Presentation given to Computational Materials at Berkeley club, Oct 16 2018, Berkeley CA

1. Software tools, crystal descriptors, and
machine learning applied to materials design
Anubhav Jain
Energy Technologies Area
Lawrence Berkeley National Laboratory
Berkeley, CA
Presentation to Computational Materials at Berkeley,
Oct 16 2018
Slides (already) posted to hackingmaterials.lbl.gov

2. 2
Materials and their properties decide what is
technologically possible
Electric vehicles and solar power
are two technologies that have
been dreamed about for many
decades, but did not have much
real impact for a long time …
1910
1956

3. 3
Materials and their properties decide what is
technologically possible
Today’s revolution in clean energy technologies are largely
due to advancements in materials –
science, engineering, and manufacturing.
Much else might be possible with better materials …
but, as past examples demonstrate, it can take a long time.

4. What constrains traditional approaches to materials design?
4
“[The Chevrel] discovery resulted from a lot of
unsuccessful experiments of Mg ions insertion
into well-known hosts for Li+ ions insertion, as
well as from the thorough literature analysis
concerning the possibility of divalent ions
intercalation into inorganic materials.”
-Aurbach group, on discovery of Chevrel cathode
for multivalent (e.g., Mg2+) batteries
Levi, Levi, Chasid, Aurbach
J. Electroceramics (2009)

5. Outline
5
① Density functional theory and “high-
throughput” screening of materials
– High-throughput density functional theory
– Searching for new thermoelectrics
② Software to accelerate materials design
– atomate
– matminer
③ Text mining for materials design
– “Materials Scholar”

6. Density functional theory (DFT) models materials properties
from first principles
6
• 1920s: The Schrödinger equation for quantum mechanics essentially contains
all of chemistry embedded within it
• it is almost always too complicated to solve due to the numerous electron
interactions and complexity of the wave function entity
• 1960s: DFT is developed and reframes the problem for ground state
properties of the system to separate interactions and written in terms of the
charge density, not wavefunction
• makes solutions tractable while in principle not sacrificing accuracy for
the ground state!
e–
e– e–
e– e–
e–

7. How does one use DFT to design new materials?
7
A. Jain, Y. Shin, and K. A.
Persson, Nat. Rev. Mater.
1, 15004 (2016).

8. How accurate is density functional theory in practice?
8
Shown are typical DFT results for (i) Li
battery voltages, (ii) electronic band gaps,
and (iii) bulk modulus
(i) (ii)
(iii)
(i) V. L. Chevrier, S. P. Ong, R. Armiento, M. K. Y. Chan, and G. Ceder,
Phys. Rev. B 82, 075122 (2010).
(ii) M. Chan and G. Ceder, Phys. Rev. Lett. 105, 196403 (2010).
(iii) M. De Jong, W. Chen, T. Angsten, A. Jain, R. Notestine, A. Gamst,
M. Sluiter, C. K. Ande, S. Van Der Zwaag, J. J. Plata, C. Toher, S.
Curtarolo, G. Ceder, K.A. Persson, and M. Asta, Sci. Data 2, 150009
(2015).
battery voltages
band gaps
bulk modulus

9. DFT is limited in length + time scale
(addressed by other techniques)
9
Source: NASA

10. High-throughput DFT: a key idea
10
Automate the DFT
procedure
Supercomputing
Power
FireWorks
Software for programming
general computational
workflows that can be
scaled across large
supercomputers.
NERSC
Supercomputing center,
processor count is
~100,000 desktop
machines. Other centers
are also viable.
High-throughput
materials screening
G. Ceder & K.A.
Persson, Scientific
American (2015)

11. • “DFT is too computationally expensive”
– Quote from a critic in ~2008: “It would be much too
expensive to ever apply DFT to the entire ICSD database”
– DFT implementations got faster, computers got much
faster and expensive: people failed to extrapolate where
computing would be in 5-10 years.
– e.g., DOE INCITE program awarded 5 million CPU hours in
2003. It awarded 6 billion CPU-hours in 2018.
– Things rarely get 1000X more powerful in 15 years.
Exponential growth is hard to visualize …
11
Reasons why this wasn’t always just really obvious

