti, elsa presentation.pdftext and data mining...
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![Page 1: Text and Data Mining for Material Synthesis - Olivetti, Elsa Presentation.pdfText and Data Mining for Material Synthesis Elsa Olivetti, MIT Gerbrand Ceder, UC Berkeley Departments](https://reader030.vdocuments.mx/reader030/viewer/2022040913/5e89af79b553782823620ce4/html5/thumbnails/1.jpg)
Text and Data Mining for Material Synthesis
Elsa Olivetti, MIT Gerbrand Ceder, UC Berkeley Departments of Materials Science & Engineering
Andrew McCallum, UMass AmherstDepartment of Computer Science & Engineering
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1980 1991 2008
1954 1963 1982
Silicon Solar Cells
Lithium Ion Batteries
1st practical silicon solar cell invented at Bell Labs
Sharp produces 1st
practical solar module of silicon
solar cells
Kyocera 1st mass produces polysilicon cells
by today’s standard process
Oxford demonstrates 1st viable rechargeable
lithium battery
Sony sells 1st commercially available Li-ion batteries for
high price consumer electronics
1st uses of Li-ion battery in production
vehicles
Challenges for technology development: Timeline for development is long
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Modern data‐driven and first‐principles materials design accelerates pace of what to make…
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Phase diagrams SurfacesBandgaps
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Text extraction workflow
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Status of data dissemination
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Example: suggesting synthesis conditions for a specific morphology in titania
Edward Kim et al., Chemistry of Materials 2017
Experimentally‐accessible (and reported) variables to facilitate practical synthesis route planning.
*Grey circled points = accuracy test pointsAll other points =
training data points
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Virtual synthesis screening is hard: data is sparse & scarce
“Sparse” = high‐dimensional vector of synthesis actions“Scarce” = materials of interest not many papers published to train on
Can deep learning / generative models be useful for synthesis screening?
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Schematic of Variational Autoencoder
Variational autoencoder:• Loss = reconstruction + f(Gaussian)• Also a generative model
Edward Kim et al., npj Computational Materials 2017
Collaborator, Stefanie Jegelka, CSAIL, MIT
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Data augmentation with text & data mining
Edward Kim et al., npj Computational Materials 2017
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Example: suggesting synthesis conditions for stabilizing desired materials
Polymorphs for MnO2overlaid with most probable alkali‐ion use in synthesis (intercalation‐based phase stability)
Edward Kim et al., npj Computational Materials 2017
Photocatalysts
Lithium‐ion batteries Molecular sieves
Alkaline batteries
10,200 articles
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Exploratory: Rare phase in common material
Clustering of latent space shows driving conditions for polymorph of TiO2 for photocatalysis
Edward Kim et al., npj Computational Materials 2017
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Comparison of literature / virtual samples for SrTiO3 synthesisCalcination Sintering Annealing NaOH (M) Reference
800C, 2h ‐ ‐ 1 Ye et al, 2016800C, 2h 1250C, 2h ‐ ‐ Zhao et al, 20041000C, 12h ‐ 500C, 2h ‐ Zhao et al, 2015600‐750C, 4h ‐ ‐ ‐ Puangpetch et al, 2008721C, 1.8h ‐ 468C, 0.4h ‐ N/A
‐ ‐ 450C, 0.9h 1 N/A955C, 6h 1182C, 7.5h ‐ ‐ N/A
Edward Kim et al., npj Computational Materials 2017
One cannot train a model exclusively on literature data and classify something as successful or not, since there are no negative examples in the literature
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Key elements in machine learning for energy technology
Ramprasad et al npjcomputational materials, 2017
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Applicability and Next steps
• Continue to improve pipeline and disseminate information to the community
• Inform structure for data going forward• Use cases in:
• Solid state synthesis, hydrothermal and sol gel methods• Alloy design• Electrolyte performance
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Thank you olivetti.mit.edu
synthesisproject.org
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