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Predicted Optimal Bifunctional Electrocatalysts for the Hydrogen Evolution Reaction and the Oxygen Evolution Reaction Using Chalcogenide Heterostructures Based on Machine Learning Analysis of in Silico Quantum Mechanics Based High Throughput Screening

Ge, Lei and Yuan, Hao and Min, Yuxiang and Li, Li and Chen, Shiqian and Xu, Lai and Goddard, William A., III (2020) Predicted Optimal Bifunctional Electrocatalysts for the Hydrogen Evolution Reaction and the Oxygen Evolution Reaction Using Chalcogenide Heterostructures Based on Machine Learning Analysis of in Silico Quantum Mechanics Based High Throughput Screening. Journal of Physical Chemistry Letters, 11 (3). pp. 869-876. ISSN 1948-7185. https://resolver.caltech.edu/CaltechAUTHORS:20200113-103102011

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Abstract

Two-dimensional van der Waals heterostructure materials, particularly transition metal dichalcogenides (TMDC), have proved to be excellent photoabsorbers for solar radiation, but performance for such electrocatalysis processes as water splitting to form H₂ and O₂ is not adequate. We propose that dramatically improved performance may be achieved by combining two independent TMDC while optimizing such descriptors as rotational angle, bond length, distance between layers, and the ratio of the bandgaps of two component materials. In this paper we apply the least absolute shrinkage and selection operator (LASSO) process of artificial intelligence incorporating these descriptors together with quantum mechanics (density functional theory) to predict novel structures with predicted superior performance. Our predicted best system is MoTe₂/WTe₂ with a rotation of 300°, which is predicted to have an overpotential of 0.03 V for HER and 0.17 V for OER, dramatically improved over current electrocatalysts for water splitting.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1021/acs.jpclett.9b03875DOIArticle
ORCID:
AuthorORCID
Yuan, Hao0000-0003-3323-2855
Li, Li0000-0001-9260-823X
Xu, Lai0000-0003-2473-3359
Goddard, William A., III0000-0003-0097-5716
Alternate Title:Predicted Optimal Bifunctional Electrocatalysts for Both HER and OER Using Chalcogenide Heterostructures Based on Machine Learning Analysis of In Silico Quantum Mechanics Based High Throughput Screening
Additional Information:© 2020 American Chemical Society. Received: December 29, 2019; Accepted: January 11, 2020; Published: January 11, 2020. We acknowledge financial support from National Key R&D Program of China (Grant No. 2017YFB0701600), the National Natural Science Foundation of China (Grant No. 91961120), Caltech-Soochow Multiscale nanoMaterials Genome Center (MnG), Innovative and Entrepreneurial Doctor (World-Famous Universities) in Jiangsu Province, Talent in Demand in the city of Suzhou. This project is also funded by the Collaborative Innovation Center of Suzhou Nano Science & Technology, the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), the 111 Project, and Joint International Research Laboratory of Carbon-Based Functional Materials and Devices. The Caltech studies were supported by the US NSF (CBET-1805022) and the Joint Center for Artificial Photosynthesis, a DOE Energy Innovation Hub, supported through the Office of Science of the U.S. Department of Energy under Award No. DE-SC0004993. The authors declare no competing financial interest.
Group:JCAP
Funders:
Funding AgencyGrant Number
National Key Research and Development Program of China2017YFB0701600
National Natural Science Foundation of China91961120
Caltech-Soochow Multiscale nanoMaterials Genome Center (MnG)UNSPECIFIED
Suzhou Nano Science and TechnologyUNSPECIFIED
Jiangsu Higher Education InstitutionsUNSPECIFIED
111 Project of ChinaUNSPECIFIED
Joint International Research Laboratory of Carbon-Based Functional Materials and DevicesUNSPECIFIED
NSFCBET-1805022
Joint Center for Artificial Photosynthesis (JCAP)UNSPECIFIED
Department of Energy (DOE)DE-SC0004993
Other Numbering System:
Other Numbering System NameOther Numbering System ID
WAG1364
Issue or Number:3
Record Number:CaltechAUTHORS:20200113-103102011
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200113-103102011
Official Citation:Predicted Optimal Bifunctional Electrocatalysts for the Hydrogen Evolution Reaction and the Oxygen Evolution Reaction Using Chalcogenide Heterostructures Based on Machine Learning Analysis of in Silico Quantum Mechanics Based High Throughput Screening. Lei Ge, Hao Yuan, Yuxiang Min, Li Li, Shiqian Chen, Lai Xu, and William A. Goddard III. The Journal of Physical Chemistry Letters 2020 11 (3), 869-876 DOI: 10.1021/acs.jpclett.9b03875
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:100673
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:13 Jan 2020 19:14
Last Modified:11 Feb 2020 23:30

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