12. • “DFT is too computationally expensive”
• “Managing all those calculations would be hard”
– G. Ceder, high-throughput pioneer, ~2006: “If you can build
a system that even just keeps track of where all our
calculations are, I would already be impressed”.
12
Reasons why this wasn’t always just really obvious
Facebook in 2005 was
valued at ~$100 million
for building something
that looked like this …

13. • “DFT is too computationally expensive”
• “Managing all those calculations would be hard”
• “DFT requires a trained researcher to set the
correct parameters, monitor job, & fix errors”
– Standard calculations are actually quite routine. Many
things are in fact better as “automatic” – after all, shouldn’t
ab initio mean no tunable parameters?
– Error correction is also better automated with a big
database of fixes rather than ad-hoc.
– However – expanding the scope of what can be automated
without researcher intervention (e.g., defect calculations,
GW) is still a major research topic.
13
Reasons why this wasn’t always just really obvious

14. Examples of (early) high-throughput studies
14
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.
See also: Curtarolo et al., Nature Materials 12 (2013) 191–201.

15. Computations predict, experiments confirm
15
Sidorenkite-based Li-ion battery
cathodes
YCuTe2 thermoelectrics
Chen, H.; Hao, Q.; Zivkovic, O.; Hautier, G.; Du, L.-S.; Tang,
Y.; Hu, Y.-Y.; Ma, X.; Grey, C. P.; Ceder, G. Sidorenkite
(Na3MnPO4CO3): A New Intercalation Cathode Material
for Na-Ion Batteries, Chem. Mater., 2013
Aydemir, U; Pohls, J-H; Zhu, H; Hautier, G; Bajaj, S; Gibbs,
ZM; Chen, W; Li, G; Broberg, D; White, MA; Asta, M;
Persson, K; Ceder, G; Jain, A; Snyder, GJ. Thermoelectric
Properties of Intrinsically Doped YCuTe2 with CuTe4-based
Layered Structure. J. Mat. Chem C, 2016
More examples here: A. Jain, Y. Shin, and K. A. Persson, Nat. Rev. Mater. 1, 15004 (2016).
Li-M-O CO2 capture compounds
Dunstan, M. T., Jain, A., Liu, W., Ong, S. P., Liu, T., Lee,
J., Persson, K. A., Scott, S. A., Dennis, J. S. & Grey, C. .
Energy and Environmental Science (2016)

16. Outline
16
① Density functional theory and “high-
throughput” screening of materials
– High-throughput density functional theory
– Searching for new thermoelectrics
② Software to accelerate materials design
– atomate
– matminer
③ Text mining for materials design
– “Materials Scholar”

17. Thermoelectric materials convert heat to electricity
• A thermoelectric material
generates a voltage based
on thermal gradient
• Applications
– Heat to electricity
– Refrigeration
• Advantages include:
– Reliability
– Easy to scale to different
sizes (including compact)
17
www.alphabetenergy.com
Alphabet Energy – 25kW generator

18. Thermoelectric figure of merit
18
• Many materials properties are important for thermoelectrics
• Focus is usually on finding materials that possess a high “figure
of merit”, or zT, for high efficiency
• Target: zT at least 1, ideally >2
ZT = α2σT/κ
power factor
>2 mW/mK2
(PbTe=10 mW/mK2)
Seebeck coefficient
> 100 V/K
Band structure + Boltztrap
electrical conductivity
> 103 /(ohm-cm)
Band structure + Boltztrap
thermal conductivity
< 1 W/(m*K)
• e from Boltztrap
• l difficult (phonon-phonon scattering)
Very difficult to balance these properties using intuition
alone!

19. Example: Seebeck and e– conductivity tradeoff
19
Heavy band:
ü Large DOS
(higher Seebeck and more carriers)
✗ Large effective mass
(poor mobility)
Light band:
ü Small effective mass
(improved mobility)
✗ Small DOS
(lower Seebeck, fewer carriers)
Multiple bands, off symmetry:
ü Large DOS with small effective
mass
✗ Difficult to design!
E
k

20. Finding good thermoelectrics is tough –
can computations help?
• Thermoelectric (TE) materials must exhibit properties that are
difficult to obtain simultaneously
• Can theory / computation help? As proposed as early as 2003 by
Blake and Metiu1:
20
“With the cost of computing become relatively inexpensive one can
envisage a time where one runs multiple computer test tube
reactions like these on large Beowulf clusters - as a means of
screening for new TE materials. Certainly it appears that in the
future theory may be a very competent dance partner for what has
previously been a solo experimental effort in searching for ever
better TE materials.”
1. Blake and Metiu. Can theory help in the search for better thermoelectric materials? Chemistry, Physics,
and Materials Science of Thermoelectric Materials: Beyond Bismuth Telluride, 2003 !

