Integration of machine learning and combinatorial ... · •Achieved by manipulation of deposition...
Transcript of Integration of machine learning and combinatorial ... · •Achieved by manipulation of deposition...
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Integration of machine learning and combinatorial synthesis methods for triple conducting oxide discoveryJake Huang and Meagan Papac
MIDDMI Spr ing 2018
Apr i l 30 , 2018
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Outline
Introduction and Experimental Setup
Model Development
Experimental Validation and Iteration
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Introduction and Experimental Setup
Model Development
Experimental Validation and Iteration
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• Develop a fundamental understanding of compositional and structural effects on mixed electronic and ionic conductivity
• Predict compositions with targeted properties
• Materials system => Ba(Zr, Y, Co, Fe)O3
Goal of experimental study
Perovskite structureCompositional space defined by
B-site dopants
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Combinatorial pulsed laser deposition
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Combinatorial pulsed laser deposition
Substrate
Ceramic pellets
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Combinatorial pulsed laser deposition
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Composition is dependent on ratio of pulses per cycle
44 measureable points!
4% Co
14% Co
BaZr0.9Co0.1O3-d
BaZr0.1Co0.9O3-d
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24% Co
34% Co
Composition is dependent on ratio of pulses per cycle
BaZr0.9Co0.1O3-d
BaZr0.1Co0.9O3-d
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Large lattice constant
• Large A-site cation (ie. Ba)
Cubic symmetry
• Doping above 20 mol% Y in Y:BZO showed decrease proton conductivity, attributed
to structural distortion and proton trapping1
Highly crystalline structure
• Achieved by manipulation of deposition parameters within the PLD chamber
Stoichiometric barium concentration
• Barium deficiency can negatively impact proton conductivity2
Hypothesized factors
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1Pergolesi, D. et al. High proton conduction in grain-boundary-free yttrium-doped barium zirconate films grown by pulsed laser deposition. Nat. Mater. 9, 846–52 (2010).2Bae, H., Lee, Y., Kim, K. J. & Choi, G. M. Effects of Fabrication Conditions on the Crystallinity, Barium Deficiency, and Conductivity of BaZr 0.8 Y 0.2 O 3- δ Films Grown by Pulsed Laser Deposition. Fuel Cells 15, 408–415 (2015).
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Synthesis conditionsVariables Deposition pressure
Substrate temperature
Laser pulse rate
Laser pulse energy
Substrate composition
Number of pulses per target per cycle
Number of cycles
Constraints System limitations
Desired composition or thickness
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Previous strategy: guess and check
Chose parameters based on literature
Used temperature gradient substrate holder• Deposits at multiple temperatures in a single deposition
Varied the deposition pressure • 1, 5, 10, 50, and 100 mT
Tried 4 different substrates• No obvious result
Made lots (and lots and lots and lots) of plots to compare different variables
Time restraints limit number of possible combinations.
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Temperature effects
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Inc. T
Crystallinity increases with increasing deposition temperature up to 850°C
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Pulse frequency (BaZr0.1Y0.1Co0.3Fe0.5)
(Ba concentration is 46-50% for all frequencies)
5 Hz 10 Hz
20 Hz 40 Hz
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Benefits of machine learning
Identify overlooked areas of composition• Previously based off of target combinations rather than covering entire space
Provide predictions where data is lacking• Characterization can be time consuming
Provide tools to analyze these large data sets more quickly• No single variable ever changes alone
Increase Co concentration decrease Zr concentration
Higher deposition pressure higher Ba non-stoichiometry
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Introduction and Experimental Setup
Model Development
Experimental Validation and Iteration
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First steps: loading data to Citrination
Export
Ingest
CitrinationProcessing details
Property Data
Data Processing
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Experimental data: 110 FWHM vs. B-site composition
Indicates composition made but no FWHM measured
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First-pass model: predicting 110 FWHM from chemical formula
Only input: chemical formula
Non-dimensional error: 0.70
Good enough for qualitative identification of trends
High uncertainty for quantitative predictions
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Incorporating additional features yields only marginal gains
Inputs:
Chemical formula
8 deposition parameters
12 additional perovskite-specific chemical features
Non-dimensional error: 0.68
Marginal improvement over formula-only model
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Key model inputs include both composition and processing features
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Key model inputs include both composition and processing features
Key composition
features
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Key model inputs include both composition and processing features
Key composition
features
Key processing parameters
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Model allows rapid screening of composition space
Plotted predictions assume Ba=0.9
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Model predictions also reveal influence of processing
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Introduction and Experimental Setup
Model Development
Experimental Validation and Iteration
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Experimental validation of model predictions
Goal: Test model predictions for B-site composition and processing trends
Deposit 2 samples with identical, novel composition
Deposit first sample with low pulses per cycle, second with high pulses per cycle
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Recommended test composition: BaxCo0.7Fe0.1Y0.1Zr0.1O3
Test Composition:
Appears in untested composition region
Appears between regions of low and high FWHM
Has fairly low predicted FWHM (~ 0.5)
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Lower pulses/cycle produces smaller FWHM but larger variance
Average FWHM: 0.50Standard Deviation: 0.04
Average FWHM: 0.50Standard Deviation: 0.02
Average
Average
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Model fails to capture variation within test samples
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Model fails to capture variation within test samples
Actual values within samples vary significantly, despite homogeneous composition and processing
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Model fails to capture variation within test samples
Actual values within samples vary significantly, despite homogeneous composition and processing
Some aspect of processing varies within samples
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Adding positional identifier improves model performance
Positional identifier reduces error from 0.68 to 0.59
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Binary classification simplifies prediction
110 FWHM
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Structure Classification
Binary classification simplifies prediction
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Classification model shows excellent performance
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Key features of classification model are different from those of FWHM model
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Key features of classification model are different from those of FWHM model
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Perovskite-specific features
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Future work
Continue developing models Develop positional feature that describes plume density for
different target configurations
Iteratively test and improve model predictions
Build models for additional material properties as data becomes available
Derive physical insight from models
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Acknowledgments
MIDDMI Support Team:
Advisors:
Ryan O’Hayre Andriy Zakutayev
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Appendix
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Model Processing Trends: Laser Energy
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Model Processing Trends: Substrate
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Model Composition Trends: A-Site Stoichiometry
viewId 4209
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Processing Trends for Test Composition BaxCo0.7Fe0.1Y0.1Zr0.1O3
Plotted predictions assume Ba=0.9
Model predicts worse crystallinity with low laser energy, but with low confidence
Better crystallinity expected with lower pulse rate
Substrate has negligible impact
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Accessible composition space
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Classification model predicts probability of crystallinity
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Fuel cells consist of many components and sub-components
Full Cell
AnodeElectrolyteCathode
Catalyst Dispersant Binder
Sintering Aid Ceramic Precursors
Ceramic Precursors
Ceramic Precursors
Sintering Aid
Pore Former
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SQL database for data management
Enables robust tracking of materials, batches, and processing details
Captures hierarchical, many-to-many system-subsystem relationships
Easily integrate with Citrination
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