Conducting and Enabling Data-Driven Research Through the Materials Project

Conducting and Enabling Data-Driven
Research Through the Materials Project
Anubhav Jain
Staff Scientist, Lawrence Berkeley National Laboratory
Deputy Director, Materials Project
materialsproject.org
The Materials Project
Slides (already) uploaded to https://hackingmaterials.lbl.gov

The promise of data-driven research –
more successes, fewer failures
Conventional design
many ideas are tested and most result in failure
Data-driven design
ideas are tested (or even hypothesized) by a
computer prior to testing, leading to higher success
rates in experiment

Outline of talk
1. What is the Materials Project?
2. Applying the Materials Project to functional materials design
3. Engaging the community: Data contributions and benchmarking machine learning

What is the Materials Project?

The core of Materials Project is a free database of
calculated materials properties and crystal structures
Free, public resource
• www.materialsproject.org
Data on ~150,000 materials,
including information on:
• electronic structure
• phonon and thermal
properties
• elastic / mechanical properties
• magnetic properties
• ferroelectric properties
• piezoelectric properties
• dielectric properties
Powered by hundreds of millions
of CPU-hours invested into high-
quality calculations
5

The core data set keeps growing with time …
6

Apps give insight into data
Materials Explorer
Phase Stability Diagrams
Pourbaix Diagrams
(Aqueous Stability)
Battery Explorer
7

The code powering the Materials Project is
available open source (BSD/MIT licenses)
just-in-time error correction, fixing your
calculations so you don’t have to
‘recipes' for common materials
science simulation tasks
making materials science web apps easy
workflow management software for
high-throughput computing
materials science analysis code:
make, transform and analyze crystals,
phase diagrams and more
& more … MP team members also contribue to
several other non-MP codes, e.g. matminer for
machine learning featurization
8

Example: calculation workflows implemented in
by dozens of collaborators
Phonons
Elasticity Defects
Magnetism
Band
Structures
Stability
Grain
Boundaries
Equations
of State
X-ray
Absorption
Spectra
Piezoelectric
Dielectric
Surfaces
& more …
9
Requirements: VASP license and a big computer
9

The Materials Project is used heavily by the research
community
> 180,000 registered
users
> 40,000 new users last year
~100 new registrations/day
~5,000-10,000 users log on every day
> 2M+ records downloaded through API each day; 1.8 TB of data served per month 10

A large fraction of users are from industry
Student
44%
Academia
36%
Industry
10%
Government
5%
Other
5%
3.5%
Schrodinger: Many of our customers are active users of
the Materials Project and use MP databases for
their projects. Enabling direct access to MP databases
from within Schrödinger software is a powerful addition
that will be appreciated by our users.
Toyota: “Materials Project
is a wonderful project.
Please accept my
appreciation to you to
release it free and easy to
access.”
Hazen Research: “Amazing
and well done data base. I
still remember searching
Landolt-Börnstein series
during my PhD for similar
things.”
11

We invite you to learn more at an upcoming Materials
Project workshop (virtual) on August 10-12, 2021
https://matsci.org/t/materials-project-virtual-workshop-august-10th-12th-2021/35855
Very good. It changed my
perspective on how to do comp.
Mat. sci.
All the lectures were very useful for
me. I want to use all the tools!
Workshop features hands-on teaching to a
limited number of participants
(registration costs involved).
Interested but can’t attend?
Workshop materials will be posted online for
free at a later date. See:
https://workshop.materialsproject.org
for content from previous workshops.
12

Applying the Materials Project to
discover functional materials
carbon
capture
phosphors
electrides
magneto-
calorics
p-type
transparent
conductors
ferro-
electrics
piezo-
electrics
photo-
catalysts
thermo-
electrics

