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Presentation given at the American Ceramic Society 2018 Conference on Electronic & Advanced Materials

Anubhav JainSeguir

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- Density functional theory calculations and data mining for new thermoelectrics discovery Anubhav Jain Energy Technologies Area Lawrence Berkeley National Laboratory Berkeley, CA 2018 Conference on Electronic & Advanced Materials Slides (already) posted to http://www.slideshare.net/anubhavster
- Outline • Using high-throughput density functional theory to screen for new thermoelectric candidates – successes and failures • Open-source software tools for data generation and data mining – atomate to easily run high-throughput DFT – matminer to help in materials data mining 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) 3 www.alphabetenergy.com Alphabet Energy – 25kW generator
- Thermoelectric figure of merit 4 • 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!
- Example: Seebeck and e– 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) Multiple bands, off symmetry: ü Large DOS with small effective mass ✗ Difficult to design! E k
- We’ve initiated a search for new bulk thermoelectrics 6 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).
- Database of transport properties calculated 7 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.
- New Materials from screening – TmAgTe2 (calcs) 8 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
- TmAgTe2 (experiments) 9 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
- YCuTe2 – friendlier elements, higher zT (0.75) 10 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
- Bournonites – CuPbSbS3 and analogues (no expt) 11 A. Faghaninia, G. Yu, U. Aydemir, M. Wood, W. Chen, G.-M.G.-M. Rignanese, et al., A computational assessment of the electronic, thermoelectric, and defect properties of bournonite (CuPbSbS3) and related substitutions, Phys. Chem. Chem. Phys. 19 (2017) 6743–6756. • Previously studied TE material – Measured thermal conductivity < 1 W/m*K – Measured Seebeck coefficient ~ 400 µV/K – BUT electrical conductivity requires improvement – can calculations help? • Calculations: p-PF is 13.8, but we know electronic conductivity will be lower than estimated – Try ~320 chemical substitutions, see whether electronic scattering time is reduced in computational models or whether favorable defect diagram can be found for high carrier concentration – Computations suggest a few interesting candidates, including CuPbSnSe3 and CuPbAsSe3 • Experiments: Preliminary experiments are unsuccessful in synthesizing bournonite CuPbSnSe3. Paused investigations due to time constraints.
- 1-2-1-4 selenides: poor electrical conductivity 12 Kuo et al, Low Thermal Conductivity Selenide Compounds, in preparation Se Ba Ag Sn • Calculations: – p-PFs in the range of 9 – 17.3 assuming a carrier concentration of 1020/cm3 – Calculations indicate low thermal conductivity, possibly due to Ag-Ag dimers • Experiments: – Thermal conductivity is indeed very low – But carrier concentrations and conductivity are much too low – not a viable thermoelectric
- Thermoelectrics screening: lessons so far • When considering our screening strategy in the abstract, the major limitations appeared to be: – no modeling of electron relaxation time – limited modeling of thermal conductivity • However, in reality the biggest limitation has been estimating dopability. This has been the major limitation for all our picks. – The materials we pick just aren’t very dopable for some reason – Computing doping limits is hard; we also learned not to trust GGA defect diagrams for this purpose (at least shift the band edges with an HSE band gap estimate) • So the problems are often not in the known limitations of the theory, but in optimizing aspects of the material you are not computing at all 13
- Outline • Using high-throughput density functional theory to screen for new thermoelectric candidates – successes and failures • Open-source software tools for data generation and data mining – atomate to easily run high-throughput DFT – matminer to help in materials data mining 14
- A “black-box” view of performing a calculation 15 “something”! Results!! researcher! What is the GGA-PBE elastic tensor of GaAs?
- Unfortunately, the inside of the “black box” is usually tedious and “low-level” 16 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?
- Atomate tries make it easy, automatic, and flexible (worry only about the things you want to worry about) 17 Results!! researcher! What is the GGA-PBE elastic tensor of GaAs?
- Atomate knows the sequence of calculations needed to compute many kinds of materials properties 18 quickly and automatically translate PI-style (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).
- Many types of simulation procedures are already available! 19 • band structure • BoltzTraP transport • spin-orbit coupling • hybrid functional calcs • elastic tensor • piezoelectric tensor • Raman spectra • NEB • GIBBS method • QH thermal expansion • AIMD • ferroelectric properties • (defects upcoming?) • FEFF method • LAMMPS MD Workflows currently available in atomate! 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.
- Workflow parameters can be customized at multiple levels of detail 20 1. Workflows have various high-level options 2. Fireworks also have options / flags (not shown) 3. Firetasks have most detailed number of options / flags (not shown) Example 1: “VASP input set” controls the rules that set DFT parameters (pseudopotentials, cutoﬀs, grid densities, etc) via pymatgen! ! Example II: If “stability_check” is enabled, the later parts of the workﬂow are skipped if the structure is determined unstable to save computer time on uninteresting structures! 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.
- atomate allows you to leverage the prior efforts and knowledge of many researchers 21 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 +
- 22 Many research groups have run tens of thousands of materials science workflows with atomate also used by: • Persson research group, UC Berkeley • Ong research group, UC San Diego • Neaton research group, UC Berkeley • Liu research group, Penn State atomate now powers the Materials Project
- Machine learning: the big problem in my view is connecting data to ML algorithms through features 23 Lots of data on complex objects that you want to interrelate Clustering, Regression, Feature extraction, Model-building, etc. Well developed data-mining routines that work only on numbers (ideally ones with high relevance to your problem) Need to transform materials science objects into a set of physically relevant numerical data (“features” or “descriptors”)
- Goal of matminer: connect materials data with data mining algorithms and data visualization libraries 24 Materials databases (e.g., Materials Project, Citrine, MPDS)! Data retrieval Data featurization Data visualization Python ML libraries
- Matminer contains a library of descriptors (developed by us and the community) to use for data mining • Composition-based – various combinations of elemental attributes of the composition • electronegativity, atomic/ionic radii, elemental thermal conductivities, etc – hundreds of these properties are available – Miedema model – Atomic orbital energies and associated HOMO/LUMO states • Structure-based – structure fingerprints (e.g., site fingerprint statistics), Coulomb matrix, Orbital Field Matrix, RDF-based metrics, Coordination number based metrics • Site-based – Order parameters, AGNI, Voronoi indices, etc • Band structure / DOS based – Branch point energy, orbital character at VBM/CBM, band structure “fingerprint”, etc… 25
- 26 An example of using structure-based fingerprints to find similar structures in terms of local environment patterns Order parameter type Distance between LOPs statistics vector d LOP TARGET CuBiSeO Rocksalt (dissimilar) similar structures
- 27 How to try out these codes https://github.com/hackingmaterials/atomate (production ready) (beta release) https://github.com/hackingmaterials/matminer
- 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 – 1-2-1-4 selenides – Jimmy Kuo – atomate – Dr. Kiran Mathew – MatMiner – Dr. Logan Ward • NERSC computing center and staff • Funding: U.S. Department of Energy, Basic Energy Sciences 28 Slides (already) posted to http://www.slideshare.net/anubhavster

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