The Materials Genome
Shortening the Iteration Cycle
in Materials Development
First IBM PC
invented
Lithium ion
Teflon
S. Whittingham
Velcro
Titanium
Polycarbonate
Diamond-like Thin
Films
Human Genome
Mapped
Sony
Gerbrand Ceder
Massachusetts Institute of Technology
GaAs
Amorphous soft
magnets
MGI Meeting Georgia Tech, June 5 2014
At all levels in the materials development process
there is significant iteration
Predictive modeling and quantification can reduce time
Iterations are too long and there are too many iterations !
Modeling may reduce iterations and shorten them
Knowledge, Methods and Information required to
make rational materials design happen
Basic Science
KNOWLEDGE
INFORMATION
Data/Facts
METHODS
Tools
… but we know nothing
There are about 50,000
to 200,000 known inorganic compounds
Elastic constants: about 200 compounds
Super conductors ≈ 1000
Dielectric constant ≈ 300-400
For almost every property we are below 1% in coverage ….
Imagine …
That one knows for every known compound
•  The bandgap, the effective masses, the Seebeck coefficient
•  The elastic constants, the dielectric constants, the absorption
spectra
•  The point defect energies, solubilities, electronic signature of
defects, mobility of defects and impurities, The stacking fault
energy, the Peierls dislocation barrier
•  Surface energies, in common environments (aqueous, air, …)
That you had the capability to query these properties
for novel compounds upon request …
We know nothing about most materials
…
Example: LiFePO4
•  First synthesized in 1977
•  Only identified as useful battery
cathode in 1997
Example: MgB2
•  First synthesized in 1953
•  Only identified as high temperature
superconductor in 2001
More quantitative materials selection and
higher scale modeling
Materials selection
Inform higher scale models
Automated Quantum Chemistry
Calculations for screening
Molecule
database
Redox Potential
Solubility
Stability
Chemical
motif
design
Reactivity
Isotropic elastic properties
Synthesis
Testing
Feedback
Anisotropic elastic properties
Work by Edwin Garcia (Purdue)
Origin of the Materials Genome idea
Compute the basic properties ( “genes” ) of materials
across the world of compounds and disseminate that
information to the materials community to enable rapid
materials searching and design.
2005-2006: several company funded discovery projects
(Duarcell, Umicore, Bosch, Samsung
The Materials Project: Vision
www.materialsproject.org
Compute the basic properties ( “genes” ) of materials
across the world of compounds and disseminate that
information to the materials community to enable rapid
materials searching and design.
2005-2006: several company funded discovery projects
(Duarcell, Umicore, Bosch, Samsung
Kristin Persson
Anubhav Jain
Daniel Gunter
Maciej Haranszyk
Wei Chen
Gerbrand Ceder
Shyue Ping Ong
Dane Morgan
David Skinner
Shreyas Cholia
Jack Deslippe
Stefano Curtarolo
Alan Dozier
Raphael Fink
Obtain basic compound properties
through scalable ab-initio computations
+
+
Ne
Ne
N
N
1 e e 1
2
H = ∑ ∇i + ∑Vnuclear (ri ) + ∑∑
2 i j≠i rj − ri
i=1
i=1
Thought provoking: NERSC flagship
Hopper could crunch through ~20k
structures in 1 day…
ICSD
Other experimental databases
User submissions
Input processing & transformations
StructureNotationalLanguage (SNL)
Web apps
Analysis
Workflow
Manager
Post-processing and
error-checking
Supercomputing Resources
Materials API
ICSD
Other experimental databases
User submissions
Input processing & transformations
pymatgen
StructureNotationalLanguage (SNL)
•  Robust materials analysis
Custodian
Web apps
•  Self-healing error recovery
Analysis
Fireworks
•  Smart workflow management
Workflow
Manager
Post-processing and
error-checking
Supercomputing Resources
Materials API
Today’s Status:
!  Over 49,000
compounds"
"
!  Growing monthly"
"
!  Multiple property sets "
!  Multiple tools"
Automatic Import, Analysis and Dissemination
Benchmarking of ~70 reaction energies with
experiments + GGA/GGA+U mixing scheme
1638 measured compound enthalpies
49,000 computed compounds
20,000 low –T Phase Diagrams > 50,000 reaction enthalpies
Kristin Persson <kapersson@lbl.gov>
[Matgen] Pourbaix Diagrams on the Materials Project
> 70,000 Pourbaix Diagrams Available
4 messages
support@materialsproject.org <support@materialsproject.org>
Reply-To: matgen@nersc.gov
To: matgen@nersc.gov
Wed, Oct 2, 2013 at 2:41 PM
A few hundred
experimental Gibbs free
energies of aqueous ions
combined with 49,000
calculated solid materials
Calculated
3
Pourbaix Diagrams on the Materials Project
Potential vs. SHE (V)
2H O --->
2
CALC.
