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. doi:10.1021/acs.jpclett.9b03875. 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.
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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 | ||||||||||||||||||||||
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Issue or Number: | 3 | ||||||||||||||||||||||
DOI: | 10.1021/acs.jpclett.9b03875 | ||||||||||||||||||||||
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: | 16 Nov 2021 17:56 |
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