Presentation given at ECS Fall, Oct 11 2021

1. Computational Materials Design and
Data Dissemination 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

2. Outline of talk
1. What is the Materials Project, and how can it be applied to functional materials design?
2. Engaging the community: Data contributions and benchmarking machine learning

3. 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
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4. Apps give insight into data
Materials Explorer
Phase Stability Diagrams
Pourbaix Diagrams
(Aqueous Stability)
Battery Explorer
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5. 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
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6. 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 6
Student
44%
Academia
36%
Industry
10%
Government
5%
Other
5%

7. 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 ≈
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8. Example – thermoelectrics discovery
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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)
1. Problem
Formulation
2. High-
throughput
computational
screening
3. Candidate
identification via
virtual screening
4. Synthesis and
testing

9. 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
(increasing rate of computationally-driven discoveries)
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10. Outline of talk
1.What is the Materials Project, and how can it be applied to functional
materials design?
2.Engaging the community: Data contributions and benchmarking
machine learning

11. 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?
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12. 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
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13. 2. Materials Project links
to your contribution
3. Your data set and
paper are linked
1. Google links to
Materials Project page
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From Google search to your data and your research, via MP

14. 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
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15. New to MPContribs: Benchmarking machine
learning algorithms (“Matbench”)
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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.

16. Matbench has an online leaderboard you can submit to – matbench.materialsproject.org

17. 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
Even more can be accomplished as a unified community to push
forward data dissemination as well as the capabilities of machine
learning
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18. 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)
Slides (already) uploaded to https://hackingmaterials.lbl.gov