The Materials Project and computational materials discovery

Anubhav Jain
The Materials Project and
computational materials discovery
DREAMS, May 2015
Lawrence Berkeley Lab
Berkeley, CA

The Materials Design Challenge
  High-Throughput density functional theory +
new battery materials
  The Materials Project
  Concluding thoughts

cost/effort to
implement+deploy
new technology
cost/benefit
to maintain new
technology
cost/benefit
to end user
of today’s
technology)
STAGE 1 STAGE 2 STAGE 3
carbon capture/storage energy efficiency retrofits
electric vehicles today
SolarCity solar panels
hybrid electric vehicles

resource constraints over time
policy / carbon tax
better manufacturing
reduce labor/installation cost
policy / incentives / rebates
new business models (“leasing”)
performance engineering
materials optimization
materials discovery
new inventions
Many ways to bring solutions from Stage 1 to Stage 3!

¡  Alternative materials could make a big dent in
sustainability, scalability, and cost
¡  But it’s hard! In most of these applications, we’ve been
re-using the same fundamental materials for decades
§  solar power w/Si since 1950s
§  graphite/LCO (basis of today’s Li battery electrodes) since
1990
¡  Why is designing brand new materials such a
challenge?

¡  Bag of 30 atoms
¡  One of 50 elements at each
site
¡  Arrange on 10x10x10
lattice
¡  Over 10108 possibilities!
§  more than grains of sand on
all beaches (1021)
§  more than number of atoms in
universe (1080)

Hunts Needle in a Haystack
How long does it take to find a
needle in a haystack? Jim
Moran, Washington, D.C.,
publicity man, recently dropped
a needle into a convenient pile
of hay, hopped in after it, and
began an intensive search for (a)
some publicity and (b) the
needle. Having found the former,
Moran abandoned the needle
hunt.

We need new ideas for
accelerating materials discovery

+ )};({
)};({
trH
dt
trd
i i
i
Ψ=
Ψ ∧
!
+
Total energy
Optimized structure
Magnetic ground state
Charge density
Band structure / DOS
H = ∇i
2
i=1
Ne
∑ + Vnuclear (ri)
i=1
Ne
∑ + Veffective(ri)
i=1
Ne
∑

relative
computing
power
types of materials computations predict
materials?
1980s 1 simple metals/
semiconductors
unimaginable by majority
1990s 1000 + oxides ~few examples
2000s 1,000,000 + complex/
correlated systems
~dozen examples**
2010s 1,000,000,000* +hybrid systems
+excited state
properties?
+AIMD
hard to keep track,
~hundreds by end of
decade?
2020s ?1 trillion? 10,000 atoms? ?routine?
* The top 2 DOE supercomputers alone have a budget of 8 billion CPU-hours/year, in theory enough to run basic
DFT characterization (structure/charge/band structure) of ~40 million materials/year!
**G. Hautier, A. Jain, and S. P. Ong, J. Mater. Sci., 2012, 47, 7317–7340. 15

Application Researcher Search space Candidates Hit rate
Scintillators Klintenberg et al. 22,000 136 1/160
Curtarolo et al. 11,893 ? ?
Topological insulators Klintenberg et al. 60,000 17 1/3500
Curtarolo et al. 15,000 28 1/535
High TC superconductors Klintenberg et al. 60,000 139 1/430
Thermoelectrics – ICSD
- Half Heusler systems
- Half Heusler best ZT
Curtarolo et al. 2,500
80,000
80,000
20
75
18
1/125
1/1055
1/4400
1-photon water splitting Jacobsen et al. 19,000 20 1/950
2-photon water splitting Jacobsen et al. 19,000 12 1/1585
Transparent shields Jacobsen et al. 19,000 8 1/2375
Hg adsorbers Bligaard et al. 5,581 14 1/400
HER catalysts Greeley et al. 756 1 1/756*
Li ion battery cathodes Ceder et al. 20,000 4 1/5000*
Entries marked with * have experimentally verified the candidates.
Hit rates are optimistic because the search space is usually pre-restricted based on intuition.
See also Curtarolo et al., Nature Materials 12 (2013) 191–201.

anode electrolyte cathode
Li+ discharge
e- discharge
e.g.
graphitic carbon
e.g.
LiPF6 / (EC/DMC)
e.g.
LiCoO2
LiFePO4
Li+ charge
e- charge

The cathode material must quickly
absorb and release large
quantities of Li without degrading
It must be cost-effective and safe
It should be light, compact, and
highly absorbent (high voltage)

Lia Mb (XYc)d
Li ion
source
electron
donor /
acceptor
structural
framework /
charge neutrality
examples:
V4+/5+,Fe2+/3+
examples:
O2-, (PO4)3-, (SiO4)4-
common cathodes: LiCoO2, LiMn2O4, LiFePO4