MP has been used to design many new
materials that have experimentally
confirmed useful properties
MP for p-type transparent conductors
References
✦ Hautier, G., Miglio,A., Ceder, G., Rignanese, G.-M. & Gonze, X. Identification and
design principles of low hole effective mass p-type transparent conducting oxides.
Nature Communications 4, (2013)
✦ Bhatia,A. et al. High-Mobility Bismuth-based Transparent p-Type Oxide from High-
Throughput Material Screening. Chemistry of Materials 28, 30–34 (2015)
✦ Ricci, F. et al.An ab initio electronic transport database for inorganic materials.
Scientific Data 4, (2017)
Prediction
Screening based on band
gap, transport properties
and band alignments.
Experiment
Predictions revealed
material with s–p
hybridized valence band
(thought to correlate
well with dopability).
When synthesized,
material has excellent
transparency and readily
dopable with K.
Ba2BiTaO6
MP for thermoelectrics
References
✦ Aydemir, U. et al.YCuTe2: a member of a new class of thermoelectric materials with
CuTe4-based layered structure. Journal of Materials Chemistry A 4, 2461–2472 (2016)
✦ Zhu, H. et al. Computational and experimental investigation ofTmAgTe2and
XYZ2compounds, a new group of thermoelectric materials identified by first-principles
high-throughput screening. Journal of Materials Chemistry C 3, 10554–10565 (2015).
✦ Pöhls, J.-H. et al. Metal phosphides as potential thermoelectric materials. Journal of
Materials Chemistry C 5, 12441–12456 (2017).
Prediction
Screening of tens of
thousands of materials
with predicted electron
transport properties
revealed a family of
promising XYZ2
candidates
Experiment
Several materials made:
YCuTe2 (zT = 0.75),
TmAgTe2 (zT = 0.47, 1.8
theoretical), novel NiP2
phosphide
TmAgTe2
MP for phosphors
References
✦ Wang, Z. et al. Mining Unexplored Chemistries for Phosphors for High-Color-
Quality White-Light-Emitting Diodes. Joule 2, 914–926 (2018)
✦ Li, S. et al. Data-Driven Discovery of Full-Visible-Spectrum Phosphor. Chemistry of
Materials 31, 6286–6294 (2019)
✦ Ha, J. et al. Color tunable single-phase Eu2+ and Ce3+ co-activated Sr2LiAlO4
phosphors. Journal of Materials Chemistry C 7, 7734–7744 (2019)
Prediction
Statistical analysis of existing
materials that co-occur with
word ‘phosphor’ followed
by structure prediction for
new materials
Experiment
Predicted first known Sr-Li-
Al-N quaternary, showed
green-yellow/blue emission
with quantum efficiency of
25% (Eu), 40% (Ce), 55%
(co-activated Eu, Ce)
Sr2LiAlN4
≈ç ≈
14

Thermoelectric materials convert between thermal
gradients and electricity
• A thermoelectric device can transform a thermal gradient to electricity,
or it can use electricity to drive a thermal gradient (refrigeration)
• Main advantages are reliability (no moving parts), scalability to small
size, and quietness
Alphabet Energy – 25kW generator
(no longer in business)
15

Designing thermoelectrics:
optimizing the figure of merit, zT
• Many materials properties are important for thermoelectrics
• Focus is usually on finding materials that possess a high “figure of merit”, or zT, for high
efficiency
• Target: zT at least 1, ideally >2
ZT = α2σT/κ
power factor
>2 mW/mK2
(PbTe=10 mW/mK2)
Seebeck coefficient
> 100 V/K
Band structure + Boltztrap
electrical conductivity
> 103 /(ohm-cm)
Band structure + Boltztrap
thermal conductivity
< 1 W/(m*K)
•  e from Boltztrap
•  l difficult (phonon-phonon scattering)
Very difficult to balance these properties using intuition alone!
16

Example of trade-offs to overcome:
e- conductivity and Seebeck
Heavy band:
ü Large DOS
(higher Seebeck and more carriers)
✗ Large effective mass
(poor mobility)
Light band:
ü Small effective mass
(improved mobility)
✗ Small DOS
(lower Seebeck, fewer carriers)
Multiple bands, off symmetry:
ü Large DOS with small effective
mass
✗ Difficult to design!
E
k
17

We calculated a database of transport properties using
high-throughput methods of the time (BoltzTraP)
F. Ricci, W. Chen, U. Aydemir, G.J. Snyder, G.-M.
Rignanese, A. Jain, et al., An ab initio electronic transport
database for inorganic materials, Sci. Data. 4 (2017) 170085.
Old method (BoltzTraP – screening is qualitative w/pitfalls)
New method (AMSET – screening is more quantitative)
Ganose, A. M.; Park, J.; Faghaninia, A.; Woods-Robinson, R.; Persson, K. A.; Jain, A. Efficient Calculation of Carrier Scattering Rates from First
Principles. Nat Commun 2021, 12 (1), 2222.
18