21. But screening not trivial – difficult to predict properties
21
Chen, W. et al. Understanding thermoelectric properties from high-
throughput calculations: trends, insights, and comparisons with
experiment. J. Mater. Chem. C 4, 4414–4426 (2016).
zT = σS2/κ
power factor from
constant, fixed relation
time approximation and
GGA band structures
minimum thermal
conductivity from GGA
elastic constants

22. Getting more accurate results is not easy …
• More accurate methods
exist, but they are:
– not automatic
– too computationally
expensive to run on many
compounds
• Developing better
computational models is a
major research effort!
22
1.0E+02
1.0E+03
1.0E+04
1.0E+05
1.0E+06
1.0E+07
1.0E+08
200 300 400 500 600 700 800 900 1000 1100
Mobility(cm2/V*s)
Temperature (K)
expt
AMSET
BoltzTraP
“AMSET” model being developed in
our group for Seebeck coefficient
and mobility

23. In the meantime – work with what we have …
23
All data (~300GB total) is
available for direct download
through the Dryad repository
linked in the following
publication:
F. Ricci, W. Chen, U. Aydemir, G.J. Snyder, G.-M.
Rignanese, A. Jain, et al., An ab initio electronic
transport database for inorganic materials, Sci.
Data. 4 (2017) 170085.

24. New Materials from screening – TmAgTe2 (calcs)
24
Zhu, H.; Hautier, G.; Aydemir, U.; Gibbs, Z. M.; Li, G.; Bajaj, S.; Pöhls, J.-H.; Broberg, D.; Chen, W.; Jain, A.; White, M. A.; Asta,
M.; Snyder, G. J.; Persson, K.; Ceder, G. Computational and experimental investigation of TmAgTe 2 and XYZ 2 compounds, a
new group of thermoelectric materials identified by first-principles high-throughput screening, J. Mater. Chem. C, 2015, 3
• Calculations:
trigonal p-
TmAgTe2 could
have power
factor up to 8
mW/mK2
• requires 1020/cm3
carriers

25. TmAgTe2 (experiments)
25
Zhu, H.; Hautier, G.; Aydemir, U.; Gibbs, Z. M.; Li, G.; Bajaj, S.; Pöhls, J.-H.; Broberg, D.; Chen, W.; Jain, A.; White, M. A.; Asta,
M.; Snyder, G. J.; Persson, K.; Ceder, G. Computational and experimental investigation of TmAgTe 2 and XYZ 2 compounds, a
new group of thermoelectric materials identified by first-principles high-throughput screening, J. Mater. Chem. C, 2015, 3
• Expt: p-zT only 0.35 despite
very low thermal
conductivity (~0.25 W/mK)
• Limitation: carrier
concentration (~1017/cm3)
• likely limited by TmAg
defects, as determined by
followup calculations

26. YCuTe2 – friendlier elements, higher zT (0.75)
26
Aydemir, U.; Pöhls, J.-H.; Zhu, H., Hautier, G.; Bajaj, S.; Gibbs, Z. M.; Chen, W.; Li, G.; Broberg, D.;
Kang, S.D.; White, M. A.; Asta, M.; Ceder, G.; Persson, K.; Jain, A.; Snyder, G. J. YCuTe2: A Member of
a New Class of Thermoelectric Materials with CuTe4-Based Layered Structure. J. Mat Chem C, 2016
experiment
computation
• Calculations: p-YCuTe2
could only reach PF of 0.4
mW/mK2
• SOC inhibits PF
• if thermal conductivity is low
(e.g., 0.4, we get zT ~1)
• Expt: zT ~0.75 – not too far
from calculation limit
• carrier concentration of 1019
• Decent performance, but
unlikely to be improved with
further optimization