Property Ease
(1=automatic, 2=weeks,
3=months)
Voltage (average) 1
Volume change / topotactic 1
Thermodynamic stability 1
O2 chemical potential 1
Bulk diffusion barriers 2, maybe 1.5 soon
Defect properties 2
Surfaces/Interfaces 3
21

Hexagonal phase
low Li 529 meV
high Li 723 meV
monoclinic phase
low Li 395 meV
high Li 509 meV
•  525 meV means a micron-sized
particle can be charged in 2 hours
•  Every 60 meV difference represents
a10X difference in diffusion coefficient
Kim, Moore, Kang,
Hautier, Jain, Ceder
J ECS (2011)
LiMnBO3

Plain Oxides
(9204)
Silicates (1857)
Phosphates (1609)
Borates (1035)
Carbonates (370)
Vanadates (1488)
Sulfates (330)
Nitrates(61)
No Oxygen (4153)
LiContainingCompoundsComputed
Jain, Hautier, Moore,
Ong, Fischer,
Mueller, Persson,
Ceder
Comp. Mat. Sci (2011)

Chemistry Novelty Energy density
vs. LiFePO4
% of theoretical capacity
already achieved in the lab
Li9V3(P2O7)3(PO4)2 New 20% greater ~65%
Origin:
V to Fe substitution in Li9Fe3(P2O7)3(PO4)2*
Remarks:
•  Structure has “layers” and “tunnels”
•  Pyrophosphate-phosphate mixture
•  Potential 2-electron material
Jain, Hautier, Moore, Kang, Lee,
Chen, Twu, and Ceder
Journal of The Electrochemical Society
159, A622–A633 (2012).
C/35 at RT
2.0mg
3.0V – 4.7V

Structure type and metal act largely
independently to create voltage
Structure effect is largely electrostatic
Redox couple + polyanion sets the
range; inductive effect raises V
Hautier, Jain,
Ong, Kang,
Moore, Doe,
and Ceder,
Chem. Mater.,
2011, 23,
3495–3508.
Jain, Hautier, Ong, Dacek, Ceder PCCP (2015)
25

Jain, Hautier, Ong, Dacek, Ceder PCCP (2015)
*Ong, Jain, Hautier, Kang, and Ceder,
Electrochem. Commun., 2010, 12, 427–430.
•  High voltage materials are less
safe
-  For a given voltage, polyanions
are safer than oxides
-  Condensed polyanions have even
higher safety
•  d5 electron configuration can give
higher safety*
•  In general, a tradeoff between
•  voltage
•  safety
•  capacity

*Cheng, Assary, Qu, Jain, Ong, Rajput,
Persson, Curtiss JPCL (2014)

redox flow active
molecule candidates
*Cheng, Assary, Qu, Jain, Ong, Rajput,
Persson, Curtiss JPCL (2014)

¡  Thermoelectrics are devices to convert waste heat to electricity
§  they can be operated in “reverse” to provide refrigeration
¡  Need new, abundant materials that possess a high “figure of merit”,
or zT, for high efficiency

ZT = α2σT/κ
power factor
>2 mW/mK2
(PbTe=10 mW/mK2)
Seebeck coefficient
> ~100 μV/K
Band structure + Boltztrap
electrical conductivity
> 103 /(ohm-cm)
Band structure + Boltztrap
thermal conductivity
< ~10 W/(m*K)
•  κe from Boltztrap
•  κl difficult (phonon-phonon
scattering)
Note: Boltztrap assumes certain regimes, e.g.
constant scattering time/acoustic phonon
scattering

Zhu, Hautier, Aydemir,
Gibbs, Li, Bajaj, Pohls,
Broberg, Chen, Jain,
White, Asta, Persson,
Ceder
submitted
TmAgTe2
Energy(eV)
!
!
Wave vector k
(a)Te
Ag Tm
3
2
1
0
-1
-2
-3Γ Σ M K Λ Γ A L H A|LM|K 0 4 8
PF mW/(mK2)
!
!
!!!!!Wave!vector!k!
!!
(b)
Energy(eV)
Wave vector k
3
2
1
0
-1
-2
-3
Γ X M Γ Z R A Z|XR|M 0 4 8
PF mW/(mK2)

zT~0.4 measured;
zT=1.8 possible if
doping can be
achieved
Zhu, Hautier, Aydemir,
Gibbs, Li, Bajaj, Pohls,
Broberg, Chen, Jain,
White, Asta, Persson,
Ceder
submitted

