Internal presentation given at LBNL, May 2022

1. The Materials Project:
Applications to energy storage and
functional materials design
Anubhav Jain
Staff Scientist, Lawrence Berkeley National Laboratory
Associate Director, Materials Project
materialsproject.org
The Materials Project
Slides (already) uploaded to https://hackingmaterials.lbl.gov

2. 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

3. Outline of talk
1.What is the Materials Project, and how can it be applied to
functional materials design?
2.Use in energy storage research
3.Bridging the gap between theory and synthesis: related efforts

4. 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
4

5. The core data set keeps growing with time …
5

6. Apps give insight into data
Materials Explorer
Phase Stability Diagrams
Pourbaix Diagrams
(Aqueous Stability)
Battery Explorer
6

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
7

8. The Materials Project is used heavily by the research
community
> 180,000 registered
users
> 40,000 new users last year
~100 new registrations/day
~10,000 users log on every day
> 2M+ records downloaded through API each day; 1.8 TB of data served per month 8

9. Used in academia and in industry
9
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.”
Student
44%
Academia
36%
Industry
10%
Government
5%
Other
5%

10. 2. Materials Project links
to your contribution
3. Your data set and
paper are linked
1. Google links to
Materials Project page
10
Researchers can also contribute their own data sets to MP

11. Today, the Materials Project has led to
many examples of “computer to lab”
success stories
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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12. Outline of talk
1.What is the Materials Project, and how can it be applied to functional
materials design?
2.Use in energy storage research
3.Bridging the gap between theory and synthesis: related efforts

13. Historically, the Materials Project originated in battery
research and screening
13
Plain Oxides
(9204)
Silicates (1857)
Phosphates (1609)
Borates (1035)
Carbonates (370)
Vanadates (1488)
Sulfates (330)
Nitrates(61)
No Oxygen (4153)
Li
Containing
Compounds
Computed
Jain, Hautier, Moore,
Ong, Fischer,
Mueller, Persson,
Ceder
Comp. Mat. Sci
(2011)
High-throughput computational screening led
to the identification of several novel Li-ion
battery cathodes.
The data formed the basis of the original
Materials Project release.

14. Apart from identifying materials, it was used to create
“design maps” and better understand statistical trends
14
Phosphates as Lithium-Ion Battery Cathodes: An Evaluation
Based on High-Throughput ab Initio Calculations
Geoffroy Hautier, Anubhav Jain, Shyue Ping Ong, Byoungwoo
Kang, Charles Moore, Robert Doe, and Gerbrand Ceder
Chem. Mater. 2011, 23, 15, 3495–3508
Relating voltage and thermal safety in Li-ion battery
cathodes: a high-throughput computational study
Anubhav Jain , Geoffroy Hautier , Shyue Ping Ong,
Stephen Dacek and Gerbrand Ceder
Phys. Chem. Chem. Phys., 2015, 17, 5942-5953

15. The Materials Project continues to be used to identify
new battery materials
15
High-Throughput Computational Screening of Li-
Containing Fluorides for Battery Cathode Coatings
Bo Liu, Da Wang, Maxim Avdeev, Siqi Shi, Jiong
Yang, and Wenqing Zhang
ACS Sustainable Chem. Eng. 2020, 8, 2, 948–957
Researchers use the Materials
Project to design new cathode
coating materials to prevent
degradation

16. The Materials Project is used as a data set to train ML
models on battery properties
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Using Materials Project to train
models for electrode voltage and
volume change upon intercalation
Machine Learning Screening of Metal-Ion Battery Electrode Materials
Isaiah A. Moses, Rajendra P. Joshi, Burak Ozdemir, Neeraj Kumar, Jesse
Eickholt, and Veronica Barone
ACS Appl. Mater. Interfaces 2021, 13, 45, 53355–53362

17. For example, some works look for superionic Li-ion
conductors based on screening MP data sets
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Holistic computational structure screening of more than 12,000
candidates for solid lithium-ion conductor materials
Austin D. Sendek, Qian Yang, Ekin D. Cubuk, Karel-Alexander N. Duerloo, Yi
Cui and Evan J. Reed
Energy Environ. Sci., 2017, 10, 306-320
After building ML model
based on custom
experimental data, screen
MP database for new solid
electrolyte candidates

18. Outline of talk
1.What is the Materials Project, and how can it be applied to functional
materials design?
2.Use in energy storage research
3.Bridging the gap between theory and synthesis: related efforts

19. Data-driven synthesis science (“D2S2”):
understanding synthesis of complex materials
19
BiFeO3 synthesis
AuNP shape control
Led by G. Ceder (MSD)

20. We are currently building an
automated synthesis lab (“A-lab”)
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Dosing & mixing
Heating in tube/box furnaces
Auto-SEM/EDS
Auto-XRD
Crucible transfer Product handling
Throughput:
50-100 samples per day
In operation:
XRD
Robot
Box furnaces
Setting up:
Tube
furnace x 4
LBNL bldg. 30
Dosing and mixing
Labman
Box
furnace
Tube
furnace
UR
#1
UR
#2
Product
handling
XRD
loading
UR
#2
UR
#3
Complete
In-progress
Facility will handle powder-
based synthesis of inorganic
materials, with automated
characterization and
experimental planning

21. Early stages of the facility
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22. Conclusion and opportunities
• The Materials Project is used by a large community of users for functional
materials design and research
• Many potential applications to energy storage:
• Electrode materials (anode / cathode)
• Solid ion conductors
• Coatings
• New projects and capabilities are bridging the gap between virtual design and
physical realization – i.e., theory to synthesis
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