27. Thermoelectrics screening: lessons so far
• There are issues in being able to calculate the
information we need:
– existing high-throughput techniques need to be more
accurate without greatly increasing computational cost
– More importantly, computing doping limits is hard and
requires better methods of estimation
• We and others are working to improve the state of
these issues – more ideas are needed!
• We are also exploring new ways of searching for
thermoelectrics based on text mining (later in talk)
27

28. Outline
28
① Density functional theory and “high-
throughput” screening of materials
– High-throughput density functional theory
– Searching for new thermoelectrics
② Software to accelerate materials design
– atomate
– matminer
③ Text mining for materials design
– “Materials Scholar”

29. With HT-DFT, we can generate data rapidly – what to do next?
29
M. de Jong, W. Chen, H.
Geerlings, M. Asta, and K. A.
Persson, Sci. Data, 2015, 2,
150053.!
M. De Jong, W. Chen, T.
Angsten, A. Jain, R. Notestine,
A. Gamst, M. Sluiter, C. K.
Ande, S. Van Der Zwaag, J. J.
Plata, C. Toher, S. Curtarolo,
G. Ceder, K. a Persson, and M.
Asta, Sci. Data, 2015, 2, 150009.!
>7500 elastic
tensors
>1000
piezoelectric
tensors
>48000
Seebeck
coefficients +
cRTA transport
Ricci, Chen, Aydemir, Snyder,
Rignanese, Jain, & Hautier (in
submission)!

30. With HT-DFT, we can generate data rapidly – what to do next?
30
M. de Jong, W. Chen, H.
Geerlings, M. Asta, and K. A.
Persson, Sci. Data, 2015, 2,
150053.!
M. De Jong, W. Chen, T.
Angsten, A. Jain, R. Notestine,
A. Gamst, M. Sluiter, C. K.
Ande, S. Van Der Zwaag, J. J.
Plata, C. Toher, S. Curtarolo,
G. Ceder, K. a Persson, and M.
Asta, Sci. Data, 2015, 2, 150009.!
>7500 elastic
tensors
>1000
piezoelectric
tensors
>48000
Seebeck
coefficients +
cRTA transport
Ricci, Chen, Aydemir, Snyder,
Rignanese, Jain, & Hautier (in
submission)!
Goal: make it easy to
generate comparable
data sets on your own

31. A “black-box” view of performing a calculation
31
“something”!
Results!!
researcher!
What is the
GGA-PBE elastic
tensor of GaAs?

32. Unfortunately, the inside of the “black box”
is usually tedious and “low-level”
32
lots of tedious,
low-level work…!
Results!!
researcher!
What is the
GGA-PBE elastic
tensor of GaAs?
Input file flags
SLURM format
how to fix ZPOTRF?
q set up the structure coordinates
q write input files, double-check all
the flags
q copy to supercomputer
q submit job to queue
q deal with supercomputer
headaches
q monitor job
q fix error jobs, resubmit to queue,
wait again
q repeat process for subsequent
calculations in workflow
q parse output files to obtain results
q copy and organize results, e.g., into
Excel

33. What would be a better way?
33
“something”!
Results!!
researcher!
What is the
GGA-PBE elastic
tensor of GaAs?

34. What would be a better way?
34
Results!!
researcher!
What is the
GGA-PBE elastic
tensor of GaAs?
Workflows to run!
q band structure!
q surface energies!
ü elastic tensor!
q Raman spectrum!
q QH thermal expansion!

35. Ideally the method should scale to millions of calculations
35
Results!!
researcher!
Start with all binary
oxides, replace O->S,
run several different
properties
Workflows to run!
ü band structure!
ü surface energies!
ü elastic tensor!
q Raman spectrum!
q QH thermal expansion!
q spin-orbit coupling!

36. Atomate tries make it easy, automatic, and flexible to
generate data with existing simulation packages
36
Results!!
researcher!
Run many different
properties of many
different materials!

37. Each simulation procedure translates high-level instructions
into a series of low-level tasks
37
quickly and automatically translate high-level (minimal)
specifications into well-defined FireWorks workflows
What is the
GGA-PBE elastic
tensor of GaAs?
M. De Jong, W. Chen, T. Angsten, A. Jain, R. Notestine, A. Gamst, et al.,
Charting the complete elastic properties of inorganic crystalline compounds,
Sci. Data. 2 (2015).