MnO4
2 Mn+++
O +4H +4
e
Today, we are excited to announce the release
of the
Pourbaix
diagram
MnO
1
2
-app. Pourbaix diagrams are solid-aqueous phase diagrams as a function of MnO4
2H O+2e
---> H +2
MnOOH
pH, standard hydrogen potential and composition
that
can
be used to
OH
0
2
+
2
-
-
-
2
Mn
++
-
HMnO2
Mn(OH)2
-1
-2
Mn
-3
-2
1 of 4
0
2
4
8
6
pH at 25 °C
10
12
14
16
10/3/13 6:48 PM
Several thousand bandstructures
Lots of stuff in the works …
•  Elastic constants
•  Surfaces
•  Point Defects
•  Finite Temperature properties (phonon based)
•  Thermal expansion
•  Thermal Conductivity
•  Seebeck Coefficient
•  …
•  Design environment planned
Data available through web interface or
through API
An open platform for accessing LOTS of Materials Project data
Identifier
(formula, id,
etc)
Preamble
Property
https://www.materialsproject.org/rest/v1/materials/Fe2O3/vasp/energy
Request
type
API keys available at
https://www.materialsproject.org/profile
Data type (vasp,
exp, etc.)
Build it and They
will Come ?
www.materialsproject.org
!  Launched online Oct 2011
!  Grants: 2012 DOE Center, JCESR, U Madison NSF
Center, JCAP
!  Users: > 6600 registered; 100-300 per day; 20+
publications last year from users
!  Course-ware : UC San Diego, UC Davis, UC
Irvine, U Michigan, John-Hopkins, Cornell, …
!  Partner institutions: UC Berkeley, UC San
Diego, MIT, Duke, U Wisconsin, U Kentucky, U
Louvain Belgium, Cambridge UK, Caltech, …
!  Companies: Toyota, Sony, Bosch, 3M, Honda,
Samsung, LG Chem, Dow Chemicals, GE Global
Research, Intermolecular, Applied Materials, Energizer,
Advanced Materials, General Motors, Corning, DuPont,
Nippon Steel, L’Oreal USA, Caterpillar, HP, Unilever,
Lockheed Martin, Texas Instruments, Ford, Bose, SigmaAldrich, Siemens, Raytheon, Umicore, Seagate, …
JCESR: Beyond Lithium Ion
CROSSCUTING
SCIENCE
MATERIALS
GENOME
Multivalent Intercalation
Chemical Transformation
Non-Aqueous Redox Flow
Systems
Analysis
and Translation
Cell Design
and
Prototyping
Commercial
Deployment
TECHNOECONOMIC
MODELING
EDL
DISTINGUISHING TOOLS
END-TO-END INTEGRATION
21
The Crosscutting ‘Electrolyte Genome’
Electrolyte Genome
A design platform for novel electrolytes and redox-active molecules
Computational structural/chemical/property data for 104-105 solvents, salts, and redox
molecules; organized for interactive searching and design
Prioritized target property list for each thrust
Multivalent
Intercalation
Chemical
Transformation
Non-Aqueous
Redox Flow
Recent Collaboration with JCAP
May contain trade secrets or commercial or financial information that is privileged or confidential and exempt from public disclosure.