You can browse these yourself at Materials Project,
which hosts the data set
https://contribs.materialsproject.org/projects/carrier_transport/ We are looking for materials that:
• Have a high calculated power factor
(note: thermal conductivity
considered separately)
• Have the electronic band shapes we
desire
• Our experimental collaborators
(Snyder, Northwestern) are willing to
synthesize
19

Identifying a candidate – TmAgTe2
Background
• Exists in two structures, trigonal (high-temperature) and
tetragonal (low-temperature)
• Is easier to make p-type
Pros
• High calculated power factors (both n-type and p-type)
• Has the electronic structure features we are looking for
• In-line with experimental work on Ag tellurides
• Independent calcs indicate low thermal conductivity
Cons
• Requires ~1020 /cm3 carriers
• Contains Tm…
Zhu, H.; Hautier, G.; Aydemir, U.; Gibbs, Z. M.; Li, G.; Bajaj, S.; Pöhls, J.-H.; Broberg, D.; Chen, W.; Jain, A.; White, M. A.; Asta, M.; Snyder, G. J.; Persson, K.; Ceder, G. Computational and
experimental investigation of TmAgTe 2 and XYZ 2 compounds, a new group of thermoelectric materials identified by first-principles high-throughput screening, J. Mater. Chem. C, 2015, 3
20

Experimental validation – zT reaches ~0.5
• TmAgTe2 indeed exhibits thermoelectric
properties, with modest zT reaching 0.5
• Despite many approximations, the
experimental results are very close to
calculated expectations
• The main limitation to performance is that
expected zT is a very strongly peaked
function of carrier concentration, and
more doping is needed
• Calculations suggest TmAg defects may
make this difficult; however, a zT of 1.4 is
possible if solved
(1) Pöhls, J.-H.; Luo, Z.; Aydemir, U.; Sun, J.-P.; Hao, S.; He, J.; Hill, I. G.; Hautier, G.; Jain, A.; Zeng, X.; Wolverton, C.; Snyder, G. J.; Zhu, H.; White, M.
A. First-Principles Calculations and Experimental Studies of XYZ2 Thermoelectric Compounds: Detailed Analysis of van Der Waals
Interactions. J. Mater. Chem. A 2018, 6 (40), 19502–19519.
(2) Zhu, H.; Hautier, G.; Aydemir, U.; Gibbs, Z. M.; Li, G.; Bajaj, S.; Pöhls, J.-H.; Broberg, D.; Chen, W.; Jain, A.; White, M. A.; Asta, M.; Snyder, G. J.;
Persson, K.; Ceder, G. Computational and Experimental Investigation of TmAgTe 2 and XYZ 2 Compounds, a New Group of Thermoelectric
Materials Identified by First-Principles High-Throughput Screening. J. Mater. Chem. C 2015, 3 (40), 10554–10565.
21

A “spinoff” candidate – YCuTe2
We can dope it, but calculations show it can’t go too far
Background
• Also exists in two structures, with high-temperature phase
being a disordered version of low-temperature
Pros
• More abundant and friendly elements than YCuTe2
• Has some of the electronic structure features we are looking
for
• Independent calcs indicate low thermal conductivity
• Doping may be easier
Cons
• Expected performance is only about ~half that of TmAgTe2
Zhu, H.; Hautier, G.; Aydemir, U.; Gibbs, Z. M.; Li, G.; Bajaj, S.; Pöhls, J.-H.; Broberg, D.; Chen, W.; Jain, A.; White, M. A.; Asta, M.; Snyder, G. J.; Persson, K.; Ceder, G. Computational and
experimental investigation of TmAgTe 2 and XYZ 2 compounds, a new group of thermoelectric materials identified by first-principles high-throughput screening, J. Mater. Chem. C, 2015, 3
experiment
computation
22

Many more TE candidates have been designed with
the help of computations, see recent review
Urban, J. J.; Menon, A. K.; Tian, Z.; Jain, A.; Hippalgaonkar, K. New Horizons in
Thermoelectric Materials: Correlated Electrons, Organic Transport, Machine
Learning, and More. Journal of Applied Physics 2019, 125 (18), 180902.
https://doi.org/10.1063/1.5092525.
Highest zTs actually came from most
limited screening in these cases,
although novelty (half-Heuslers) was
arguably less
i.e.,
If you are looking for novelty,
screening large spaces may be the
way to go
If you are looking for performance,
then more targeted computational
searches in known areas might be
more effective
Note also increasing rate of
computational discovery …
23

