Automated Machine Learning Applied to
Diverse Materials Design Problems
Anubhav Jain
Energy Technologies Area
Lawrence Berkeley National Laboratory
Berkeley, CA
MRS Spring Meeting, 2019
Slides (already) posted to hackingmaterials.lbl.gov

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There are many algorithms developed for machine learning
in materials – new ones are constantly reported!

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Q: Which one is the “best” based
on all the literature reports?

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Q: Which one is the “best” based
on all the literature reports?
A: Can’t tell! They are (almost?)
all tested on different data sets.

• Different data sets
– Source (e.g., OQMD vs MP)
– Quantity (e.g., MP 2018 vs MP 2019)
– Subset / data filtering (e.g., ehull<X)
• Different cross validation
metrics
– e.g., what fraction is test set?
• Often, this can’t be helped
– Usually can’t access training /
test data of past works
– Sometimes no runnable version
of a published algorithm
– should referees be tougher on this?
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Difficulty of comparing different ML algorithms
Data set used
in study A
Data set used
in study B
Data set used
in study C

• Matbench: a standard test method for materials
science problems
– A set of diverse materials data sets for testing
– A consistent cross-validation strategy
• Automatminer: A “black box” materials science
ML algorithm
– Materials-specific descriptors using matminer
– AutoML to tune hyperparameters
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Outline

• Matbench: a standard test method for materials
science problems
– A set of diverse materials data sets for testing
– A consistent cross-validation strategy
• Automatminer: A “black box” materials science
ML algorithm
– Materials-specific descriptors using matminer
– AutoML to tune hyperparameters
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Outline

• We want a test set that contains a diverse array
of problems
– Smaller data versus larger data
– Different applications (electronic, mechanical, etc.)
– Composition-only or structure information available
– Classification or regression
• We also want a cross-validation metric that gives
reliable error estimates
– i.e., less dependent on specific choice of splits
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A standard test method for ML algorithms in materials

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Overview of Matbench test set
Target Property Data Source Samples Method
Bulk Modulus Materials Project 10,987 DFT-GGA
Shear Modulus Materials Project 10,987 DFT-GGA
Band Gap Materials Project 106,113 DFT-GGA
Metallicity Materials Project 106,113 DFT-GGA
Band Gap Zhuo et al. [1] 6,354 Experiment
Metallicity Zhuo et al. [1] 6,354 Experiment
Bulk Metallic Glass formation Landolt -Bornstein 7,190 Experiment
Refractive index Materials Project 4,764 DFPT-GGA
Formation Energy Materials Project 132,752 DFT-GGA
Perovskite Formation Energy Castelli et al [2] 18,928 DFT-GGA
Freq. at Last Phonon PhDOS Peak Materials Project 1,296 DFPT-GGA
Exfoliation Energy JARVIS-2D 636 DFT-vDW-DF
Steel yield strength Citrine Informatics 312 Experiment
1. doi.org/10.1021/acs.jpclett.8b00124 2. doi.org/10.1039/C2EE22341D

<1K
1K-10K10K-100K
>100K
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Diversity of benchmark suite
mechanical
electronic
stability
optical
thermal
classification
regression
experiment
(composition
only)
DFT
(structure)
application data size
problem
type
data type

• Matbench: a standard test method for materials
science problems
– A set of diverse materials data sets for testing
– A consistent cross-validation strategy
• Automatminer: A “black box” materials science
ML algorithm
– Materials-specific descriptors using matminer
– AutoML to tune hyperparameters
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Outline

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Most commonly used test procedure
• Training/validation
is used for model
selection
• Test / hold-out is
used only for error
estimation
(Test set should not
inform model
selection, i.e. “final
answer”)

Think of it as N different “universes” – we have a different
training of the model in each universe and a different hold-out.
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Nested CV – like hold-out, but varies the hold-out set

Think of it as N different “universes” – we have a different
training of the model in each universe and a different hold-out.
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Nested CV – like hold-out, but varies the hold-out set
“A nested CV procedure provides an almost unbiased estimate of the true error.”
Varma and Simon, Bias in error estimation when using cross-validation for model
selection (2006)

• Matbench is a curated set of data sets that provide a
diverse set of problems representative of those
found in materials science
• ML developers can work on a consistent set of test
problems
• Ideally – consistent reports of error in the literature!
• Matbench v1 will be released soon …
– Let us know if you have feedback / comments /
suggestions!
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Summary of Matbench

• Matbench: a standard test method for materials
science problems
– A set of diverse materials data sets for testing
– A consistent cross-validation strategy
• Automatminer: A “black box” materials science
ML algorithm
– Materials-specific descriptors using matminer
– AutoML to tune hyperparameters
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Outline

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Typically several steps of machine learning are performed by
a human researcher – can these be automated?
Descriptors developed and
chosen by a researcher
ML model developed
and chosen by a
researcher
Why can’t we just give the computer some raw input data
(compositions, crystal structures) and output properties and get
back an ML model?

