Empirical modeling of dopability in diamond-like semiconductors

Miller, Samuel A.; Dylla, Maxwell; Anand, Shashwat; Gordiz, Kiarash; Snyder, G. Jeffrey; Toberer, Eric S.

doi:10.1038/s41524-018-0123-6

Title: Empirical modeling of dopability in diamond-like semiconductors

Journal Article · Thu Dec 06 00:00:00 EST 2018 · npj Computational Materials

DOI:https://doi.org/10.1038/s41524-018-0123-6· OSTI ID:1489329

^[1]; Dylla, Maxwell ^[1]; Anand, Shashwat ^[1]; Gordiz, Kiarash ^[2];

^[1]; Toberer, Eric S. ^[3]

Northwestern Univ., Evanston, IL (United States)
Colorado School of Mines, Golden, CO (United States)
Colorado School of Mines, Golden, CO (United States); National Renewable Energy Lab. (NREL), Golden, CO (United States)

Carrier concentration optimization has been an enduring challenge when developing newly discovered semiconductors for applications (e.g., thermoelectrics, transparent conductors, photovoltaics). This barrier has been particularly pernicious in the realm of high-throughput property prediction, where the carrier concentration is often assumed to be a free parameter and the limits are not predicted due to the high computational cost. In this work, we explore the application of machine learning for high-throughput carrier concentration range prediction. Bounding the model within diamond-like semiconductors, the learning set was developed from experimental carrier concentration data on 127 compounds ranging from unary to quaternary. The data were analyzed using various statistical and machine learning methods. Accurate predictions of carrier concentration ranges in diamond-like semiconductors are made within approximately one order of magnitude on average across both p- and n-type dopability. The model fit to empirical data is analyzed to understand what drives trends in carrier concentration and compared with previous computational efforts. Lastly, dopability predictions from this model are combined with high-throughput quality factor predictions to identify promising thermoelectric materials.

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Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: AC36-08GO28308

OSTI ID:: 1489329

Report Number(s):: NREL/JA-5K00-73016

Journal Information:: npj Computational Materials, Vol. 4, Issue 1; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 21 works

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References (68)

