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Discovering advanced materials for energy
applications
(with high-throughput computing and by mining the scientific litera...
2
Often, world-changing ideas are inhibited by the physical
properties of available materials at the time
Electric vehicle...
• Often, materials are known for several decades
before their functional applications are known
– MgB2 sitting on lab shel...
4
A material is defined at multiple length scales –
stick to the fundamental scale for now
5
A material is defined at multiple length scales –
stick to the fundamental scale for now
6
Atoms in a box – the materials universe is huge!
• Bag of 30 atoms
• Each atom is one of 50
elements
• Arrange on 10x10x...
7
Finding the right material is like
“finding a needle in a haystack”
What constrains traditional approaches to materials design?
8
“[The Chevrel] discovery resulted from a lot of
unsuccessful...
• Materials are:
– Important – constrain what’s possible in the physical
world
– Difficult to design – many, many possibil...
10
Researchers are starting to fundamentally re-think how we
invent the materials that make up our devices
Next-
generatio...
11
Today, computer aided design of products is ubiquitous –
but what are the governing equations to model materials?
Materials physics is determined by quantum mechanics
12
−!2
2m
∇2
Ψ(r)+V (r)Ψ(r) = EΨ(r)
Schrödinger equation describes al...
• There aren’t too many real situations where we can
get a closed solution to the Schrödinger equation
• Let’s pretend we ...
Maybe Dirac said it best …
14
“The underlying physical laws necessary
for the mathematical theory of a large part
of physi...
What is density functional theory (DFT)?
15
DFT is a method solve for the electronic structure and energetics of arbitrary...
How does one use DFT to design new materials?
16
A. Jain, Y. Shin, and K. A.
Persson, Nat. Rev. Mater.
1, 15004 (2016).
• System size is essentially limited to a few thousand atoms
– many important materials phenomena simply do not occur at t...
• Ok, so we have a computational model now that
allows us to assemble atoms in a computer and
predict their physical prope...
A big advantage of computational modeling is that it can be
automated – so we can screen many ideas in parallel
19
Automat...
• The answer is “it really varies a lot”
– how big / complicated are the materials you are modeling?
– how complex / expen...
Example of high-throughput materials screening:
Li ion battery cathodes
21
anode electrolyte cathode
Li+ discharge
e- disc...
The cathode material is like a Li sponge (on the atomic scale)
The cathode material must quickly
absorb and release large
...
Anatomy of a cathode composition
Lia Mb (XYc)d
Li ion
source
electron
donor /
acceptor
structural
framework /
charge neutr...
Calculate average voltage by computing energy differences
in structures w/ or w/o Li
24
24
GGA+U
results
Li
avg
OC
xF
G
V
...
Diffusion via Nudged Elastic Band
Hexagonal phase
low Li 529 meV
high Li 723 meV
monoclinic phase
low Li 395 meV
high Li 5...
Compounds screened over time
Plain Oxides
(9204)
Silicates (1857)
Phosphates (1609)
Borates (1035)
Carbonates (370)
Vanada...
New mixed phosphate-pyrophosphate
Chemistry Novelty Energy density
vs. LiFePO4
% of theoretical capacity
already achieved ...
One can apply this template to many different applications
28
Sidorenkite-based Li-ion battery
cathodes
YCuTe2 thermoelect...
29
Examples of experimentally-confirmed materials designed
with DFT (1)
Jain, A., Shin, Y., Persson, K.A., 2016. Computati...
30
Examples of experimentally-confirmed materials designed
with DFT (2)
Jain, A., Shin, Y., Persson, K.A., 2016. Computati...
• This information is much harder to find, but:
– New alkaline battery from Duracell with assist from high-throughput
scre...
32
Today, DFT is often used within a pipeline that includes
machine learning – but that is a separate talk …
Machine learn...
33
Researchers are starting to fundamentally re-think how we
invent the materials that make up our devices
Next-
generatio...
34
Can ML help us work through our backlog of information we
need to assimilate from text sources?
papers to read “someday...
Extracted ~2 million
abstracts of relevant
scientific articles
Use natural language
processing algorithms
to try to extrac...
36
Algorithms to automatically identify keywords in the
abstracts based on word2vec and LSTM networks
Weston, L. et al Nam...
37
Named entity recognition to detect materials, applications,
etc.
Named Entity Recognition
X
• Custom machine learning m...
38
Now we can search!
Live on www.matscholar.com
39
Another example …
40
And also analyze and make suggestions for new text …
41
Could these techniques also be used to predict which
materials we might want to screen for an application?
papers to re...
• We use the word2vec
algorithm (Google) to turn
each unique word in our
corpus into a 200-
dimensional vector
• These vec...
• We use the word2vec
algorithm (Google) to turn
each unique word in our
corpus into a 200-
dimensional vector
• These vec...
