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Combined Theory and Data-Driven Approaches to Thermoelectrics Materials Discovery 

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Presentation given at MRS Spring 2019

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Combined Theory and Data-Driven Approaches to Thermoelectrics Materials Discovery 

  1. 1. Combined Theory and Data-Driven Approaches to Thermoelectrics Materials Discovery Anubhav Jain Energy Technologies Area Lawrence Berkeley National Laboratory Berkeley, CA MRS Spring 2019 Slides (already) posted to hackingmaterials.lbl.gov
  2. 2. 2 Today, it is possible to screen for thermoelectric materials computationally Year Composition Method of prediction Peak zT in experiments Notes 2006 - 2009 LiZnS DFT-based screening of 570 Sb-containing 0.08 at ~525 K, p-type Could not be doped n- type 2008 - 2015 NbFeS DFT based screening of 36 half-Heusler compositions 1.5 at 1200 K, p-type Multiple independent predictions 2014 SnS High-throughput screening >450 binary sulfides 0.6 at 873 K, p-type Complex prediction history 2015 TmAgTe2 DFT-based screening of ~48,000 compounds 0.47 at ~700 K, p-type Couldn’t dope to desired carrier concentration 2016 YCuTe2 Substitutions from above screening 0.75 at 780 K, p-type Experiment is close to prediction (zT ~0.82) 2016 Er12Co5Bi Machine learning recommendation engine 0.07 at 600 K, n-type Pure ML, no theory 2017 KAlSb4 DFT-based screening of 145 Zintl compounds 0.7 at ~650 K, n-type Experiment is very close to prediction 2018 Cd1.6Cu3.4In3Te8 DFT-based screening of 214 diamond-like systems 1.04 at 875 K, p-type CdIn2Te4 was the initial hit from screening 2019 TaFeSb DFT-based screening of 27 half-Heusler compounds 1.52 at 973 K, p-type Compound never reported previously Urban, Menon, Tian, Jain, Hippalgoankar. New Horizons in Thermoelectric Materials…in review, J. Applied Physics
  3. 3. 3 The record so far in terms of computationally-guided thermoelectrics predictions Year Composition Method of prediction Peak zT in experiments Notes 2006 - 2009 LiZnS DFT-based screening of 570 Sb-containing 0.08 at ~525 K, p-type Could not be doped n- type 2008 - 2015 NbFeS DFT based screening of 36 half-Heusler compositions 1.5 at 1200 K, p-type Multiple independent predictions 2014 SnS High-throughput screening >450 binary sulfides 0.6 at 873 K, p-type Complex prediction history 2015 TmAgTe2 DFT-based screening of ~48,000 compounds 0.47 at ~700 K, p-type Couldn’t dope to desired carrier concentration 2016 YCuTe2 Substitutions from above screening 0.75 at 780 K, p-type Experiment is close to prediction (zT ~0.82) 2016 Er12Co5Bi Machine learning recommendation engine 0.07 at 600 K, n-type Pure ML, no theory 2017 KAlSb4 DFT-based screening of 145 Zintl compounds 0.7 at ~650 K, n-type Experiment is very close to prediction 2018 Cd1.6Cu3.4In3Te8 DFT-based screening of 214 diamond-like systems 1.04 at 875 K, p-type CdIn2Te4 was the initial hit from screening 2019 TaFeSb DFT-based screening of 27 half-Heusler compounds 1.52 at 973 K, p-type Compound never reported previously Urban, Menon, Tian, Jain, Hippalgoankar. New Horizons in Thermoelectric Materials…in review, J. Applied Physics
  4. 4. Outline 4 ① AMSET model: improving the accuracy of electronic transport calculations ② Suggesting new thermoelectrics by “reading the literature” using natural language processing
  5. 5. • High-throughput calculations of mobility (and Seebeck) typically employ a constant, fixed relaxation time approximation • The goal of AMSET is to provide a model that can explicitly calculate scattering rates while remaining computationally efficient – E.g., the accuracy of EPW at 1/1000 the computational cost 5 AMSET is a model to overcome limitations in constant, fixed relaxation time models https://github.com/hackingmaterials/amset
  6. 6. 6 AMSET overview
  7. 7. 7 AMSET overview • Limitations of AMSET • Requires distinct band extrema (one or several is fine) • No intervalley scattering (two valleys within the same band) • No interband scattering (two valleys in different bands) • No metals (need distinct VB and CB) • Anisotropy is OK! (but takes more time)
  8. 8. Acoustic deformation potential scattering (ADP) Inputs: Deformation potential, elastic constant Ionized impurity scattering (IMP) Inputs: Dielectric constant Piezoelectric scattering (PIE) Inputs: Dielectric constant, piezoelectric coefficient Polar optical phonon scattering (POP) Inputs: Polar optical phonon frequency, dielectric constant 8 AMSET scattering equations
  9. 9. 9 AMSET mobility (no fitting parameters) and comparison against cRTA Paper in preparation Anisotropic- b-axis data
  10. 10. 10 AMSET Seebeck results (no fitting parameters) Paper in preparation
  11. 11. • The next step for AMSET is to run in a “medium” throughput – i.e., hundreds of compounds • We also want to auto-detect when AMSET might not be applicable – likely to have intervalley / interband scattering – can’t separate band structure into distinct valleys • A manuscript is in preparation • https://github.com/hackingmaterials/amset/ 11 Next steps
  12. 12. Outline 12 ① AMSET model: improving the accuracy of electronic transport calculations ② Suggesting new thermoelectrics by “reading the literature” using natural language processing
  13. 13. We have extracted ~3 million abstracts of scientific articles We will use natural language processing algorithms to try to extract knowledge from all this data 13 Do past journal articles contain enough information to predict what materials will be studied in the future?
  14. 14. • 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 14 Key concept 1: the word2vec algorithm Paper in review
  15. 15. • 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! (BoltzTraP) 15 Key concept 2: vector dot products measure similarity Paper in review
  16. 16. “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 16 Can we predict future thermoelectrics discoveries with this method? Paper in review
  17. 17. • 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 17 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
  18. 18. • We are developing a new level of theory called AMSET that gives more accurate results for mobility / Seebeck at low computational cost – https://github.com/hackingmaterials/amset/ • We are employing text mining to suggest compositions likely to be thermoelectrics 18 Conclusions
  19. 19. • AMSET – A. Faghaninia and A. Ganose – Funding: U.S. Department of Energy, Basic Energy Sciences, Early Career Research Program – Computing: NERSC • Text mining – V. Tshitoyan, J. Dagdelen, L. Weston, K.A. Persson, G. Ceder – Funding: Toyota Research Institute 19 Thank you! Slides (already) posted to hackingmaterials.lbl.gov
  20. 20. 20 Interpreting predictions
  21. 21. 21 AMSET mobility results (no fitting parameters)
  22. 22. 22 AMSET mobility results (no fitting parameters) Overestimation due to lack of intervalley scattering
  23. 23. 23 AMSET mobility (no fitting parameters) and comparison against cRTA – constant temperature (T=300K) Paper in preparation

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