Accurate force field for molybdenum by machine learning large materials data Chen, Chi; Deng, Zhi; Tran, Richard Physical Review Materials, Vol. 1, Issue 4 https://doi.org/10.1103/PhysRevMaterials.1.043603	journal	September 2017
A phenomenological model for systematization and prediction of doping limits in II–VI and I–III–VI2 compounds Zhang, S. B.; Wei, Su-Huai; Zunger, Alex Journal of Applied Physics, Vol. 83, Issue 6 https://doi.org/10.1063/1.367120	journal	March 1998
Doping Rules and Doping Prototypes in A2BO4 Spinel Oxides Paudel, Tula R.; Zakutayev, Andriy; Lany, Stephan Advanced Functional Materials, Vol. 21, Issue 23 https://doi.org/10.1002/adfm.201101469	journal	October 2011
A strategy to apply machine learning to small datasets in materials science Zhang, Ying; Ling, Chen npj Computational Materials, Vol. 4, Issue 1 https://doi.org/10.1038/s41524-018-0081-z	journal	May 2018
On the Prediction of Ternary Semiconductor Properties by Artificial Intelligence Methods Zeng, Yingzhi; Chua, Soo Jin; Wu, Ping Chemistry of Materials, Vol. 14, Issue 7 https://doi.org/10.1021/cm0103996	journal	July 2002
Polarization doping for III-nitride optoelectronics: Polarization doping for III-nitride optoelectronics Khokhlev, Oleg V.; Bulashevich, Kirill A.; Karpov, Sergey Yu. physica status solidi (a), Vol. 210, Issue 7 https://doi.org/10.1002/pssa.201228614	journal	March 2013
Nanotube electronics and optoelectronics Avouris, Phaedon; Chen, Jia Materials Today, Vol. 9, Issue 10 https://doi.org/10.1016/S1369-7021(06)71653-4	journal	October 2006
Practical doping principles Zunger, Alex Applied Physics Letters, Vol. 83, Issue 1 https://doi.org/10.1063/1.1584074	journal	July 2003
High-throughput screening of bimetallic catalysts enabled by machine learning Li, Zheng; Wang, Siwen; Chin, Wei Shan Journal of Materials Chemistry A, Vol. 5, Issue 46 https://doi.org/10.1039/C7TA01812F	journal	January 2017
Charting the complete elastic properties of inorganic crystalline compounds de Jong, Maarten; Chen, Wei; Angsten, Thomas Scientific Data, Vol. 2, Issue 1 https://doi.org/10.1038/sdata.2015.9	journal	March 2015
Complex thermoelectric materials Snyder, G. Jeffrey; Toberer, Eric S. Nature Materials, Vol. 7, Issue 2, p. 105-114 https://doi.org/10.1038/nmat2090	journal	February 2008
Mining Materials Design Rules from Data: The Example of Polymer Dielectrics Mannodi-Kanakkithodi, Arun; Huan, Tran Doan; Ramprasad, Rampi Chemistry of Materials, Vol. 29, Issue 21 https://doi.org/10.1021/acs.chemmater.7b02027	journal	October 2017
Informatics-aided bandgap engineering for solar materials Dey, Partha; Bible, Joe; Datta, Somnath Computational Materials Science, Vol. 83 https://doi.org/10.1016/j.commatsci.2013.10.016	journal	February 2014
Hydrogen as a Cause of Doping in Zinc Oxide Van de Walle, Chris G. Physical Review Letters, Vol. 85, Issue 5 https://doi.org/10.1103/PhysRevLett.85.1012	journal	July 2000
Machine-Learning-Augmented Chemisorption Model for CO ₂ Electroreduction Catalyst Screening Ma, Xianfeng; Li, Zheng; Achenie, Luke E. K. The Journal of Physical Chemistry Letters, Vol. 6, Issue 18 https://doi.org/10.1021/acs.jpclett.5b01660	journal	August 2015
Intrinsic limitations to the doping of wide-gap semiconductors Walukiewicz, W. Physica B: Condensed Matter, Vol. 302-303 https://doi.org/10.1016/S0921-4526(01)00417-3	journal	January 2001
Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces Li, Zhenwei; Kermode, James R.; De Vita, Alessandro Physical Review Letters, Vol. 114, Issue 9 https://doi.org/10.1103/PhysRevLett.114.096405	journal	March 2015
Ultralow Thermal Conductivity in Diamond-Like Semiconductors: Selective Scattering of Phonons from Antisite Defects Ortiz, Brenden R.; Peng, Wanyue; Gomes, Lídia C. Chemistry of Materials, Vol. 30, Issue 10 https://doi.org/10.1021/acs.chemmater.8b00890	journal	May 2018
Doping of Materials During Manufacture <I>p</I>–<I>n</I>-Junctions and Bipolar Transistors. Analytical Approaches to Model Technological Approaches and Ways of Optimization of Distributions of Dopants Pankratov, E. L.; Bulaeva, E. A. Reviews in Theoretical Science, Vol. 1, Issue 1 https://doi.org/10.1166/rits.2013.1004	journal	March 2013
Investigation of thermoelectric properties of Cu2GaxSn1−xSe3 diamond-like compounds by hot pressing and spark plasma sintering Fan, Jing; Liu, Huili; Shi, Xiaoya Acta Materialia, Vol. 61, Issue 11 https://doi.org/10.1016/j.actamat.2013.04.003	journal	June 2013
Doping limitations in wide gap II–VI compounds by Fermi level pinning Faschinger, W.; Ferreira, S.; Sitter, H. Journal of Crystal Growth, Vol. 151, Issue 3-4 https://doi.org/10.1016/0022-0248(95)00068-2	journal	June 1995
Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning Medasani, Bharat; Gamst, Anthony; Ding, Hong npj Computational Materials, Vol. 2, Issue 1 https://doi.org/10.1038/s41524-016-0001-z	journal	December 2016
A database to enable discovery and design of piezoelectric materials de Jong, Maarten; Chen, Wei; Geerlings, Henry Scientific Data, Vol. 2, Issue 1 https://doi.org/10.1038/sdata.2015.53	journal	September 2015
Overcoming doping bottlenecks in semiconductors and wide-gap materials Zhang, S. B.; Wei, S. -H; Zunger, A. Physica B: Condensed Matter, Vol. 273-274 https://doi.org/10.1016/S0921-4526(99)00605-5	journal	December 1999
Origins of the doping asymmetry in oxides: Hole doping in NiO versus electron doping in ZnO Lany, Stephan; Osorio-Guillén, Jorge; Zunger, Alex Physical Review B, Vol. 75, Issue 24 https://doi.org/10.1103/PhysRevB.75.241203	journal	June 2007
Ternary compound CuInTe2: a promising thermoelectric material with diamond-like structure Liu, Ruiheng; Xi, Lili; Liu, Huili Chemical Communications, Vol. 48, Issue 32 https://doi.org/10.1039/c2cc30318c	journal	January 2012
An 18.2%-efficient black-silicon solar cell achieved through control of carrier recombination in nanostructures Oh, Jihun; Yuan, Hao-Chih; Branz, Howard M. Nature Nanotechnology, Vol. 7, Issue 11 https://doi.org/10.1038/nnano.2012.166	journal	September 2012
High-throughput computational screening of thermal conductivity, Debye temperature, and Grüneisen parameter using a quasiharmonic Debye model Toher, Cormac; Plata, Jose J.; Levy, Ohad Physical Review B, Vol. 90, Issue 17 https://doi.org/10.1103/PhysRevB.90.174107	journal	November 2014
Mechanism of Schottky barrier formation: The role of amphoteric native defects Walukiewicz, W. Journal of Vacuum Science & Technology B: Microelectronics and Nanometer Structures, Vol. 5, Issue 4 https://doi.org/10.1116/1.583729	journal	July 1987
Engineered doping of organic semiconductors for enhanced thermoelectric efficiency Kim, G-H.; Shao, L.; Zhang, K. Nature Materials, Vol. 12, Issue 8 https://doi.org/10.1038/nmat3635	journal	May 2013
Optimization of thermoelectric efficiency in SnTe: the case for the light band Zhou, Min; Gibbs, Zachary M.; Wang, Heng Phys. Chem. Chem. Phys., Vol. 16, Issue 38 https://doi.org/10.1039/C4CP02091J	journal	January 2014
First-principles calculations for defects and impurities: Applications to III-nitrides Van de Walle, Chris G.; Neugebauer, Jörg Journal of Applied Physics, Vol. 95, Issue 8 https://doi.org/10.1063/1.1682673	journal	April 2004
Controlling doping and carrier injection in carbon nanotube transistors Derycke, V.; Martel, R.; Appenzeller, J. Applied Physics Letters, Vol. 80, Issue 15 https://doi.org/10.1063/1.1467702	journal	April 2002
