Empirical modeling of dopability in diamond-like semiconductors
Abstract
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.
- Authors:
-
- 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)
- Publication Date:
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE)
- OSTI Identifier:
- 1489329
- Report Number(s):
- NREL/JA-5K00-73016
Journal ID: ISSN 2057-3960
- Grant/Contract Number:
- AC36-08GO28308
- Resource Type:
- Accepted Manuscript
- Journal Name:
- npj Computational Materials
- Additional Journal Information:
- Journal Volume: 4; Journal Issue: 1; Journal ID: ISSN 2057-3960
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; computational methods; thermoelectrics
Citation Formats
Miller, Samuel A., Dylla, Maxwell, Anand, Shashwat, Gordiz, Kiarash, Snyder, G. Jeffrey, and Toberer, Eric S. Empirical modeling of dopability in diamond-like semiconductors. United States: N. p., 2018.
Web. doi:10.1038/s41524-018-0123-6.
Miller, Samuel A., Dylla, Maxwell, Anand, Shashwat, Gordiz, Kiarash, Snyder, G. Jeffrey, & Toberer, Eric S. Empirical modeling of dopability in diamond-like semiconductors. United States. https://doi.org/10.1038/s41524-018-0123-6
Miller, Samuel A., Dylla, Maxwell, Anand, Shashwat, Gordiz, Kiarash, Snyder, G. Jeffrey, and Toberer, Eric S. Thu .
"Empirical modeling of dopability in diamond-like semiconductors". United States. https://doi.org/10.1038/s41524-018-0123-6. https://www.osti.gov/servlets/purl/1489329.
@article{osti_1489329,
title = {Empirical modeling of dopability in diamond-like semiconductors},
author = {Miller, Samuel A. and Dylla, Maxwell and Anand, Shashwat and Gordiz, Kiarash and Snyder, G. Jeffrey and Toberer, Eric S.},
abstractNote = {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.},
doi = {10.1038/s41524-018-0123-6},
journal = {npj Computational Materials},
number = 1,
volume = 4,
place = {United States},
year = {Thu Dec 06 00:00:00 EST 2018},
month = {Thu Dec 06 00:00:00 EST 2018}
}
Web of Science
Figures / Tables:
Works referenced in this record:
Accurate force field for molybdenum by machine learning large materials data
journal, September 2017
- Chen, Chi; Deng, Zhi; Tran, Richard
- Physical Review Materials, Vol. 1, Issue 4
A phenomenological model for systematization and prediction of doping limits in II–VI and I–III–VI2 compounds
journal, March 1998
- Zhang, S. B.; Wei, Su-Huai; Zunger, Alex
- Journal of Applied Physics, Vol. 83, Issue 6
Doping Rules and Doping Prototypes in A2BO4 Spinel Oxides
journal, October 2011
- Paudel, Tula R.; Zakutayev, Andriy; Lany, Stephan
- Advanced Functional Materials, Vol. 21, Issue 23
A strategy to apply machine learning to small datasets in materials science
journal, May 2018
- Zhang, Ying; Ling, Chen
- npj Computational Materials, Vol. 4, Issue 1
On the Prediction of Ternary Semiconductor Properties by Artificial Intelligence Methods
journal, July 2002
- Zeng, Yingzhi; Chua, Soo Jin; Wu, Ping
- Chemistry of Materials, Vol. 14, Issue 7
Polarization doping for III-nitride optoelectronics: Polarization doping for III-nitride optoelectronics
journal, March 2013
- Khokhlev, Oleg V.; Bulashevich, Kirill A.; Karpov, Sergey Yu.
- physica status solidi (a), Vol. 210, Issue 7
Nanotube electronics and optoelectronics
journal, October 2006
- Avouris, Phaedon; Chen, Jia
- Materials Today, Vol. 9, Issue 10
Practical doping principles
journal, July 2003
- Zunger, Alex
- Applied Physics Letters, Vol. 83, Issue 1
High-throughput screening of bimetallic catalysts enabled by machine learning
journal, January 2017
- Li, Zheng; Wang, Siwen; Chin, Wei Shan
- Journal of Materials Chemistry A, Vol. 5, Issue 46
Charting the complete elastic properties of inorganic crystalline compounds
journal, March 2015
- de Jong, Maarten; Chen, Wei; Angsten, Thomas
- Scientific Data, Vol. 2, Issue 1
Complex thermoelectric materials
journal, February 2008
- Snyder, G. Jeffrey; Toberer, Eric S.