Some notes on these case studies
• Despite all the approximations, what we could calculate was quite predictive
of the experimental results
• The major limitation was an aspect of the material we could not effectively
compute (dopability)
• Thus, computations can be good at telling you what’s possible, but
sometimes cannot fully tell you if it is possible
• On the flip side, computations can also help define what’s likely not possible
(i.e., no reason to keep working on YCuTe2)
• Some evidence that targeted computational screening is more effective at
achieving high performance, but large-scale screening gets you more novelty
24

If you are interested in more case studies like this,
we describe more examples in a review
Jain, A.; Shin, Y.; Persson, K. A. Computational Predictions of Energy
Materials Using Density Functional Theory. Nature Reviews Materials
2016, 1 (1), 15004. https://doi.org/10.1038/natrevmats.2015.4
Many more examples since writing this
(remember increasing rate of computationally-driven discoveries)
25

Engaging the community:
data platform and ML benchmarks

How can we use Materials Project to build a
community of materials researchers?
Materials Project now has
high visibility (e.g., by search
engines)
How can we use this
platform to help add value to
the community of materials
researchers?
27

Beyond calculations: MPContribs allows the research
community to contribute their own data
A “materials detail page,”
containing all the information MP
has calculated about a specific
material
Experimental data on a
material (either specific
phase, composition, or
chemical system)
“MPContribs” bridges
the gap
28

2. Materials Project links
to your contribution
3. Your data set and
paper are linked
1. Google links to
Materials Project page
29
From Google search to your data and your research, via MP

MPContribs is open for contributions
You can now apply to contribute
your data set and we will work
with you to disseminate via MP
Designed for:
• smaller data sets (e.g., MBs to
GBs); for large data files see
NOMAD or other repos
• Linking to MP compositions
Available via mpcontribs.org
30

31
Machine learning
High-throughput DFT
Expensive calculation
Experiment
Millions of candidates
The next challenge – incorporating machine learning

MP is now involved in an effort to benchmark
various machine learning algorithms
32

The current state of comparison
• 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 evaluation metrics
• Different error metrics (e.g., RMSE vs MAE)
• Different test set fraction (e.g., 10% vs 20%)
• Often no runnable version of a published algorithm.
Data set used
in study A
Data set used
in study B
Data set used
in study C
33

What’s needed –
an “ImageNet” for materials science
https://qz.com/1034972/the-data-that-changed-the-direction-of-ai-research-and-possibly-the-world/
34

Can we make the same
advancements in materials
as in computer vision?
One of the reasons computer science
/ machine learning seems to advance
so quickly is that they decouple
data generation from algorithm
development
This allows groups to focus on
algorithm development without all
the data generation, data cleaning,
etc. that often is the majority of an
end-to-end data science project
Clear comparisons also move the
field forward and measure progress 35

The ingredients of the Matbench
benchmark
qStandard data sets
qStandard test splits according to nested cross-validation procedure
qAn online leaderboard that encourages reproducible results
36

Matbench includes 13 different ML tasks
37
Dunn, A.; Wang, Q.; Ganose, A.; Dopp, D.; Jain, A. Benchmarking Materials Property Prediction Methods: The Matbench Test Set and Automatminer
Reference Algorithm. npj Comput Mater 2020, 6 (1), 138. https://doi.org/10.1038/s41524-020-00406-3.

The tasks encompass a variety of
problems
38

The ingredients of the Matbench
benchmark
qStandard data sets
qStandard test splits according to nested cross-validation procedure
qAn online leaderboard that encourages reproducible results
39

Under development: The
Matbench “leaderboard”
40
https://hackingmaterials.lbl.gov/matbench/
How will it work?
• You can test your algorithm on the full set
of benchmarks in ~10 lines of code
• If the algorithm outperforms an existing
leaderboard entry, you can submit a pull
request containing the output of the test
and it will show up on the leaderboard
https://github.com/hackingmaterials/matbench

