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Title: Machine learning approaches to identify and design low thermal conductivity oxides for thermoelectric applications
Authors: Tewari, Abhishek
Dixit S.
Sahni N.
Bordas S.P.A.
Published in: Data-Centric Engineering
Abstract: The search space for new thermoelectric oxides has been limited to the alloys of a few known systems, such as ZnO, SrTiO3, and CaMnO3. Notwithstanding the high power factor, their high thermal conductivity is a roadblock in achieving higher efficiency. In this paper, we apply machine learning (ML) models for discovering novel transition metal oxides with low lattice thermal conductivity . A two-step process is proposed to address the problem of small datasets frequently encountered in material informatics. First, a gradient-boosted tree classifier is learnt to categorize unknown compounds into three categories of: low, medium, and high. In the second step, we fit regression models on the targeted class (i.e., low) to estimate with an 0.9 $]]>. Gradient boosted tree model was also used to identify key material properties influencing classification of, namely lattice energy per atom, atom density, band gap, mass density, and ratio of oxygen by transition metal atoms. Only fundamental materials properties describing the crystal symmetry, compound chemistry, and interatomic bonding were used in the classification process, which can be readily used in the initial phases of materials design. The proposed two-step process addresses the problem of small datasets and improves the predictive accuracy. The ML approach adopted in the present work is generic in nature and can be combined with high-Throughput computing for the rapid discovery of new materials for specific applications. ©
Citation: Data-Centric Engineering, 1(6)
Issue Date: 2020
Publisher: Cambridge University Press
Keywords: Machine learning
rapid materials discovery
thermal conductivity
ISSN: 26326736
Author Scopus IDs: 7102448636
Author Affiliations: Tewari, A., Department of Metallurgical and Materials Engineering, Indian Institute of Technology Roorkee, Hardiwar, India
Dixit, S., Department of Mathematics, Shiv Nadar University, Gautam Buddha Nagar, India
Sahni, N., Department of Mathematics, Shiv Nadar University, Gautam Buddha Nagar, India
Bordas, S.P.A., Department of Engineering, Institute of Computational Engineering, University of Luxembourg, Esch-sur-Alzette, Luxembourg, Institute of Mechanics and Advanced Materials, School of Engineering, Cardiff University, Cardiff, United Kingdom
Funding Details: This research was supported by grants from the Science and Engineering Research Board, India through project number SRG/2019/000644. In addition, S.P.A. Bordas received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 811099 TWINNING Project DRIVEN for the University of Luxembourg: \url{ } Université du Luxembourg; Horizon 2020 Framework Programme, H2020: 811099; Science and Engineering Research Board, SERB: SRG/2019/000644
Corresponding Author: Tewari, A.; Department of Metallurgical and Materials Engineering, India; email:
Appears in Collections:Journal Publications [MT]

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