- Nature Materials, Vol. 7, Issue 2, p. 105-114
Mining Materials Design Rules from Data: The Example of Polymer Dielectrics
journal, October 2017
- Mannodi-Kanakkithodi, Arun; Huan, Tran Doan; Ramprasad, Rampi
- Chemistry of Materials, Vol. 29, Issue 21
Informatics-aided bandgap engineering for solar materials
journal, February 2014
- Dey, Partha; Bible, Joe; Datta, Somnath
- Computational Materials Science, Vol. 83
Hydrogen as a Cause of Doping in Zinc Oxide
journal, July 2000
- Van de Walle, Chris G.
- Physical Review Letters, Vol. 85, Issue 5
Machine-Learning-Augmented Chemisorption Model for CO 2 Electroreduction Catalyst Screening
journal, August 2015
- Ma, Xianfeng; Li, Zheng; Achenie, Luke E. K.
- The Journal of Physical Chemistry Letters, Vol. 6, Issue 18
Intrinsic limitations to the doping of wide-gap semiconductors
journal, January 2001
- Walukiewicz, W.
- Physica B: Condensed Matter, Vol. 302-303
Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces
journal, March 2015
- Li, Zhenwei; Kermode, James R.; De Vita, Alessandro
- Physical Review Letters, Vol. 114, Issue 9
Ultralow Thermal Conductivity in Diamond-Like Semiconductors: Selective Scattering of Phonons from Antisite Defects
journal, May 2018
- Ortiz, Brenden R.; Peng, Wanyue; Gomes, Lídia C.
- Chemistry of Materials, Vol. 30, Issue 10
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
journal, March 2013
- Pankratov, E. L.; Bulaeva, E. A.
- Reviews in Theoretical Science, Vol. 1, Issue 1
Investigation of thermoelectric properties of Cu2GaxSn1−xSe3 diamond-like compounds by hot pressing and spark plasma sintering
journal, June 2013
- Fan, Jing; Liu, Huili; Shi, Xiaoya
- Acta Materialia, Vol. 61, Issue 11
Doping limitations in wide gap II–VI compounds by Fermi level pinning
journal, June 1995
- Faschinger, W.; Ferreira, S.; Sitter, H.
- Journal of Crystal Growth, Vol. 151, Issue 3-4
Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning
journal, December 2016
- Medasani, Bharat; Gamst, Anthony; Ding, Hong
- npj Computational Materials, Vol. 2, Issue 1
A database to enable discovery and design of piezoelectric materials
journal, September 2015
- de Jong, Maarten; Chen, Wei; Geerlings, Henry
- Scientific Data, Vol. 2, Issue 1
Overcoming doping bottlenecks in semiconductors and wide-gap materials
journal, December 1999
- Zhang, S. B.; Wei, S. -H; Zunger, A.
- Physica B: Condensed Matter, Vol. 273-274
Origins of the doping asymmetry in oxides: Hole doping in NiO versus electron doping in ZnO
journal, June 2007
- Lany, Stephan; Osorio-Guillén, Jorge; Zunger, Alex
- Physical Review B, Vol. 75, Issue 24
Ternary compound CuInTe2: a promising thermoelectric material with diamond-like structure
journal, January 2012
- Liu, Ruiheng; Xi, Lili; Liu, Huili
- Chemical Communications, Vol. 48, Issue 32
An 18.2%-efficient black-silicon solar cell achieved through control of carrier recombination in nanostructures
journal, September 2012
- Oh, Jihun; Yuan, Hao-Chih; Branz, Howard M.
- Nature Nanotechnology, Vol. 7, Issue 11
High-throughput computational screening of thermal conductivity, Debye temperature, and Grüneisen parameter using a quasiharmonic Debye model
journal, November 2014
- Toher, Cormac; Plata, Jose J.; Levy, Ohad
- Physical Review B, Vol. 90, Issue 17
Mechanism of Schottky barrier formation: The role of amphoteric native defects
journal, July 1987
- Walukiewicz, W.
- Journal of Vacuum Science & Technology B: Microelectronics and Nanometer Structures, Vol. 5, Issue 4
Engineered doping of organic semiconductors for enhanced thermoelectric efficiency
journal, May 2013
- Kim, G-H.; Shao, L.; Zhang, K.