• The classic example is:
– “king” - “man” + “woman” = ? → “queen”
44
Word embeddings trained on ”normal” text learns
rela...
45
When trained on materals science abstracts,
word2vec learns scientific concepts
crystal structures and principal
oxides...
• Dot product of a composition word with
the word “thermoelectric” essentially
predicts how likely that word is to appear
...
“Go back in time”
approach:
– For every year since
2001, see which
compounds we would
have predicted using only
literature...
• Thus far, 2 of our top 20 predictions made in
~August 2018 have already been reported in the
literature for the first ti...
49
How is this working?
“Context
words” link
together
information
from different
sources
• Developing new materials is of fundamental
importance to realizing new physical
technologies
• Today, it possible to sta...
51
Acknowledgements
Slides (already) posted to hackingmaterials.lbl.gov
• High-throughput DFT
– Gerbrand Ceder and “BURP” ...
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Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 1 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 2 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 3 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 4 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 5 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 6 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 7 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 8 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 9 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 10 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 11 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 12 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 13 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 14 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 15 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 16 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 17 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 18 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 19 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 20 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 21 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 22 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 23 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 24 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 25 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 26 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 27 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 28 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 29 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 30 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 31 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 32 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 33 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 34 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 35 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 36 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 37 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 38 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 39 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 40 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 41 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 42 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 43 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 44 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 45 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 46 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 47 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 48 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 49 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 50 Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Slide 51
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Presentation given at ACM meetup, Santa Clara CA, Jan 2020

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Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature)

  1. 1. Discovering advanced materials for energy applications (with high-throughput computing and by mining the scientific literature) Anubhav Jain Energy Technologies Area Lawrence Berkeley National Laboratory Berkeley, CA ACM Meetup, Jan 2020 Slides (already) posted to hackingmaterials.lbl.gov
  2. 2. 2 Often, world-changing ideas are inhibited by the physical properties of available materials at the time Electric vehicles and solar power are two technologies that had been dreamed about for many decades, yet are only seeing wide adoption today 1910 1956
  3. 3. • Often, materials are known for several decades before their functional applications are known – MgB2 sitting on lab shelves for 50 years before its identification as a superconductor in 2001 – LiFePO4 known since 1938, only identified as a Li-ion battery cathode in 1997 • Even after discovery, optimization and commercialization still take decades • To get a sense for why this is so hard, let’s look at the problem in more detail … 3 Typically, both new materials discovery and optimization take decades
  4. 4. 4 A material is defined at multiple length scales – stick to the fundamental scale for now
  5. 5. 5 A material is defined at multiple length scales – stick to the fundamental scale for now
  6. 6. 6 Atoms in a box – the materials universe is huge! • Bag of 30 atoms • Each atom is one of 50 elements • Arrange on 10x10x10 lattice • Over 10108 possibilities! – more than grains of sand on all beaches (1021) – more than number of atoms in universe (1080)
  7. 7. 7 Finding the right material is like “finding a needle in a haystack”
  8. 8. What constrains traditional approaches to materials design? 8 “[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)
  9. 9. • Materials are: – Important – constrain what’s possible in the physical world – Difficult to design – many, many possibilities – Ripe for new ways of approaching the problem 9 Why do we need new ways of designing materials?
  10. 10. 10 Researchers are starting to fundamentally re-think how we invent the materials that make up our devices Next- generation materials design Computer- aided materials design Natural language processing “Self-driving laboratories”
  11. 11. 11 Today, computer aided design of products is ubiquitous – but what are the governing equations to model materials?
  12. 12. Materials physics is determined by quantum mechanics 12 −!2 2m ∇2 Ψ(r)+V (r)Ψ(r) = EΨ(r) Schrödinger equation describes all the properties of a system through the wavefunction: Time-independent, non-relativistic Schrödinger equation
  13. 13. • There aren’t too many real situations where we can get a closed solution to the Schrödinger equation • Let’s pretend we want to approach things numerically for 1000 electrons – There are ~500,000 electron-electron interactions to worry about. – Even storing the wavefunction would take ~101000 GB! • Discretize the x,y,z, position of each electron into a 1000- element grid = 1 billion positions per electron • Need the wavefunction output (real + complex part) for each combination of all electron positions, i.e. 1E9 ^ (1000) * 2, or 2E9000 values • even at 1 byte per wavefunction value (low resolution), you have about 2E1000 GB needed needed to store the wavefunction! 13 The wave function is formidable
  14. 14. Maybe Dirac said it best … 14 “The underlying physical laws necessary for the mathematical theory of a large part of physics and the whole of chemistry are thus completely known, and the difficulty is only that the exact application of these laws leads to equations much too complicated to be soluble.” “It therefore becomes desirable that approximate practical methods of applying quantum mechanics should be developed, which can lead to an explanation of the main features of complex atomic systems without too much computation.”
