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
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Combining High-Throughput Computing and Statistical Learning to Develop and Understand New Thermoelectric Compounds
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Combining High-Throughput Computing and Statistical Learning to Develop and Understand New Thermoelectric Compounds
Anubhav JainEnergy Technologies Area
Lawrence Berkeley National LaboratoryBerkeley, CA
MRS Fall 2016
Slides (already) posted to http://www.slideshare.net/anubhavster
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)
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www.alphabetenergy.com
Alphabet Energy – 25kW generator
Thermoelectric figure of merit
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• 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
by previous work by Rode with DFT parameters– ionized impurity scattering– deformation potential scattering– piezoelectric scattering– polar optical phonon
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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).
Transport database
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All data will be made available via upcoming publication as well as on Materials Project• Seebeck• conductivity/tau• effective mass• electronic thermal conductivity
New Materials from screening – TmAgTe2 (calcs)
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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
TmAgTe2 (experiments)
11Zhu, 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
YCuTe2 – friendlier elements, higher zT (0.75)
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• 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
Bournonites – CuPbSbS3 and analogues
• Natural mineral• Measured thermal conductivity for
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
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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)
Variation of properties with substitution
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Variation of properties with substitution
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B and C groups (lone pair sites) require heavier elements for stability (low Eh) – Si and N are very unstable!
Variation of properties with substitution
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As expected, band gaps tend to decrease with heavier anionsThis is due to shifting up of the VBM level
Variation of properties with substitution
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Variation of properties with substitution
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Cu has lowest bandgap because Cu1+ also tends to be very high up in the valence band
AMSET indicates interband scattering is extremely significant – need to confirm
Substitutions listed here are close to thermodynamic stability (<0.05 eV /atom unstable)
Defects – selenide looks slightly better than sulfide
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(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
Open data and software
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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: AMSETComing soon: MatMiner
MatMiner (coming soon)MatMiner’s goal: help enable data mining studies in materials science
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Interactive demo of MatMiner
• Can we create a machine learning model to predict bulk modulus that is accurate to ~20GPa in ~10 mins?