The Materials Project and computational materials discovery

transcript

Anubhav Jain

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/efforttoimplement+deploynewtechnology

cost/benefittomaintainnewtechnology

cost/benefittoenduseroftoday’stechnology)

STAGE 1 STAGE 2 STAGE 3

carboncapture/storage energyefficiencyretrofitselectricvehiclestoday

SolarCitysolarpanelshybridelectricvehicles

resourceconstraintsovertimepolicy/carbontax

bettermanufacturingreducelabor/installationcostpolicy/incentives/rebatesnewbusinessmodels(“leasing”)

performanceengineeringmaterialsoptimizationmaterialsdiscoverynewinventions

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

¡  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

+ )};({)};({ trHdt

trdi ii Ψ=

Ψ ∧

Total energy Optimized structure Magnetic ground state Charge density Band structure / DOS

H = ∇i2

∑ + Vnuclear (ri)i=1

∑ + Veffective(ri)i=1

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

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.

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

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

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)

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)

*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)

Ongoing work: thermoelectrics

¡  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

!! Wave vector k

(a) Te

Γ Σ M K Λ Γ A L H A|LM|K 0 4 8 PF mW/(mK2)

!!!!!Wave!vector!k!!!

Wave vector k

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 LuCuTe2 TmCuTe2 ScAgSe2

NdAgTe2 YAgSe2 HoAgSe2

TmAgSe2

Sm,Dy,Tm, Er,Ho,Tb, Lu,YAgTe2

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

TmAuSe2

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

(a) S Se Te

4 3 2 1 0

ScAgSe2 ScAgS2

ScAgTe2

(b) 4 3 2 1 0

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 ✔ ✔ ✔

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)

betaversiononlinethroughXtalToolkitapp

“I need data on compound X”

SUBMIT

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

YourMaterialsData

betatest?emailajain@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/

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.

¡  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

basiccompounddesign-here?

orwillitstayhereforever?

¡  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,Featureextraction,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