JCESR Sandbox: The Electrolyte Genome
May contain trade secrets or commercial or financial information that is privileged or confidential and exempt from public disclosure.
Other collaborations
•  Nanoporous materials center (Minnesota)
•  Functionals for accurate bandgaps (Jacobsen
Denmark)
•  CO2 capture materials (Cambridge U)
•  Thermoelectrics (Caltech)
•  Mg alloy corrosion (Volkswagen)
•  Interfaces (Intermolecular) – negotiation in process
•  Advanced Battery Materials (Samsung)
May contain trade secrets or commercial or financial information that is privileged or confidential and exempt from public disclosure.
Some important factors
May contain trade secrets or commercial or financial information that is privileged or confidential and exempt from public disclosure.
Experimental data leverages computed data
Experimental data not just for “checking”
Combination of experiment and computed data is highly
synergistic. The two leverage each other
Experimental formation energies of used to correct
some of the worst errors of DFT
L. Wang et al. Physical
Review B, 73, 195107 (2006).
Using experimental enthalpy of formation – the Us are determined for every TM
to make sure that the enthalpy of reactions between binaries are reproduced
Our solution: Different approaches in different chemical
domains. Connect through reference states
Elemental
metals
GGA
METALS
reference
reactions
Binary
Oxides
OXIDES
GGA + U
A. Jain, Formation Enthalpies by Mixing GGA and GGA+U
Calculations, Physical Review B, 84 (4), 045115 (2011).
Stability in Aqueous Media?
Cu+: http://www.ccp1.ac.uk/
newsletters/Current/Sprik/
CarParrinello.html
+
20,000 low –T phase diagrams
From first-principles ?
◦  Computationally demanding
~◦  300
aqueous
2+, Na+, Mgion
2+, Al3+, K+,
Li+, Be
2+,Cu2+, Ag+, OH-, Al3+,
Ca2+, Fe
Gibbs
free
energies
and SO3- …
Dissolution of solids in water: Combining experimental and
computed energies
reference reactions
Element or simple
compound
Solid world
GGA/GGA + U
Dissolved
ion
Aqueous
solutions
Experimental
free energies
K. Persson et al, Physical Review B, 85, 235438 (2012).
Kristin Persson <kapersson@lbl.gov>
[Matgen] Pourbaix Diagrams on the Materials Project
> 70,000 Pourbaix Diagrams Available
4 messages
support@materialsproject.org <support@materialsproject.org>
Reply-To: matgen@nersc.gov
To: matgen@nersc.gov
Wed, Oct 2, 2013 at 2:41 PM
A few hundred
experimental Gibbs free
energies of aqueous ions
combined with 49,000
calculated solid materials
Calculated
3
Pourbaix Diagrams on the Materials Project
Potential vs. SHE (V)
2H O --->
2
CALC.
MnO4
2 Mn+++
O +4H +4
e
Today, we are excited to announce the release
of the
Pourbaix
diagram
MnO
1
2
-app. Pourbaix diagrams are solid-aqueous phase diagrams as a function of MnO4
2H O+2e
---> H +2
MnOOH
pH, standard hydrogen potential and composition
that
can
be used to
OH
0
2
+
2
-
-
-
2
Mn
++
-
HMnO2
Mn(OH)2
-1
-2
Mn
-3
-2
1 of 4
0
2
4
8
6
pH at 25 °C
10
12
14
16
10/3/13 6:48 PM
Software Management Philosophies

Open-source
◦  More eyes => robustness
◦  Contributions from all over the
world

Benevolent dictators
◦  Unified vision
◦  Quality control

Clear documentation
◦  Prevent code rot
◦  More users

•  Pymatgen
•  Custodian
Test, test, test
◦  Continuous integration to
ensure code is always working
•  Fireworks
Python Materials Genomics
is the core analysis code
powering the Materials Project
v2.7.0  v2.9.9
www.pymatgen.org stats
•  Steady increase over the past year
•  > 1000 views per month on average
Major new features /
functionality
•  Support for ABINIT 7.6.1
(ABINIT group/UCL)
•  Defects (Haranczyk/LBNL)
•  Qchem (JCESR)
•  Robust units handling
(UCSD/UCL)
•  XRD pattern simulation
(UCSD)
Major new users / fans
> 500 commits over the last year.