- Nature Materials, Vol. 12, Issue 8
Optimization of thermoelectric efficiency in SnTe: the case for the light band
journal, January 2014
- Zhou, Min; Gibbs, Zachary M.; Wang, Heng
- Phys. Chem. Chem. Phys., Vol. 16, Issue 38
First-principles calculations for defects and impurities: Applications to III-nitrides
journal, April 2004
- Van de Walle, Chris G.; Neugebauer, Jörg
- Journal of Applied Physics, Vol. 95, Issue 8
Controlling doping and carrier injection in carbon nanotube transistors
journal, April 2002
- Derycke, V.; Martel, R.; Appenzeller, J.
- Applied Physics Letters, Vol. 80, Issue 15
Zintl Chemistry for Designing High Efficiency Thermoelectric Materials † ‡
journal, February 2010
- Toberer, Eric S.; May, Andrew F.; Snyder, G. Jeffrey
- Chemistry of Materials, Vol. 22, Issue 3
Phase Boundary Mapping to Obtain n-type Mg3Sb2-Based Thermoelectrics
journal, January 2018
- Ohno, Saneyuki; Imasato, Kazuki; Anand, Shashwat
- Joule, Vol. 2, Issue 1
The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies
journal, December 2015
- Kirklin, Scott; Saal, James E.; Meredig, Bryce
- npj Computational Materials, Vol. 1, Issue 1
How Chemical Composition Alone Can Predict Vibrational Free Energies and Entropies of Solids
journal, July 2017
- Legrain, Fleur; Carrete, Jesús; van Roekeghem, Ambroise
- Chemistry of Materials, Vol. 29, Issue 15
Fabrication of 7.2% Efficient CZTSSe Solar Cells Using CZTS Nanocrystals
journal, December 2010
- Guo, Qijie; Ford, Grayson M.; Yang, Wei-Chang
- Journal of the American Chemical Society, Vol. 132, Issue 49
Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials
journal, January 2017
- Sendek, Austin D.; Yang, Qian; Cubuk, Ekin D.
- Energy & Environmental Science, Vol. 10, Issue 1
Machine learning bandgaps of double perovskites
journal, January 2016
- Pilania, G.; Mannodi-Kanakkithodi, A.; Uberuaga, B. P.
- Scientific Reports, Vol. 6, Issue 1
Stabilizing the Optimal Carrier Concentration for High Thermoelectric Efficiency
journal, November 2011
- Pei, Yanzhong; LaLonde, Aaron D.; Heinz, Nicholas A.
- Advanced Materials, Vol. 23, Issue 47
Material descriptors for predicting thermoelectric performance
journal, January 2015
- Yan, Jun; Gorai, Prashun; Ortiz, Brenden
- Energy & Environmental Science, Vol. 8, Issue 3
Eco-Friendly SnTe Thermoelectric Materials: Progress and Future Challenges
journal, September 2017
- Moshwan, Raza; Yang, Lei; Zou, Jin
- Advanced Functional Materials, Vol. 27, Issue 43
Doping-induced efficiency enhancement in organic photovoltaic devices
journal, January 2007
- Chan, M. Y.; Lai, S. L.; Fung, M. K.
- Applied Physics Letters, Vol. 90, Issue 2
Capturing Anharmonicity in a Lattice Thermal Conductivity Model for High-Throughput Predictions
journal, November 2016
- Miller, Samuel A.; Gorai, Prashun; Ortiz, Brenden R.
- Chemistry of Materials, Vol. 29, Issue 6
Synthesis of CuInS 2 , CuInSe 2 , and Cu(In x Ga 1- x )Se 2 (CIGS) Nanocrystal “Inks” for Printable Photovoltaics
journal, December 2008
- Panthani, Matthew G.; Akhavan, Vahid; Goodfellow, Brian
- Journal of the American Chemical Society, Vol. 130, Issue 49
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
journal, July 2013
- Jain, Anubhav; Ong, Shyue Ping; Hautier, Geoffroy
- APL Materials, Vol. 1, Issue 1
Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)
journal, September 2013
- Saal, James E.; Kirklin, Scott; Aykol, Muratahan
- JOM, Vol. 65, Issue 11
Big Data of Materials Science: Critical Role of the Descriptor
journal, March 2015
- Ghiringhelli, Luca M.; Vybiral, Jan; Levchenko, Sergey V.