38. Atomate contains a library of simulation procedures
38
VASP-based
• band structure
• spin-orbit coupling
• hybrid functional
calcs
• elastic tensor
• piezoelectric tensor
• Raman spectra
• NEB
• GIBBS method
• QH thermal
expansion
• AIMD
• ferroelectric
• surface adsorption
• work functions
Other
• BoltzTraP
• FEFF method
• Q-Chem
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.

39. 39
Full operation diagram
job 1
job 2
job 3 job 4
structure! workflow! database of
all workflows!
automatically submit + execute!output files + database!

40. Atomate thus encodes and standardizes knowledge about
running various kinds of simulations from domain experts
40
K. Mathew J. Montoya S. Dwaraknath A. Faghaninia
All past and present knowledge, from everyone in the group,
everyone previously in the group, and our collaborators, about
how to run calculations
M. Aykol
S.P. Ong
B. Bocklund T. Smidt
H. Tang I.H. Chu M. Horton J. Dagdalen B. Wood
Z.K. Liu J. Neaton K. Persson A. Jain
+

41. Atomate now powers the Materials Project
• Online resource of density
functional theory simulation data
for ~85,000 inorganic materials
• Includes band structures, elastic
tensors, piezoelectric tensors,
battery properties and more
• >60,000 registered users
• Free
• www.materialsproject.org
41
Jain et al. Commentary: The Materials Project: A
materials genome approach to accelerating
materials innovation. APL Mater. 1, 11002 (2013).!

42. 42
Getting started with atomate
Mathew, K. et al. Atomate: A high-
level interface to generate, execute,
and analyze computational
materials science workflows.
Comput. Mater. Sci. 139, 140–152
(2017).!
hackingmaterials.github.io
/atomate!
https://groups.google.com/
forum/#!forum/atomate!
Paper Docs Support

43. Outline
43
① Density functional theory and “high-
throughput” screening of materials
– High-throughput density functional theory
– Searching for new thermoelectrics
② Software to accelerate materials design
– atomate
– matminer
③ Text mining for materials design
– “Materials Scholar”

44. 44
Bottom-up vs top-down approach
Small number of
general principles
Large number of
specific cases
• Conventional theory starts
with a small number of
principles and keeps
extending / simplifying to
tackle more and more cases
(growing the theory)
• Data mining starts from a
*very* large space of
possible models and
removes ones that are
inconsistent with the data
(“trimming” the theories)

45. 45
What is needed to do machine learning on materials?
How can we represent
chemistry and structure as
vectors?
How do we get
enough output
data for training?

46. Matminer connects materials data with data mining
algorithms and data visualization libraries
46
Ward, L. et al. Matminer: An open source toolkit for materials data mining. Comput. Mater. Sci. 152, 60–69 (2018).

47. >40 featurizer classes can
generate thousands of potential
descriptors that are described in
the literature
47
Matminer contains a library of descriptors for various
materials science entities
feat = EwaldEnergy([options])
y = feat.featurize([input_data])
• compatible with scikit-
learn pipelining
• automatically deploy
multiprocessing to
parallelize over data
• include citations to
methodology papers

48. >40 featurizer classes can
generate thousands of potential
descriptors that are described in
the literature
48
Matminer contains a library of descriptors for various
materials science entities
feat = EwaldEnergy([options])
y = feat.featurize([input_data])
• compatible with scikit-
learn pipelining
• automatically deploy
multiprocessing to
parallelize over data
• include citations to
methodology papers

49. 49
The crystal structure is a core entity that
machine learning algorithms should know about
Step 1. Describe each site as a
fingerprint telling you similar it is
to each of 22 known local
environments (e.g., tetrahedral,
octahedral, etc.)
• site à vector
Step 2: Describe each structure as
the average of its site fingerprints
(other site stats can be added)
• structure à vector
tetrahedron
octahedron
distorted 8-coordinated cube

50. Defining local order parameters for various environments
50
Use a given local order parameter
with a threshold
for motif recognition:
If qtet > qthresh,
then motif is tetrahedron.
Else
not (too much) a tetrahedron.
Tetrahedral order parameter, qtet, [1]:
[1] Zimmermann et al., J. Am. Chem. Soc., 2017, 10.1021/jacs.5b08098