Zintl Chemistry for Designing High Efficiency Thermoelectric Materials ^† ^‡ Toberer, Eric S.; May, Andrew F.; Snyder, G. Jeffrey Chemistry of Materials, Vol. 22, Issue 3 https://doi.org/10.1021/cm901956r	journal	February 2010
Phase Boundary Mapping to Obtain n-type Mg3Sb2-Based Thermoelectrics Ohno, Saneyuki; Imasato, Kazuki; Anand, Shashwat Joule, Vol. 2, Issue 1 https://doi.org/10.1016/j.joule.2017.11.005	journal	January 2018
The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies Kirklin, Scott; Saal, James E.; Meredig, Bryce npj Computational Materials, Vol. 1, Issue 1 https://doi.org/10.1038/npjcompumats.2015.10	journal	December 2015
How Chemical Composition Alone Can Predict Vibrational Free Energies and Entropies of Solids Legrain, Fleur; Carrete, Jesús; van Roekeghem, Ambroise Chemistry of Materials, Vol. 29, Issue 15 https://doi.org/10.1021/acs.chemmater.7b00789	journal	July 2017
Fabrication of 7.2% Efficient CZTSSe Solar Cells Using CZTS Nanocrystals Guo, Qijie; Ford, Grayson M.; Yang, Wei-Chang Journal of the American Chemical Society, Vol. 132, Issue 49 https://doi.org/10.1021/ja108427b	journal	December 2010
Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials Sendek, Austin D.; Yang, Qian; Cubuk, Ekin D. Energy & Environmental Science, Vol. 10, Issue 1 https://doi.org/10.1039/C6EE02697D	journal	January 2017
Machine learning bandgaps of double perovskites Pilania, G.; Mannodi-Kanakkithodi, A.; Uberuaga, B. P. Scientific Reports, Vol. 6, Issue 1 https://doi.org/10.1038/srep19375	journal	January 2016
Stabilizing the Optimal Carrier Concentration for High Thermoelectric Efficiency Pei, Yanzhong; LaLonde, Aaron D.; Heinz, Nicholas A. Advanced Materials, Vol. 23, Issue 47 https://doi.org/10.1002/adma.201103153	journal	November 2011
Material descriptors for predicting thermoelectric performance Yan, Jun; Gorai, Prashun; Ortiz, Brenden Energy & Environmental Science, Vol. 8, Issue 3 https://doi.org/10.1039/C4EE03157A	journal	January 2015
Eco-Friendly SnTe Thermoelectric Materials: Progress and Future Challenges Moshwan, Raza; Yang, Lei; Zou, Jin Advanced Functional Materials, Vol. 27, Issue 43 https://doi.org/10.1002/adfm.201703278	journal	September 2017
Doping-induced efficiency enhancement in organic photovoltaic devices Chan, M. Y.; Lai, S. L.; Fung, M. K. Applied Physics Letters, Vol. 90, Issue 2 https://doi.org/10.1063/1.2430783	journal	January 2007
Capturing Anharmonicity in a Lattice Thermal Conductivity Model for High-Throughput Predictions Miller, Samuel A.; Gorai, Prashun; Ortiz, Brenden R. Chemistry of Materials, Vol. 29, Issue 6 https://doi.org/10.1021/acs.chemmater.6b04179	journal	November 2016
Synthesis of CuInS ₂ , CuInSe ₂ , and Cu(In _x Ga _{1- x} )Se ₂ (CIGS) Nanocrystal “Inks” for Printable Photovoltaics Panthani, Matthew G.; Akhavan, Vahid; Goodfellow, Brian Journal of the American Chemical Society, Vol. 130, Issue 49 https://doi.org/10.1021/ja805845q	journal	December 2008
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation Jain, Anubhav; Ong, Shyue Ping; Hautier, Geoffroy APL Materials, Vol. 1, Issue 1 https://doi.org/10.1063/1.4812323	journal	July 2013
Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD) Saal, James E.; Kirklin, Scott; Aykol, Muratahan JOM, Vol. 65, Issue 11 https://doi.org/10.1007/s11837-013-0755-4	journal	September 2013
Big Data of Materials Science: Critical Role of the Descriptor Ghiringhelli, Luca M.; Vybiral, Jan; Levchenko, Sergey V. Physical Review Letters, Vol. 114, Issue 10 https://doi.org/10.1103/PhysRevLett.114.105503	journal	March 2015
High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds Oliynyk, Anton O.; Antono, Erin; Sparks, Taylor D. Chemistry of Materials, Vol. 28, Issue 20 https://doi.org/10.1021/acs.chemmater.6b02724	journal	October 2016