- Physical Review Letters, Vol. 114, Issue 10
High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds
journal, October 2016
- Oliynyk, Anton O.; Antono, Erin; Sparks, Taylor D.
- Chemistry of Materials, Vol. 28, Issue 20
Virtual screening of inorganic materials synthesis parameters with deep learning
journal, December 2017
- Kim, Edward; Huang, Kevin; Jegelka, Stefanie
- npj Computational Materials, Vol. 3, Issue 1
TE Design Lab: A virtual laboratory for thermoelectric material design
journal, February 2016
- Gorai, Prashun; Gao, Duanfeng; Ortiz, Brenden
- Computational Materials Science, Vol. 112
Advances in Environment-Friendly SnTe Thermoelectrics
journal, September 2017
- Li, Wen; Wu, Yixuan; Lin, Siqi
- ACS Energy Letters, Vol. 2, Issue 10
Machine-learning-assisted materials discovery using failed experiments
journal, May 2016
- Raccuglia, Paul; Elbert, Katherine C.; Adler, Philip D. F.
- Nature, Vol. 533, Issue 7601
Universal alignment of hydrogen levels in semiconductors, insulators and solutions
journal, June 2003
- Van de Walle, Chris G.; Neugebauer, J.
- Nature, Vol. 423, Issue 6940
Ionization Interaction between Impurities in Semiconductors and Insulators
journal, May 1956
- Longini, R. L.; Greene, R. F.
- Physical Review, Vol. 102, Issue 4
First-principles study of anisotropic thermoelectric transport properties of IV-VI semiconductor compounds SnSe and SnS
journal, September 2015
- Guo, Ruiqiang; Wang, Xinjiang; Kuang, Youdi
- Physical Review B, Vol. 92, Issue 11
Machine-learned and codified synthesis parameters of oxide materials
journal, September 2017
- Kim, Edward; Huang, Kevin; Tomala, Alex
- Scientific Data, Vol. 4, Issue 1
A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds
journal, October 2016
- de Jong, Maarten; Chen, Wei; Notestine, Randy
- Scientific Reports, Vol. 6, Issue 1
Thermoelectric SnS and SnS-SnSe solid solutions prepared by mechanical alloying and spark plasma sintering: Anisotropic thermoelectric properties
journal, February 2017
- Asfandiyar, ; Wei, Tian-Ran; Li, Zhiliang
- Scientific Reports, Vol. 7, Issue 1
Complex thermoelectric materials
book, October 2010
- Snyder, G. Jeffrey; Toberer, Eric S.
- Materials for Sustainable Energy
A Dual-Bonded Approach for Improving Hydrogel Implant Stability in Cartilage Defects
journal, February 2017
- Liu, Yan; Wu, Yuxuan; Zhou, Lei
- Materials, Vol. 10, Issue 2
High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds
text, January 2016
- Oliynyk, Ao; Antono, E.; Sparks, Td
- Apollo - University of Cambridge Repository
The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies
text, January 2015
- Kirklin, Scott; Saal, James E.; Meredig, Bryce
- London : Nature Publ. Group
Big Data of Materials Science - Critical Role of the Descriptor
text, January 2014
- Ghiringhelli, Luca M.; Vybiral, Jan; Levchenko, Sergey V.
- arXiv
First-principles study of anisotropic thermoelectric transport properties of IV-VI semiconductor compounds SnSe and SnS
text, January 2015
- Guo, Ruiqiang; Wang, Xinjiang; Kuang, Youdi
- arXiv
Representation of compounds for machine-learning prediction of physical properties
text, January 2016
- Seko, Atsuto; Hayashi, Hiroyuki; Nakayama, Keita
- arXiv
Accurate Force Field for Molybdenum by Machine Learning Large Materials Data
text, January 2017
- Chen, Chi; Deng, Zhi; Tran, Richard
- arXiv
Works referencing / citing this record:
Machine Learning Approaches for Thermoelectric Materials Research
journal, November 2019
- Wang, Tian; Zhang, Cheng; Snoussi, Hichem
- Advanced Functional Materials, Vol. 30, Issue 5
Inertial effective mass as an effective descriptor for thermoelectrics via data-driven evaluation
journal, January 2019
- Suwardi, Ady; Bash, Daniil; Ng, Hong Kuan
- Journal of Materials Chemistry A, Vol. 7, Issue 41
Figures / Tables found in this record: