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

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).

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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40

30

20

10

x (m

m)

50454035302520

2θ (°)

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30

20

10

5 mT

40

30

20

10

10 mT

40

30

20

10

50 mT

40

30

20

10

100 mT

2

3

4

5

6

7

8

9

1000

2Inte

nsity (arb

. u

nits)

1 mT

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Temperature effects

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Inc. T

Crystallinity increases with increasing deposition temperature up to 850°C

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

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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