  15. 15. What is density functional theory (DFT)? 15 DFT is a method solve for the electronic structure and energetics of arbitrary materials starting from first-principles. It replaces many-body interactions with a mean field interaction that reproduces the same charge density. In theory, it is exact for the ground state. In practice, accuracy depends on the choice of (some) parameters, the type of material, the property to be studied, and whether the simulated system (crystal) is a good approximation of reality. DFT resulted in the 1999 Nobel Prize for chemistry (W. Kohn). It is responsible for 2 of the top 10 cited papers of all time, across all sciences. e–e– e– e– e– e–
  16. 16. How does one use DFT to design new materials? 16 A. Jain, Y. Shin, and K. A. Persson, Nat. Rev. Mater. 1, 15004 (2016).
  17. 17. • System size is essentially limited to a few thousand atoms – many important materials phenomena simply do not occur at this length scale; other techniques available with reduced accuracy • Certain materials, such as those with strong electron correlation, remain difficult to model accurately • Certain properties, including excited state properties such as band gap, remain difficult to model accurately • These are all active areas of research and improvement to the theory, and the situation is improving on all fronts 17 Limitations of density functional theory
  18. 18. • Ok, so we have a computational model now that allows us to assemble atoms in a computer and predict their physical properties • What next? 18
  19. 19. A big advantage of computational modeling is that it can be automated – so we can screen many ideas in parallel 19 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) S. Kirklin et al., Acta Mater. 102 (2016) 125-135
  20. 20. • The answer is “it really varies a lot” – how big / complicated are the materials you are modeling? – how complex / expensive are the physical properties you are trying to predict? • Ballpark numbers: – Low range: optimize structure of ~3-atom compounds • time to do a million materials ~ 10 million core-hours – Medium range: bulk modulus of ~50 atom compounds • time to do a million materials ~ 2 billion core-hours – The “high range” can go almost as high as you’d like … • A “tiered” screening strategy is common 20 How much computer time is needed for high-throughput DFT?
  21. 21. Example of high-throughput materials screening: Li ion battery cathodes 21 anode electrolyte cathode Li+ discharge e- discharge e.g. graphitic carbon e.g. LiPF6 / (EC/DMC) e.g. LiCoO2 LiFePO4 Li+ charge e- charge
  22. 22. The cathode material is like a Li sponge (on the atomic scale) 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) 22
  23. 23. Anatomy of a cathode composition 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 23
  24. 24. Calculate average voltage by computing energy differences in structures w/ or w/o Li 24 24 GGA+U results Li avg OC xF G V D D = - [ + ] E (Li Mn O2) - [ E (MnO2) + E (Li) ] ΔG ~
  25. 25. Diffusion via Nudged Elastic Band 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
  26. 26. Compounds screened over time 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) 26
  27. 27. New mixed phosphate-pyrophosphate 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). 27 C/35 at RT 2.0mg 3.0V – 4.7V
  28. 28. One can apply this template to many different applications 28 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)
  29. 29. 29 Examples of experimentally-confirmed materials designed with DFT (1) Jain, A., Shin, Y., Persson, K.A., 2016. Computational predictions of energy materials using density functional theory. Nature Reviews Materials 1, 15004.
  30. 30. 30 Examples of experimentally-confirmed materials designed with DFT (2) Jain, A., Shin, Y., Persson, K.A., 2016. Computational predictions of energy materials using density functional theory. Nature Reviews Materials 1, 15004.
  31. 31. • This information is much harder to find, but: – New alkaline battery from Duracell with assist from high-throughput screening from Computational Modeling Consultants • (based on personal communication) – New alloys for watch and phones from Apple with assist from computational alloy design by Questek • https://www.americaninno.com/chicago/inside-the-small-evanston-company-whose- tech-was-acquired-by-apple-and-used-by-spacex/ – New alloys for 3D printing with guidance from ML-based models from Citrine • https://citrine.io/media-post/aluminum-alloy-designed-using-citrine-platform-becomes- first-ever-officially-registered-for-3d-printing/ – New phosphor materials from Lumenari with guidance from MaterialsQM Consulting • (own work) 31 How about commercial impact?
  32. 32. 32 Today, DFT is often used within a pipeline that includes machine learning – but that is a separate talk … Machine learning / optimization High-throughput DFT Expensive calculation Experiment Training data Compounds to screen external databases (DFT or expt)
  33. 33. 33 Researchers are starting to fundamentally re-think how we invent the materials that make up our devices Next- generation materials design Computer- aided materials design Natural language processing “Self-driving laboratories”
  34. 34. 34 Can ML help us work through our backlog of information we need to assimilate from text sources? papers to read “someday” NLP algorithms
  35. 35. Extracted ~2 million abstracts of relevant scientific articles Use natural language processing algorithms to try to extract knowledge from all this data 35 Use computers to parse research abstracts on our behalf
  36. 36. 36 Algorithms to automatically identify keywords in the abstracts based on word2vec and LSTM networks Weston, L. et al Named Entity Recognition and Normalization Applied to Large-Scale Information Extraction from the Materials Science Literature. J. Chem. Inf. Model. (2019).