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Automatminer is a ”black box” machine learning model
Give it any data set with either composition or structure inputs, and
automatminer will train an ML model (no researcher intervention)

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Automatminer develops an ML model automatically given
raw data (structures or compositions plus output properties)
Featurizer
MagPie
SOAP
Sine Coulomb Matrix
+ many, many more
• Dropping
features with
many errors
• Missing value
imputation
• One-hot
encoding
• PCA-based
• Correlation
• Model-
based (tree)
Uses genetic
algorithms to find
the best machine
learning model +
hyperparameters

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Automatminer develops an ML model automatically given
raw data (structures or compositions plus output properties)
Featurizer
MagPie
SOAP
Sine Coulomb Matrix
+ many, many more
• Dropping
features with
many errors
• Missing value
imputation
• One-hot
encoding
• PCA-based
• Correlation
• Model-
based (tree)
Uses genetic
algorithms to find
the best machine
learning model +
hyperparameters

>60 featurizer classes can
generate thousands of potential
descriptors that are described in
the literature
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Matminer contains a library of descriptors for various
materials science entities
feat = EwaldEnergy([options])
y = feat.featurize([input_data])
• compatible with scikit-
learn pipelining
• automatically deploy
multiprocessing to
parallelize over data
• include citations to
methodology papers

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The matminer library is available for open use
Ward et al. Matminer : An open
source toolkit for materials data
mining. Computational Materials
Science, 152, 60–69 (2018).
Paper Docs Support
hackingmaterials.github.io
/matminer
https://groups.google.com/
forum/#!forum/matminer

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Automatminer develops an ML model automatically given
raw data (structures or compositions plus output properties)
Featurizer
MagPie
SOAP
Sine Coulomb Matrix
+ many, many more
• Dropping
features with
many errors
• Missing value
imputation
• One-hot
encoding
• PCA-based
• Correlation
• Model-
based (tree)
Uses genetic
algorithms to find
the best machine
learning model +
hyperparameters

• TPOT uses genetic algorithms to determine
the best ML model and hyperparameters
using the training / validation set
– Also some internal feature reduction, scaling,
etc. – a full pipeline of operations
• Menu of ML options is all the algorithms
implemented in scikit-learn
– i.e., not neural networks
• Parameters include population size and
number of generations for genetic algorithm
– Tradeoff between CPU time and performance
– Auto-convergence or early stop possible
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TPOT for AutoML
Olson, R. S. & Moore, J. H. TPOT: A Tree-based Pipeline Optimization Tool for
Automating Machine Learning. in Proceedings of the Workshop on Automatic Machine
Learning (eds. Hutter, F., Kotthoff, L. & Vanschoren, J.) 64, 66–74 (PMLR, 2016).

• Comparison 1: CGCNN
• Comparison 2: MEGNET
• Comparison 3: Untuned random forest (“no frills”)
– MAGPIE features for composition
– MAGPIE + Sine Coulomb matrix for structure
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Comparing automatminer against state-of-the-art
Xie, T. & Grossman, J. C. Phys. Rev. Lett. 120, 145301 (2018).
Chen, C., Ye, W., Zuo, Y., Zheng, C. & Ong, S. P.
arXiv:1812.05055 (2018).
Ward, L., Agrawal, A., Choudhary, A. & Wolverton, C. npj
Computational Materials 2, 16028–16028 (2016).
Ward, L., Agrawal, A., Choudhary, A. & Wolverton, C. npj
Computational Materials 2, 16028–16028 (2016).

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Matbench results for all algorithms

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How does data set size affect performance?
For all structure-based regression problems, divide the mean absolute
error of model by mean absolute deviation of the data set.
• Always predicting the mean would yield a value of 1.0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
100 1000 10000 100000 1000000
MAE/MAD
Data Set Size
automatminer
CGCNN
MEGNET

• Automatminer is much faster / easier to train
– One can adjust training time of all algorithms to some
extent
– Note that MEGNET is faster than CGCNN but same
order of magnitude
• GPUs might greatly accelerate CGCNN /
MEGNET training (no timing available)
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Algorithm training time per fold on 8-16 CPU cores
Data set size Automatminer CGNN MEGNET
~1K ~1 hour or less ~few hours ~few hours
~10K ~few hours ~few days ~few days
~100K ~12 hours ~few weeks ~few weeks

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Getting started with automatminer
Paper Docs Support
hackingmaterials.github.io
/automatminer
https://groups.google.com/
forum/#!forum/matminer
In preparation …

• We proposed a diverse benchmark test suite of
problems to develop and test ML algorithms against
• We presented a black-box ML algorithm,
Automatminer, that performs comparably or
outperforms literature values on small data sets
(N<10,000), but does more poorly on larger data sets
• Further upgrades to automatminer are in progress!
– See if we can do better on N>10,000 problems
– Although crystal networks might alternately use transfer
learning to tackle N<10,000 problems (e.g., MEGNET)
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Conclusions

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Acknowledgements
Alex Dunn
Graduate student
Qi Wang
Postdoc
Alex Ganose
Postdoc
Alireza Faghaninia
Postdoc
Samy Cherfaoui
Undergraduate
Daniel Dopp
Undergraduate
Funding:
U.S. Department
of Energy, Basic
Energy Sciences
Slides (already) posted to
hackingmaterials.lbl.gov