Application of the Materials Project database and data mining towards the design of thermoelectric and functional materials
2 likes1,643 views
This document summarizes research into using high-throughput density functional theory (DFT) calculations and data mining of the Materials Project database to design new thermoelectric and functional materials. Key findings include identifying new thermoelectric materials like TmAgTe2 with a peak zT of 0.75 through screening 40,000 compounds. Preliminary analysis also began to understand how to control band structure by ranking elemental orbitals' likelihood of forming the valence band maximum and conduction band minimum. The overall goal is to accelerate materials discovery and design through computational modeling and data mining at massive scales.
Application of the Materials Project database and data mining towards the design of thermoelectric and functional materials
- 1. Application of the Materials Project database and data mining towards the design of thermoelectric and functional materials Anubhav Jain Berkeley Lab MRS Fall 2015 Slides posted to http://www.slideshare.net/anubhavster
- 2. Today, it is possible to design materials with DFT 2 • Some computable properties include: thermodynamic quantities, electronic structure (e.g., band structure), magnetic states, diffusion barriers, and elastic properties Hautier, G.; Jain, A.; Ong, S. P. From the computer to the laboratory: materials discovery and design using first- principles calculations, J. Mater. Sci., 2012, 47, 7317–7340 Jain, A.; Shin, Y; Persson, K; Computational Predictions of Energy Materials using Density Functional Theory. Nature Reviews Materials, ACCEPTED
- 3. The Materials Project – a library of DFT properties 3 Jain*, Ong*, Hautier, Chen, Richards, Dacek, Cholia, Gunter, Skinner, Ceder, and Persson, APL Mater., 2013, 1, 011002. *equal contributions The Materials Project (http://www.materialsproject.org) free and open >15,000 registered users around the world >65,000 compounds calculated Data includes • thermodynamic props. • electronic band structure • aqueous stability (E-pH) • elasticity tensors >75 million CPU-hours invested = massive scale!
- 4. MPContribs –small data contributions to MP 4 Your Materials Data A. T. N’Diaye (ALS, LBNL): • measured XAS/XMCD spectra • properties of rare earth substitutes • processing of instrumental data • integration w/ MP phase diagrams D. Morgan, H. Wu (SI2, UW): • computed diffusion coefficients • automated VASP data extraction and integration • Scope: small, structured data sets that summarize materials and complement MP data • NOT raw input and output files • Provenance and citation for your data • Contact [email protected] or [email protected] Demo: https://youtu.be/xlwttmXSpHg Users can now disseminate data using MP as their platform
- 5. MPComplete – suggest structures to compute 5 Total Submissions & Users since Launch 2015 Upload a structure suggestion to help fill in MP database. • Can’t control parameters • Results are public Demo: https://youtube.com/user/materialsproject
- 6. Other computational materials libraries 6 Lin, L. Materials Databases Infrastructure Constructed by First Principles Calculations: A Review, Mater. Perform. Charact., 2015, 4, MPC20150014 • AFLOWLIB • AiiDA • Alloy Database • CCCBDB • CEPDB • CMR/ASE • ESP • ESTEST • MAST • Materials Project • NoMad • OQMD/qmpy • Pychemia Even this list is not complete! • PhononDB from Kyoto University • MatDB/TEDesignLab from NREL Citrine Informatics – combining DBs
- 7. Thermoelectric materials • A thermoelectric material generates a voltage based on applied thermal gradient – picture a charged gas that diffuses from hot to cold until the electric field balances the thermal gradient • The voltage per Kelvin is the Seebeck coefficient • A thermoelectric module generates current by linking n and p type materials 7 www.alphabetenergy.com
- 8. Why are thermoelectrics useful? 8 • Applications: energy from heat, refrigeration • Already used in spacecraft • Large-scale waste heat recovery is targeted – Back of the envelope suggests ~$1-$10/watt needed Alphabet Energy – 25kW generator Uses tetrahedrite materials developed in 2013 by Michigan State/UCLA
- 9. Thermoelectric figure of merit 9 • 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)
- 10. We’ve initiated a search for thermoelectric materials 10 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 Madsen, G. K. H. Automated search for new thermoelectric materials: the case of LiZnSb. J. Am. Chem. Soc., 2006, 128, 12140–6
- 11. Community is developing other models 11 A “quality factor” approach to estimating zT Yan, J.; Gorai, P.; Ortiz, B.; Miller, S.; Barnett, S. A.; Mason, T.; Stevanović, V.; Toberer, E. S. Material descriptors for predicting thermoelectric performance, Energy Environ. Sci., 2015, 8, 983–994 Thermal conductivity from quasi-harmonic approximation using average of square Gruneisen Madsen, G. K. H.; Katre, A.; Bera, C. Calculating the thermal conductivity of the silicon clathrates using the quasi-harmonic approximation, 1–7. Thermal conductivity from E-V curves and the GIBBS approximation Toher, C.; Plata, J. J.; Levy, O.; de Jong, M.; Asta, M.; Nardelli, M. B.; Curtarolo, S. High-Throughput Computational Screening of thermal conductivity, Debye temperature and Gruneisen parameter using a quasi-harmonic Debye Model, 2014, 1–15.
