Combining High-Throughput Computing and Statistical Learning to Develop and Understand New Thermoelectric Compounds
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This document summarizes research into developing new thermoelectric materials through high-throughput computing and statistical learning. Key points: - Thermoelectric materials can convert heat to electricity but require high figure of merit (ZT). Computational screening of over 50,000 compounds has identified promising candidates. - TmAgTe2 and YCuTe2 were discovered through this method and experimentally validated, with zT of 0.75 for YCuTe2. - Bournonite materials like CuPbSbS3 have low thermal conductivity but require improved electrical properties. Over 300 substitutions were modeled to explore variations. - Open data resources like Materials Project and tools like MatMiner being developed to enable data mining and accelerate materials
Combining High-Throughput Computing and Statistical Learning to Develop and Understand New Thermoelectric Compounds
- 1. Combining High-Throughput Computing and Statistical Learning to Develop and Understand New Thermoelectric Compounds Anubhav Jain Energy Technologies Area Lawrence Berkeley National Laboratory Berkeley, CA MRS Fall 2016 Slides (already) posted to http://www.slideshare.net/anubhavster
- 2. 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) 2 www.alphabetenergy.com Alphabet Energy – 25kW generator
- 3. Thermoelectric figure of merit 3 • Require new, abundant 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!
- 4. Example: Seebeck and conductivity tradeoff 4 Heavy band: ü Large DOS (higher Seebeck and more carriers) ✗Large effective mass (poor mobility)
- 5. Example: Seebeck and conductivity tradeoff 5 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)
- 6. Example: Seebeck and conductivity tradeoff 6 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!
- 7. We’ve initiated a search for new bulk thermoelectrics 7 Initial procedure similar to Madsen (2006) On top of this traditional procedure we add: • thermal conductivity model of Pohl-Cahill • targeted defect calculations to assess doping • Today - ~50,000 compounds screened! Madsen, G. K. H. Automated search for new thermoelectric materials: the case of LiZnSb. J. Am. Chem. Soc., 2006, 128, 12140–6 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).
- 8. Going beyond constant relaxation time - AMSET • Fully ab initio mobility and Seebeck including realistic scattering effects • Previously aMOBT (Washington University in St. Louis) • Parameterizes the band structure into 1D – Misses anisotropic effects and doesn’t fully treat multi-band effects (for now) • Uses scattering expressions derived by previous work by Rode with DFT parameters – ionized impurity scattering – deformation potential scattering – piezoelectric scattering – polar optical phonon 8 Faghaninia, A., Ager, J. W. & Lo, C. S. Ab initio electronic transport model with explicit solution to the linearized Boltzmann transport equation. Phys. Rev. B 91, 235123 (2015).
- 9. Transport database 9 All data will be made available via upcoming publication as well as on Materials Project • Seebeck • conductivity/tau • effective mass • electronic thermal conductivity
- 10. New Materials from screening – TmAgTe2 (calcs) 10 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
- 11. TmAgTe2 (experiments) 11 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
- 12. YCuTe2 – friendlier elements, higher zT (0.75) 12 • A combination of intuition and calculations suggest to try YCuTe2 • Higher carrier concentration of ~1019 • Retains very low thermal conductivity, peak zT ~0.75 • But – unlikely to improve further Aydemir, U.; Pöhls, J.-H.; Zhu, H.l 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
- 13. Bournonites – CuPbSbS3 and analogues • Natural mineral • Measured thermal conductivity for CuPbSbS3 < 1 W/m*K – Stereochemical lone pair scattering mechanisms • Measured Seebeck coefficient in the range of 400 µV/K • BUT electrical conductivity likely requires improvement – can calculations help? • Total of 320 substitutions into ABCD3 formula computed • Experimental study is next 13 Faghaninia A., Yu G., Aydemir U., Wood M., Chen W., Rignanese G.M., Snyder J., Hautier G., Jain, A. A computational assessment of the electronic, thermoelectric, and defect properties of bournonite (CuPbSbS3) and related substitutions (submitted)
- 14. Variation of properties with substitution 14
- 15. Variation of properties with substitution 15 B and C groups (lone pair sites) require heavier elements for stability (low Eh) – Si and N are very unstable!
- 16. Variation of properties with substitution 16 As expected, band gaps tend to decrease with heavier anions This is due to shifting up of the VBM level
- 17. Variation of properties with substitution 17
- 18. Variation of properties with substitution 18 Cu has lowest bandgap because Cu1+ also tends to be very high up in the valence band
- 19. Variation of properties with substitution 19 Jain, A., Hautier, G., Ong, S. P. & Persson, K. New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships. J. Mater. Res. 31, 977–994 (2016).
- 20. Interesting bournonites and effect of scattering 20 AMSET indicates interband scattering is extremely significant – need to confirm Substitutions listed here are close to thermodynamic stability (<0.05 eV /atom unstable)
- 21. Defects – selenide looks slightly better than sulfide 21 (a) (b) • Multiple defects prevent n-type formation • p-type limited by SbPb defect. Situation slightly better in selenide because CuPb can help compensate • Extrinsic defects calculations (not shown) do not provide clear paths forward Faghaninia A., Yu G., Aydemir U., Wood M., Chen W., Rignanese G.M., Snyder J., Hautier G., Jain, A. A computational assessment of the electronic, thermoelectric, and defect properties of bournonite (CuPbSbS3) and related substitutions (submitted) CuPbSbS3 CuPbSbSe3
- 22. Open data and software 22 www.materialsproject.org www.pymatgen.org www.github.com/hackingmaterials/MatMethods www.pythonhosted.org/FireWorksNote: results of 50,000 transport calcs will eventually be posted here Coming soon: AMSET Coming soon: MatMiner
- 23. MatMiner (coming soon) MatMiner’s goal: help enable data mining studies in materials science 23
- 24. Interactive demo of MatMiner • Can we create a machine learning model to predict bulk modulus that is accurate to ~20GPa in ~10 mins? • Let’s find out! • Code posted at: – https://gist.github.com/computron 24
- 25. Thank you! • Collaborating research groups – Jeffrey Snyder – Geoffroy Hautier – Mary Anne White – Mark Asta – Hong Zhu – Kristin Persson – Gerbrand Ceder • Primary researchers – TmAgTe2 – Prof. Hong Zhu and Dr. Umut Aydemir – YCuTe2 – Dr. Umut Aydemir and Dr. Jan Pohls – CuPbSbS3 – Dr. Alireza Faghaninia – MatMiner – Dr. Saurabh Bajaj • NERSC computing center and staff • Funding: U.S. Department of Energy, Basic Energy Sciences 25Slides (already) posted to http://www.slideshare.net/anubhavster