¡  A more practical composition with similar
performance can be achieved
TbAgS2
DyAgS2
TmAgS2
ErAgS2
HoAgS2
LuAgS2
ScAgS2 SmAgSe2
PrAgTe2
TbAgSe2
ErAgSe2
LuAgSe2
DyAgSe2
CrAgS2
LuCuTe2TmCuTe2ScAgSe2
NdAgTe2
YAgSe2
HoAgSe2
TmAgSe2
Sm,Dy,Tm,
Er,Ho,Tb,
Lu,YAgTe2
YAgS2
(a)
MaximumtheoreticalzT
4
3
2
1
0 0.00 0.01 0.02 0.03 0.04 0.05
Decomposition energy (eV)
S
Se
Te
(b)
ScAgSe2
L
Tm,Lu,Er
Y,Dy,TbA
TmC
MaximumtheoreticalzT
(a) 4
3
2
1
0
Deco
0.00 0
(b)

quick assessment of 9000 thermoelectric compositions

Jain*, Ong*, Hautier, Chen, Richards, Dacek, Cholia, Gunter, Skinner, Ceder, and
Persson, APL Mater., 2013, 1, 011002. *equal contributions

Compounds
Total
Energies
Optimized
Structures
Band
Structures
Elastic
Tensor
Defects
today ~60,000 ✔ ✔ ~40,000 ~1000
~100
(soon)
near –
term
~60,000 ✔ ✔ ✔ >5000 >500
medium –
term
90,000 +
(all of ICSD
plus many
predictions)
✔ ✔ ✔
common
compounds
common
compounds

¡  pymatgen (www.pymatgen.org)
¡  FireWorks (http://pythonhosted.org/FireWorks)
¡  others at www.github.com/materialsproject

K. He, Y. Zhou, P. Gao, L. Wang, N. Pereira, G.G. Amatucci, et al.,
Sodiation via Heterogeneous Disproportionation in FeF2 Electrodes for
Sodium-Ion Batteries., ACS Nano. 8 (2014) 7251–9.
M.M. Doeff, J. Cabana, M. Shirpour, Titanate Anodes for Sodium Ion
Batteries, J. Inorg. Organomet. Polym. Mater. 24 (2013) 5–14.
learn to use these: hackingmaterials.com/pdcomic

¡  Video tutorials at:
§  www.youtube.com/user/MaterialsProject
¡  or go to www.materialsproject.org and click
Tutorials link

Where is the
Materials Project
headed in the
future?

de Jong, Chen, Angsten, Jain,
Notestine, Gamst, Sluiter, Ande,
van der Swaag, Curtarolo, Toher,
Plata, Ceder, Persson & Asta
in submission
KVRH – bulk modulus
GVRH – shear modulus
color = Poisson’s ratio
dashed lines = Pugh number
(correlates with ducility)
arrow orientation
high atom density
(low volume/atom)
intermediate atom density
(intermediate volume/atom)
low atom density
(high volume/atom)
45

beta version online
through Xtal Toolkit
app

“I need data on
compound X”
SUBMIT

“I have this
great dataset,
but need help
sharing it with
the world”
Your
Materials
Data

beta test?
email ajain@lbl.gov

¡  High-throughput and DFT-based materials design is
now a viable technique for finding new materials
¡  But the computer models are by no means complete!
§  missing insight into higher length and time scales,
nanostructuring, surface phenomena, etc.
§  issues with accuracy, especially for excited-state
properties
§  These can be important!
¡  However, within the universe of DFT screening, could
we do even better?

http://xkcd.com/1002/
NASA antenna design
http://en.wikipedia.org/wiki/Evolved_antenna
this antenna is the product of a radiation
model+genetic algorithm solver. It was
better than human designs and launched
into space.

¡  Computers can be like a
“gifted child”
¡  Already used for structure
prediction / solution
¡  At some point it may be
better to program models
into computers and let them
(mostly) solve them

¡  Computers can be like a
“gifted child”
¡  Already used for structure
prediction / solution
¡  At some point it may be
better to program models
into computers and let them
(mostly) solve them
basic compound design - here?
or will it stay here forever?

¡  Band gap > 1.5
¡  Band edges
straddle H+/H2
and O2/H2O
potentials
¡  Stability
§  thermodynamic
§  aqueous
§  under illumination
Castelli, Olsen, Datta, Landis, Dahl, Thygesen, Jacobsen
Energy & Environmental Science (2011)

A B X3
52
metals
52
metals
7 mixtures
{O, N, F, S}
examples: SnTiO3, SrGeO3
(about 19,000 total compounds!)

Jain, Castelli, Hautier, Bailey,
Jacobsen
J. Materials Science (2013)

Results of
high-
throughput
computation
Clustering, Regression, Feature
extraction, Model-building, etc.
Well developed, powerful
data-mining routines
Need frameworks for connection/translation
into meaningful descriptors

¡  Dr. Kristin Persson and Prof. Gerbrand Ceder,
founders of Materials Project and their teams
¡  Prof. Shyue Ping Ong
¡  Prof. Geoffroy Hautier
¡  Prof. Jeffrey Snyder + team (thermoelectrics)
¡  Prof. Mary Anne White + team (thermoelectrics)
¡  Prof. Mark Asta and team (elastic tensor/TEs)
¡  Prof. Karsten Jacobsen + team (perovskite GA)
¡  NERSC computing center and staff
¡  Funding: DOE, LBL LDRD, Bosch, Umicore

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