What algorithms have been tested
on the matbench data set so far?
41
•Automatminer (will be discussed next)
• Dunn, A.; Wang, Q.; Ganose, A.; Dopp, D.; Jain, A. Benchmarking Materials Property Prediction
Methods: The Matbench Test Set and Automatminer Reference Algorithm. npj Comput Mater 2020, 6
(1), 138.
•CGCNN (run by automatminer team)
• Xie, T.; Grossman, J. C. Crystal Graph Convolutional Neural Networks for an Accurate and
Interpretable Prediction of Material Properties. Phys. Rev. Lett. 2018, 120 (14), 145301.
•MEGNET (run by automatminer team)
• Chen, C.; Ye, W.; Zuo, Y.; Zheng, C.; Ong, S. P. Graph Networks as a Universal Machine Learning
Framework for Molecules and Crystals. Chemistry of Materials 2019, 31 (9), 3564–3572.
•MODNet (results to be integrated)
• De Breuck, P.-P.; Evans, M. L.; Rignanese, G.-M. Robust Model Benchmarking and Bias-Imbalance in
Data-Driven Materials Science: A Case Study on MODNet. arXiv:2102.02263 [cond-mat] 2021.
•CRABNet (results to be integrated)
• Wang, A.; Kauwe, S.; Murdock, R.; Sparks, T. Compositionally-Restricted Attention-Based Network for
Materials Property Prediction; ChemRxiv, 2020. https://doi.org/10.26434/chemrxiv.11869026.v1.

Results so far: graph NN for large
data sets, conventional ML for small
42

Results so far: graph NN for large
data sets, conventional ML for small
But watch the leaderboard for
changes!!
Already some ML algorithms may do
better on both large and small data
(e.g. MODNet reported results)
43

Concluding thoughts
The Materials Project is a free resource providing data and tools to
help perform research and development of new materials
The number of proven examples of data-driven materials design is
increasing, and joint computational–experimental discoveries are
becoming common as in the case of thermoelectric materials
Even more can be accomplished as a unified community to push
forward data dissemination as well as the capabilities of machine
learning
44

Kristin Persson
MP Director
The team Intro
Thank you!
Matt Horton
Staff Scientist
(Materials
Project)
Patrick Huck
Staff Scientist
(MPContribs)
Alex Dunn
Grad Student
(Matbench)
Special thanks to Matt Horton for many slides /
graphics used in this talk!
Slides (already) uploaded to https://hackingmaterials.lbl.gov

MP for p-type transparent conductors
References
✦ Hautier, G., Miglio, A., Ceder, G., Rignanese, G.-M. & Gonze, X. Identification and
design principles of low hole effective mass p-type transparent conducting oxides.
Nature Communications 4, (2013)
✦ Bhatia, A. et al. High-Mobility Bismuth-based Transparent p-Type Oxide from High-
Throughput Material Screening. Chemistry of Materials 28, 30–34 (2015)
✦ Ricci, F. et al. An ab initio electronic transport database for inorganic materials.
Scientific Data 4, (2017)
Prediction
Screening based on band
gap, transport properties
and band alignments.
Experiment
Predictions revealed
material with s–p
hybridized valence band
(thought to correlate
well with dopability).
When synthesized,
material has excellent
transparency and readily
dopable with K.
Ba2BiTaO6
47

MP for carbon capture
References
✦ Dunstan, M. T. et al. Large scale computational screening and experimental discovery
of novel materials for high temperature CO2 capture. Energy & Environmental
Science 9, 1346–1360 (2016).
Prediction
Using phase diagram
functionality, looked at 600
materials and 1400
carbonination reactions to
extract 5 synthesis
candidates
Experiment
Demonstration of rapid
screening a success, with
synthesized material
demonstrating reversible
carbonination
Na3SbO4
48

MP for phosphors
References
✦ Wang, Z. et al. Mining Unexplored Chemistries for Phosphors for High-Color-
Quality White-Light-Emitting Diodes. Joule 2, 914–926 (2018)
✦ Li, S. et al. Data-Driven Discovery of Full-Visible-Spectrum Phosphor. Chemistry of
Materials 31, 6286–6294 (2019)
✦ Ha, J. et al. Color tunable single-phase Eu2+ and Ce3+ co-activated Sr2LiAlO4
phosphors. Journal of Materials Chemistry C 7, 7734–7744 (2019)
Prediction
Statistical analysis of existing
materials that co-occur with
word ‘phosphor’ followed
by structure prediction for
new materials
Experiment
Predicted first known Sr-Li-
Al-N quaternary, showed
green-yellow/blue emission
with quantum efficiency of
25% (Eu), 40% (Ce), 55%
(co-activated Eu, Ce)
Sr2LiAlN4
≈ç ≈
49

