Discovering and Exploring New Materials through the Materials Project

Discovering and Exploring New
Materials through the Materials
Project
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

Outline of talk
1.What is the Materials Project?
2.Applications to functional materials design
3.Community data contributions
4.Using the software tools

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
3

The core data set keeps growing with time …
4

Apps give insight into data
Materials Explorer
Phase Stability Diagrams
Pourbaix Diagrams
(Aqueous Stability)
Battery Explorer
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
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
~10,000 users log on every day
> 2M+ records downloaded through API each day; 1.8 TB of data served per month 7

Used in academia and in industry
8
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%

Historically, the Materials Project originated in battery
research and screening
10
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.

The Materials Project continues to be used to identify
new battery materials
11
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

The Materials Project is used as a data set to train ML
models on battery properties
12
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

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 ≈
13

If you are interested in more case studies like this,
we describe more examples in a review paper
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)
14

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?
16

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
17

2. Materials Project links
to your contribution
3. Your data set and
paper are linked
1. Google links to
Materials Project page
18
From Google search to your data and your research, via MP

Current status of contributions
19
Main MPContribs Lightsources Machine-Learning
Public projects 27 1 13
Private projects 12 4 0
Contributions
(~1GB)
834,002 188 408,062
Structures (~12GB) 505,773 0 231,307
Tables (~1GB) 385,678 189 0
Attachments
(~38GB)
521,477 2 0
https://next-gen.materialsproject.org/catalysis
“standard” data sets
“enhanced” data sets
Advanced Search, Visualize, etc.

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
20

New to MPContribs: Benchmarking machine
learning algorithms (“Matbench”)
21
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 ingredients of the Matbench
benchmark
ü Standard data sets
ü Standard test splits according to nested cross-validation procedure
ü An online leaderboard that encourages reproducible results
22

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

Steps for theory & simulation
25
material generation
simulation procedure
calculation & execution
data analysis
(crystal, surface/interface,
molecule, etc...)
simulation parameters and
sequence (workflow)
job management,
execution, error recovery
databases, plotting,
analysis, insight

Software stack for theory &
simulation
26
material generation
data analysis
Custodian

27
material generation
data analysis
molecule, etc...)
sequence (workflow)
job management,
analysis, insight

Step 1: materials generation
28
material
generation
• Pymatgen can help generate
models for:
– crystal structures
– molecules
– systems (surfaces, interfaces, etc.)
• Tools include:
– order-disorder (shown at right) and
SQS
– interstitial finding
– surface / slab generation
– structure matching and analysis
– get structures from Materials Project
Example: Order-disorder
resolve partial or mixed
occupancies into a fully ordered
crystal structure
(e.g., mixed oxide-fluoride site
into separate oxygen/fluorine)

29
material generation
data analysis
molecule, etc...)
sequence (workflow)
job management,
analysis, insight

Atomate contains a library of simulation procedures
30
Mathew, K. et al Atomate: A high-level interface to generate, execute, and analyze
computational materials science workflows, Comput. Mater. Sci. 139 (2017) 140–152.
electronic bandstrcutre
elastic tensor
piezo/dielectric tensor
ferrolectricity
nudged elastic band
EELS/XAS
dipole moment
thermo expansion
exchange
QChem FF optimization
charge density
lobster
Gibbs
magnetic
SCAN funtional
LAMMPS
adsorption 2017 package
CP2K [WIP]
QChem force [WIP]
NBO [WIP]
...
lattice dymamics [WIP]
2022 package
2017|first
launch 2022|5 years
progress 202X|future
plan
a
b

Each simulation procedure in atomate is composed of
multiple levels of detail / abstraction
31
Workflow – complete set of calculations to get a materials property
Firework – one step in the Workflow (typically one DFT calculation)
Firetask – one step in a Firework
Starting with just a crystal structure,
this workflow performs four
calculations to get an optimized
structure, optimized charge
density, and band structure on two
types of grids (uniform and line)

32
material generation
data analysis
molecule, etc...)
sequence (workflow)
job management,
analysis, insight

FireWorks allows you to write your workflow once and execute (almost) anywhere
33
• Execute workflows locally
or at a supercomputing
center
• Queue systems supported
– PBS
– SGE
– SLURM
– IBM LoadLeveler
– NEWT (a REST-based API at
NERSC)
– Cobalt (Argonne LCF)
• Cloud based services
(user-generated)
– https://github.com/CovertLa
b/borealis

Dashboard with status of all jobs
34

Job provenance and automatic metadata storage
35
what machine
what time
what directory
what was the output
when was it queued
when did it start running
when was it completed

36
material generation
data analysis
molecule, etc...)
sequence (workflow)
job management,
analysis, insight

Examples of analyses
37
phase diagrams
Pourbaix diagrams
diffusivity from MD
band structure analysis

• Papers: good general overview / vision but of no practical help
• Online MP Workshop videos and tutorials: good way to get started
but not comprehensive. A “zero to one” situation
• Code documentation: Most comprehensive way to learn, although
some experience probably necessary
• Help channels: Useful if you already started and run into problems
38
What resources are available to learn?

• Resources on http://workshop.materialsproject.org
• Videos on MP channel of Youtube:
– https://www.youtube.com/channel/UC6pqY-__Nu-
mKkv0LMP8FJQ
39
Online MP workshop and videos

Online documentation
• Online documentation is the most comprehensive writeup
– www.pymatgen.org
– https://materialsproject.github.io/fireworks/
– https://materialsproject.github.io/custodian/
– https://hackingmaterials.github.io/atomate/
• The online documentation includes installation, examples, tutorials, and
descriptions of how to use the code
• If you want to do “everything”, suggest starting with atomate and going
from there
40

Help lists
• A help forum for each code is at https://discuss.matsci.org
41
Note: matsci.org now
includes help forums
for ~25 different
codes!
Including LAMMPS,
ASE, NOMAD, and
many more

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
42

Kristin Persson
MP Director
The team Intro
Thank you!
Matt Horton
Staff Developer
(Materials
Project)
Patrick Huck
Staff Developer
(MPContribs)
Slides (already) uploaded to https://hackingmaterials.lbl.gov

Discovering and Exploring New Materials through the Materials Project

Recommended

Recommended

More Related Content

Similar to Discovering and Exploring New Materials through the Materials Project

Similar to Discovering and Exploring New Materials through the Materials Project (20)

More from Anubhav Jain

More from Anubhav Jain (20)

Recently uploaded

Recently uploaded (20)

Discovering and Exploring New Materials through the Materials Project