  37. 37. 37 Named entity recognition to detect materials, applications, etc. Named Entity Recognition X • Custom machine learning models to extract the most valuable materials-related information. • Utilizes a long short-term memory (LSTM) network trained on ~1000 hand-annotated abstracts. • f1 scores of ~0.9. f1 score for inorganic materials extraction is >0.9. Weston, L., et al. J. Chem. Inf. Model. (2019). doi:10.1021/acs.jcim.9b00470
  38. 38. 38 Now we can search! Live on www.matscholar.com
  39. 39. 39 Another example …
  40. 40. 40 And also analyze and make suggestions for new text …
  41. 41. 41 Could these techniques also be used to predict which materials we might want to screen for an application? papers to read “someday” NLP algorithms
  42. 42. • We use the word2vec algorithm (Google) to turn each unique word in our corpus into a 200- dimensional vector • These vectors encode the meaning of each word meaning based on trying to predict context words around the target 42 Key concept 1: the word2vec algorithm Barazza, L. How does Word2Vec’s Skip-Gram work? Becominghuman.ai. 2017
  43. 43. • We use the word2vec algorithm (Google) to turn each unique word in our corpus into a 200- dimensional vector • These vectors encode the meaning of each word meaning based on trying to predict context words around the target 43 Key concept 1: the word2vec algorithm Barazza, L. How does Word2Vec’s Skip-Gram work? Becominghuman.ai. 2017 “You shall know a word by the company it keeps” - John Rupert Firth (1957)
  44. 44. • The classic example is: – “king” - “man” + “woman” = ? → “queen” 44 Word embeddings trained on ”normal” text learns relationships between words
  45. 45. 45 When trained on materals science abstracts, word2vec learns scientific concepts crystal structures and principal oxides of the elements “word embedding” periodic table Tshitoyan, V. et al. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature 571, 95–98 (2019).
  46. 46. • Dot product of a composition word with the word “thermoelectric” essentially predicts how likely that word is to appear in an abstract with the word thermoelectric • Compositions with high dot products are typically known thermoelectrics • Sometimes, compositions have a high dot product with “thermoelectric” but have never been studied as a thermoelectric • These compositions usually have high computed power factors! (DFT+BoltzTraP) 46 Key concept 2: vector dot products can be used to predict which words might co-occur in abstracts Tshitoyan, V. et al. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature 571, 95–98 (2019).
  47. 47. “Go back in time” approach: – For every year since 2001, see which compounds we would have predicted using only literature data until that point in time – Make predictions of what materials are the most promising thermoelectrics for data until that year – See if those materials were actually studied as thermoelectrics in subsequent years 47 Can we predict future thermoelectrics discoveries with this method? Tshitoyan, V. et al. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature 571, 95–98 (2019).
  48. 48. • Thus far, 2 of our top 20 predictions made in ~August 2018 have already been reported in the literature for the first time as thermoelectrics – Li3Sb was the subject of a computational study (predicted zT=2.42) in Oct 2018 – SnTe2 was experimentally found to be a moderately good thermoelectric (expt zT=0.71) in Dec 2018 • We are working with an experimentalist on one of the predictions (but ”spare time” project) 48 How about “forward” predictions? [1] Yang et al. "Low lattice thermal conductivity and excellent thermoelectric behavior in Li3Sb and Li3Bi." Journal of Physics: Condensed Matter 30.42 (2018): 425401 [2] Wang et al. "Ultralow lattice thermal conductivity and electronic properties of monolayer 1T phase semimetal SiTe2 and SnTe2." Physica E: Low-dimensional Systems and Nanostructures 108 (2019): 53-59
  49. 49. 49 How is this working? “Context words” link together information from different sources
  50. 50. • Developing new materials is of fundamental importance to realizing new physical technologies • Today, it possible to start designing phases of matter in a computer (or supercomputer) • New advancements in computation and machine learning will bring us closer to being able to design new substances from our desks 50 Conclusions
  51. 51. 51 Acknowledgements Slides (already) posted to hackingmaterials.lbl.gov • High-throughput DFT – Gerbrand Ceder and “BURP” team – Funding: Bosch / Umicore • Natural language processing – Gerbrand Ceder, Kristin Persson, and “Matscholar” team – Funding: Toyota Research Institutes • Overall work funded by US Department of Energy

Presentation given at ACM meetup, Santa Clara CA, Jan 2020

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