MP for magnetocalorics
References
✦ Horton, M. K., Montoya, J. H., Liu, M. & Persson, K. A. High-throughput prediction of
the ground-state collinear magnetic order of inorganic materials using Density
Functional Theory. npj Computational Materials 5, (2019)
✦ Cooley, J. A. et al. From Waste-Heat Recovery to Refrigeration: Compositional
Tuning of Magnetocaloric Mn1+xSb. Chemistry of Materials 32, 1243–1249 (2020).
Prediction
Screening of materials using
a ‘magnetic deformation’
proxy, a descriptor that
correlates with entropy
change during magnetization
Experiment
Many new candidates
revealed not previously
studied for magnetocaloric
properties. One,
synthesized, was shown to
be readily tuneable for a
room temperature peak
response.
MnSb
50

MP for photocatalysts
References
✦ Yan, Q. et al. Solar fuels photoanode materials discovery by integrating high-
throughput theory and experiment. Proceedings of the National Academy of
Sciences 114, 3040–3043 (2017)
✦ Yan, Q. et al. Mn2V2O7: An Earth Abundant Light Absorber for Solar Water
Splitting. Advanced Energy Materials 5, 1401840 (2015)
Experiment
Candiate material is earth-
abundant, shows nearly
optimal properties, and
experiment confirms
significant photocurrent
generated.
Mn2V2O7
Prediction
In addition to band
energetics, Pourbaix
infrastructure allowed
screening based on aqeous
stability.
51

MP for electrides
References
✦ Burton, L. A., Ricci, F., Chen, W., Rignanese, G.-M. & Hautier, G. High-Throughput
Identification of Electrides from All Known Inorganic Materials. Chemistry of
Materials 30, 7521–7526 (2018).
✦ Chanhom, P. et al. Sr3CrN3: A New Electride with a Partially Filled d-Shell
Transition Metal. Journal of the American Chemical Society 141, 10595–10598
(2019).
Prediction
Screening to look for
materials with empty space
and excess electrons
(according to formal
valence)
Experiment
From 65 new potential
electrides, synthesized the
only known electride
containing a redox-active
element
Sr3CrN3
52

MP for thermoelectrics
References
✦ Aydemir, U. et al. YCuTe2: a member of a new class of thermoelectric materials with
CuTe4-based layered structure. Journal of Materials Chemistry A 4, 2461–2472 (2016)
✦ Zhu, H. et al. Computational and experimental investigation of TmAgTe2and
XYZ2compounds, a new group of thermoelectric materials identified by first-principles
high-throughput screening. Journal of Materials Chemistry C 3, 10554–10565 (2015).
✦ Pöhls, J.-H. et al. Metal phosphides as potential thermoelectric materials. Journal of
Materials Chemistry C 5, 12441–12456 (2017).
Prediction
Screening of tens of
thousands of materials
with predicted electron
transport properties
revealed a family of
promising XYZ2
candidates
Experiment
Several materials made:
YCuTe2 (zT = 0.75),
TmAgTe2 (zT = 0.47, 1.8
theoretical), novel NiP2
phosphide
TmAgTe2
53

MP for piezoelectrics
References
✦ Garten, L. M. et al. Theory-Guided Synthesis of a Metastable Lead-Free Piezoelectric
Polymorph. Advanced Materials 30, 1800559 (2018)
✦ Ding, H. et al. Computational Approach for Epitaxial Polymorph Stabilization through
Substrate Selection. ACS Applied Materials & Interfaces 8, 13086–13093 (2016)
✦ de Jong, M., Chen, W., Geerlings, H., Asta, M. & Persson, K. A. A database to enable
discovery and design of piezoelectric materials. Scientific Data 2, (2015)
Prediction
MP predicts piezoelectric
response directly. With the
Materials Project substrate
matcher, energetically-
favorable substrates for
epitaxial growth were also
predicted.
Experiment
Predicted metastable phase
synthesized on predicted
substrate with direct
piezoelectric response
demonstrated.
SrHfO3
54

Conducting and Enabling Data-Driven Research Through the Materials Project

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