the materials project and computational materials discovery
TRANSCRIPT
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Anubhav Jain
The Materials Project and computational materials discovery
DREAMS, May 2015
Lawrence Berkeley Lab Berkeley, CA
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The Materials Design Challenge
High-Throughput density functional theory + new battery materials
The Materials Project
Concluding thoughts
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cost/efforttoimplement+deploynewtechnology
cost/benefittomaintainnewtechnology
cost/benefittoenduseroftoday’stechnology)
STAGE 1 STAGE 2 STAGE 3
carboncapture/storage energyefficiencyretrofitselectricvehiclestoday
SolarCitysolarpanelshybridelectricvehicles
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resourceconstraintsovertimepolicy/carbontax
bettermanufacturingreducelabor/installationcostpolicy/incentives/rebatesnewbusinessmodels(“leasing”)
performanceengineeringmaterialsoptimizationmaterialsdiscoverynewinventions
Many ways to bring solutions from Stage 1 to Stage 3!
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¡ 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?
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¡ 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)
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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.
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We need new ideas for accelerating materials discovery
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The Materials Design Challenge
High-Throughput density functional theory + new battery materials
The Materials Project
Concluding thoughts
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+ )};({)};({ trHdt
trdi ii Ψ=
Ψ ∧
!+
Total energy Optimized structure Magnetic ground state Charge density Band structure / DOS
H = ∇i2
i=1
Ne
∑ + Vnuclear (ri)i=1
Ne
∑ + Veffective(ri)i=1
Ne
∑
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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
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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.
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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.
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anode electrolyte cathode
Li+ discharge
e- discharge
e.g. graphitic carbon
e.g. LiPF6 / (EC/DMC)
e.g. LiCoO2 LiFePO4
Li+ charge
e- charge
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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)
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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
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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
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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
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Plain Oxides (9204)
Silicates (1857)
Phosphates (1609)
Borates (1035)
Carbonates (370)
Vanadates (1488)
Sulfates (330)
Nitrates(61)
No Oxygen (4153)
Li C
onta
inin
g C
ompo
unds
Com
pute
d
Jain, Hautier, Moore, Ong, Fischer, Mueller, Persson, Ceder Comp. Mat. Sci (2011)
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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
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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
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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
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*Cheng, Assary, Qu, Jain, Ong, Rajput, Persson, Curtiss JPCL (2014)
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redox flow active molecule candidates
*Cheng, Assary, Qu, Jain, Ong, Rajput, Persson, Curtiss JPCL (2014)
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Ongoing work: thermoelectrics
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¡ 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
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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
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Zhu, Hautier, Aydemir, Gibbs, Li, Bajaj, Pohls, Broberg, Chen, Jain, White, Asta, Persson,
Ceder submitted
TmAgTe2
Ener
gy (e
V)
!! Wave vector k
(a) Te
Ag Tm
3
2
1
0
-1
-2
-3
Γ Σ M K Λ Γ A L H A|LM|K 0 4 8 PF mW/(mK2)
!!
!!!!!Wave!vector!k!!!
(b)
Ener
gy (e
V)
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)
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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
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¡ A more practical composition with similar performance can be achieved
TbAgS2
DyAgS2
TmAgS2
ErAgS2
HoAgS2 LuAgS2
ScAgS2 SmAgSe2
PrAgTe2
TbAgSe2 ErAgSe2 LuAgSe2
DyAgSe2
CrAgS2 LuCuTe2 TmCuTe2 ScAgSe2
NdAgTe2 YAgSe2 HoAgSe2
TmAgSe2
Sm,Dy,Tm, Er,Ho,Tb, Lu,YAgTe2
YAgS2
(a)
Max
imum
theo
retic
al zT
4 3 2 1 0
0.00 0.01 0.02 0.03 0.04 0.05
Decomposition energy (eV)
S Se Te
ScCuSe2 LuCuSe2 TmCuSe2
ScCuS2
LuCuS2 YCuSe2
ErCuSe2 LuAuSe2
NdAgTe2 PrAgTe2
(b)
TmAuSe2
Max
imum
theo
retic
al zT
4 3 2 1 0
Decomposition energy (eV) 0.00 0.01 0.02 0.03 0.04 0.05
ErAgSe2
TmAgSe2
Y,DyAgSe2 TbAgSe2
ScAgSe2
ScAgS2
LuAgSe2
Tm,Lu,Er,Ho,Y,Dy,TbAgTe2
HoAgS2 TbAgS2
YAgS2 DyAgS2
ErAgS2
TmAgS2
PrAgTe2
NdAgTe2
TmCuTe2
LuCuTe2 SmAgTe2
HoAgSe2
LuAgS2 SmAgSe2
Max
imum
theo
retic
al zT
(a) S Se Te
4 3 2 1 0
Decomposition energy (eV) 0.00 0.01 0.02 0.03 0.04 0.05
ScAgSe2 ScAgS2
ScAgTe2
Max
imum
theo
retic
al zT
(b) 4 3 2 1 0
Decomposition energy (eV) 0.00 0.01 0.02 0.03 0.04 0.05
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quick assessment of 9000 thermoelectric compositions
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The Materials Design Challenge
High-Throughput density functional theory + new battery materials
The Materials Project
Concluding thoughts
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Jain*, Ong*, Hautier, Chen, Richards, Dacek, Cholia, Gunter, Skinner, Ceder, and Persson, APL Mater., 2013, 1, 011002. *equal contributions
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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
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¡ pymatgen (www.pymatgen.org) ¡ FireWorks (http://pythonhosted.org/FireWorks) ¡ others at www.github.com/materialsproject
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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
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¡ Video tutorials at:
§ www.youtube.com/user/MaterialsProject
¡ or go to www.materialsproject.org and click Tutorials link
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Where is the Materials Project
headed in the future?
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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
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betaversiononlinethroughXtalToolkitapp
“I need data on compound X”
SUBMIT
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“I have this great dataset, but need help sharing it with the world”
YourMaterialsData
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The Materials Design Challenge
High-Throughput density functional theory + new battery materials
The Materials Project
Concluding thoughts
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¡ 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?
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??
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http://xkcd.com/1002/
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http://xkcd.com/1002/
NASAantennadesign
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.
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¡ 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
http://xkcd.com/1002/
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¡ 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
http://xkcd.com/1002/
basiccompounddesign-here?
orwillitstayhereforever?
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¡ 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)
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A B X3 52
metals 52
metals 7 mixtures {O, N, F, S}
examples: SnTiO3, SrGeO3
(about 19,000 total compounds!)
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Jain, Castelli, Hautier, Bailey, Jacobsen J. Materials Science (2013)
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Results of high-throughput computation
Clustering,Regression,Featureextraction,Model-building,etc.
Well developed, powerful data-mining routines
Need frameworks for connection/translation into meaningful descriptors
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¡ 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