Pymatgen coders work Mon-Wed.
# of active contributors has more than doubled!
Developers and users come from > 30 countries spread
across all continents (except Antarctica!).
MGI – Broadening its impact
Recommendations: Data as Infrastructure
•  Data becomes part of infrastructure.
•  Is it time for a user facility to support data
production and management for materials science
and engineering ? (i.e. not just computing). Similar
to the protein databank ?
•  Is the materials community convinced of this need ?
•  Requirements for data preservation and storage will
come your way. Unless we act proactively we may
not like what will be imposed on us
Recommendations: Data and Code houses
•  Compute: Center for high-throughput
computed data and curation.
Compute all that can be computed. Make highthroughput routine. When a new property method becomes
available, scale it !
Agencies (NSF, DOE, …) may have to provide more
commodity computing (i.e. not exascale)
•  Experimental Data:
need a place to dump it. Use DOI and minimal descriptors.
Don’t try to “catalogue”
•  Software:
For problems where the physics is well established,
commodity code is needed. Can communities pick a few of
these topics ?
Even Google can not find most experimental data
Science, Jan, 2014
Smaller particle size in LMCO/C leads to higher capacity than LMCO
Recommendations: Learn best practice for MGI driven
Materials Design by setting up field specific teams
•  Create teams of experimentalists/theory + MGI
efforts to demonstrate how data can be used in real
materials design. Do this in different fields
•  Feedback from design exercises to MGI effort is
valuable. To design materials, field-specific
expertise is needed
•  E.g. team on thermoelectrics, carbon capture, light
weight alloys, etc.
Recommendations: Education
•  Complexity of Materials Science requires
computation and modeling. Yin and Yang
•  Traditional MS&E is not used to having lots of
quantitative information
•  Continue to educate our young scientists and
workforce.
•  Engage the educational institutions more in MGI.
They will only do this if MGI is reasonably permanent
Charting the Materials Genome is Possible
• Within ten years most basic properties of inorganic compounds can
be determined computationally
• Materials researchers will have access to all this data. What you
do with it will determine your competitive advantage
• Researchers will be able to sit behind a terminal and request the
computed properties of modified compounds
Voltage$(V)$
2.5
3
3.5
4
4.5
5
5.5
0
500
Eq.$Oxygen$Release$µO2,
Charged$State$(eV)
'1
1000
'1.5
1500
'2
2000
'2.5
2500
'3
3000
'3.5
Eq.$Oxygen$Release$Temp.,
Charged$State$(eV)
2
0
'0.5
3500
'4
4000
'4.5
'5
oxides
phosphates
silicates
borates
sulfates
4500
… towards a
materials genome
Let’s set pragmatic goals, not wish lists
Thanks to
Wei Chen, Geoffroy Hautier, Anubhav Jain, Shyue Ping Ong, Kristin Persson
Kristin Persson
Anubhav Jain
Daniel Gunter
Maciej Haranszyk
Wei Chen
Gerbrand Ceder
Shyue Ping Ong
Dane Morgan
David Skinner
Shreyas Cholia
Jack Deslippe
Stefano Curtarolo
Alan Dozier
Raphael Fink