51. We have now developed mathematical order parameters for
22 different local environments
51

52. Applications of order parameters
52
Defect / interstitial site identification
[1] Zimmermann et al., Frontiers of Materials, 2017, doi: 10.3389/fmats.2017.00034
Diffusion path characterization

53. Deployed on Materials Project: similar structure matching
53
https://www.materialsproject.org/materials/mp-91/!
Target: W
similar structures
(distance near 0)
Cs3Sb!
TiGaFeCo!
CeMg2Cu!

54. 54
Can cluster
crystal structures
by “local
environment
similarity”

55. 55
Matbench: use matminer to create a black-box optimizer

56. Dataset: 24,597 crystalline mats
Scoring: 10% held validation set
Test Set variability (MAD): 0.81 eV/atom
Literature MAE: 0.12 eV/atom (best)1
Matbench MAE: 0.122 ± 0.024 eV/atom
DFT Eform Exp. Eg
Dataset: 6,354 mats
Scoring: 20% held validation set
Test Set variability (SD): 1.5 eV
Literature RMSE: 0.45 eV (best)2
Matbench RMSE: 0.48 ± 0.07 eV
Regression Performance = Variability of Test Set
Average Predictive Error
56
Performance against literature best on two
regression problems
Problem 1:
DFT-based
formation energy of
bulk materials based
on composition +
structure
Problem 2:
experimental band
gap prediction based
on composition only
Choudhary et al. Physical
Review Materials, 2,
083801 (2018)
Zhuo et al. The Journal
of Physical Chemistry
Letters, 9, 1668 (2018)

57. 57
Matminer – getting started
Ward et al. Matminer : An open
source toolkit for materials data
mining. Computational Materials
Science, 152, 60–69 (2018).!
Paper Docs Support
hackingmaterials.github.io
/matminer!
https://groups.google.com/
forum/#!forum/matminer!

58. Outline
58
① Density functional theory and “high-
throughput” screening of materials
– High-throughput density functional theory
– Searching for new thermoelectrics
② Software to accelerate materials design
– atomate
– matminer
③ Text mining for materials design
– “Materials Scholar”

59. 59
An engine to label the content of scientific abstracts
Collect, clean, and extract information from millions of
published materials science journal abstracts

60. 60
Application: a revised materials search engine
Auto-generated summaries of materials based on text mining

61. 61
Application: materials compositions of interest …
A search for thermoelectrics that do not have Pb or Bi

62. • Predicting thermoelectric compositions
– Step 1: Start with all chemical compositions in our text
library
– Step 2: Identify compositions with high correlation to
the word “thermoelectric” (details TBA)
– Step 3: (optional) Filter out compositions explicitly
studied as thermoelectrics to yield only new
predictions
62
How about new materials discovery?

63. 63
This method can predict thermoelectric materials
years in advance of actual discovery
Each year is trained only on abstracts published until that year

64. 64
Text mining predictions correspond to materials with very
promising computed properties – without any simulations
a
Li2CuSb
b
optoe
in
electron
BaC

65. 65
How does this work? (schematic)

66. • Recently, many new methods are being
developed that combine theory, computation,
and machine learning to accelerate materials
design problems
• The rate of change is accelerating and new ideas
are needed to bring things even further!
66
Conclusions
Quantum mechanics Density functional theory High-throughput DFT
e– e–
e– e–
e– e–
Materials databases Machine learning
1920s 1960s 2000s 2010s 2010s

67. • High-throughput DFT & Materials Project
– K.A. Persson, G. Ceder, S.P. Ong & many others
• Thermoelectrics screening
– G. Snyder, G. Hautier, M.A. White, & many others on the team
• Atomate
– K. Mathew & development team
• Matminer
– L. Ward & development team
• Text mining
– V. Tshitoyan, J. Dagdelen, L. Weston & collaborators
• Funding:
– DOE-BES (MP)
– DOE-BES (ECRP)
– Toyota Research Institute
• Computing: NERSC
67
Thank you!
Slides (already) posted to hackingmaterials.lbl.gov