- 12. Overview of data set (40,000 compounds) 12 Chen, Pöhls, Hautier, Broberg, Bajaj, Aydemir, Gibbs, Zhu, Ceder, Asta, Snyder, Meredig, White, Persson, Jain. Understanding Thermoelectric Properties from High-Throughput Calculations: Trends, Insights, and Comparisons with Experiment. IN PREP.
- 13. Abundant thermoelectrics: difficulty of oxides • Oxides would be great: synthesizability, stability, cost • But they suffer from a triple strike: – they are difficult to dope due to wide band gap – they have higher thermal conductivity – they have poorer thermoelectric performance independent of these issues 13 Chen, Pöhls, Hautier, Broberg, Bajaj, Aydemir, Gibbs, Zhu, Ceder, Asta, Snyder, Meredig, White, Persson, Jain. Understanding Thermoelectric Properties from High-Throughput Calculations: Trends, Insights, and Comparisons with Experiment. IN PREP.
- 14. New Materials from screening – TmAgTe2 (calcs) 14 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
- 15. TmAgTe2 - experiments 15 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
- 16. YCuTe2 – friendlier elements, higher zT (0.75) 16 • A combination of intuition and calculations suggest to try YCuTe2 • Higher carrier concentration of ~1019 • Retains very low thermal conductivity, peak zT ~0.75 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. IN PREP.
- 17. Future: rationally control the band structure 17 example: • understanding the character of states that form the VBM / CBM • in TmAgTe2, increased hybridization lowers the valley degeneracy • Can we predict the orbital character of arbitrary materials? Jain, A.; Hautier, G.; Ong, S.; Persson, K.A.; New Opportunities for Materials Informatics: Resources and Data Mining Techniques for Uncovering Hidden Relationships. SUBMITTED. DFT/GGA+U projected DOS for MoO3
- 18. Procedure for ranking likelihood to form VBM/CBM • Data set of 2558 materials – ionic materials evaluated via Bond Valence Sum method – band gap of 0.2 or higher (clear VBM and CBM) – avoid f-electron materials – limited pool of elements/orbitals competing for VBM/CBM • For each material: – determine the ionic orbitals (e.g., Mn3+:d, O2-:p, P5+:p) that are present – determine the contribution of each ionic orbital to VBM/CBM using projected DOS – For each pair of ionic orbitals (e.g., Mn3+:d versus O2-:p), score a “win” for the ionic orbital that contributes more to VBM/CBM • Use model to determine universal ranking from the series of pairwise competitions (Bradley-Terry model) 18 Jain, A.; Hautier, G.; Ong, S.; Persson, K.A.; New Opportunities for Materials Informatics: Resources and Data Mining Techniques for Uncovering Hidden Relationships. SUBMITTED.
- 19. Results: likelihood to form VBM/CBM 19 • Example interpretation: in a material with Cu1+:d, Fe3+:d, and O2-:p states, the Cu is likely to be VBM and Fe likely to be CBM (this is true for FeCuO2) • There are problems with such a universal ranking (discussed in paper) Jain, A.; Hautier, G.; Ong, S.; Persson, K.A.; New Opportunities for Materials Informatics: Resources and Data Mining Techniques for Uncovering Hidden Relationships. SUBMITTED.
- 20. Summary • Materials Project DB: MPContribs and MPComplete • Screened ~40,000 materials as thermoelectrics • Overall, thermoelectric design difficult, particularly oxides and isotropic materials with low HHI • Two new materials uncovered and tested: TmAgTe2 and YCuTe2, with zT reaching 0.75 • Preliminary data analysis begun to understand how to control band character and band structure, with a ranking for states likely to dominate VBM/ CBM 20
- 21. Thank you! • UC Berkeley / LBL: Wei Chen, Hong Zhu, Danny Broberg, Mark Asta, Kristin Persson, Gerbrand Ceder • Caltech: Umut Aydemir, Zach Gibbs, Saurabh Bajaj, Jeff Snyder • UC Louvain, Belgium: Geoffroy Hautier • Dalhousie University: Jan Pohls, Mary Anne White • Funding: DOE BES Materials Science Division • Thank you: NERSC supercomputing center 21 Slides posted to http://www.slideshare.net/anubhavster