Virtual screening of inorganic materials synthesis parameters with deep learning Kim, Edward; Huang, Kevin; Jegelka, Stefanie npj Computational Materials, Vol. 3, Issue 1 https://doi.org/10.1038/s41524-017-0055-6	journal	December 2017
TE Design Lab: A virtual laboratory for thermoelectric material design Gorai, Prashun; Gao, Duanfeng; Ortiz, Brenden Computational Materials Science, Vol. 112 https://doi.org/10.1016/j.commatsci.2015.11.006	journal	February 2016
Advances in Environment-Friendly SnTe Thermoelectrics Li, Wen; Wu, Yixuan; Lin, Siqi ACS Energy Letters, Vol. 2, Issue 10 https://doi.org/10.1021/acsenergylett.7b00658	journal	September 2017
Machine-learning-assisted materials discovery using failed experiments Raccuglia, Paul; Elbert, Katherine C.; Adler, Philip D. F. Nature, Vol. 533, Issue 7601 https://doi.org/10.1038/nature17439	journal	May 2016
Universal alignment of hydrogen levels in semiconductors, insulators and solutions Van de Walle, Chris G.; Neugebauer, J. Nature, Vol. 423, Issue 6940 https://doi.org/10.1038/nature01665	journal	June 2003
Ionization Interaction between Impurities in Semiconductors and Insulators Longini, R. L.; Greene, R. F. Physical Review, Vol. 102, Issue 4 https://doi.org/10.1103/PhysRev.102.992	journal	May 1956
First-principles study of anisotropic thermoelectric transport properties of IV-VI semiconductor compounds SnSe and SnS Guo, Ruiqiang; Wang, Xinjiang; Kuang, Youdi Physical Review B, Vol. 92, Issue 11 https://doi.org/10.1103/PhysRevB.92.115202	journal	September 2015
Machine-learned and codified synthesis parameters of oxide materials Kim, Edward; Huang, Kevin; Tomala, Alex Scientific Data, Vol. 4, Issue 1 https://doi.org/10.1038/sdata.2017.127	journal	September 2017
A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds de Jong, Maarten; Chen, Wei; Notestine, Randy Scientific Reports, Vol. 6, Issue 1 https://doi.org/10.1038/srep34256	journal	October 2016
Thermoelectric SnS and SnS-SnSe solid solutions prepared by mechanical alloying and spark plasma sintering: Anisotropic thermoelectric properties Scientific Reports, Vol. 7, Issue 1 https://doi.org/10.1038/srep43262	journal	February 2017
Complex thermoelectric materials Snyder, G. Jeffrey; Toberer, Eric S. Materials for Sustainable Energy https://doi.org/10.1142/9789814317665_0016	book	October 2010
A Dual-Bonded Approach for Improving Hydrogel Implant Stability in Cartilage Defects Liu, Yan; Wu, Yuxuan; Zhou, Lei Materials, Vol. 10, Issue 2 https://doi.org/10.3390/ma10020191	journal	February 2017
High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds Oliynyk, Ao; Antono, E.; Sparks, Td Apollo - University of Cambridge Repository https://doi.org/10.17863/cam.9378	text	January 2016
The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies Kirklin, Scott; Saal, James E.; Meredig, Bryce London : Nature Publ. Group https://doi.org/10.34657/7521	text	January 2015
Big Data of Materials Science - Critical Role of the Descriptor Ghiringhelli, Luca M.; Vybiral, Jan; Levchenko, Sergey V. arXiv https://doi.org/10.48550/arxiv.1411.7437	text	January 2014
First-principles study of anisotropic thermoelectric transport properties of IV-VI semiconductor compounds SnSe and SnS Guo, Ruiqiang; Wang, Xinjiang; Kuang, Youdi arXiv https://doi.org/10.48550/arxiv.1505.02601	text	January 2015
Representation of compounds for machine-learning prediction of physical properties Seko, Atsuto; Hayashi, Hiroyuki; Nakayama, Keita arXiv https://doi.org/10.48550/arxiv.1611.08645	text	January 2016
Accurate Force Field for Molybdenum by Machine Learning Large Materials Data Chen, Chi; Deng, Zhi; Tran, Richard arXiv https://doi.org/10.48550/arxiv.1706.09122	text	January 2017

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