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; Yuan, Hao; Min, Yuxiang; Li, Li; Chen, Shiqian; Xu, Lai; Goddard, William A.

doi:10.1021/acs.jpclett.9b03875

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 Article · Sat Jan 11 00:00:00 EST 2020 · Journal of Physical Chemistry Letters

DOI:https://doi.org/10.1021/acs.jpclett.9b03875· OSTI ID:1801619

Ge, Lei ^[1]; ^[2]; Min, Yuxiang ^[2]; ^[2]; Chen, Shiqian ^[2]; ^[2]; ^[3]

Soochow University, Suzhou (China); OSTI
Soochow University, Suzhou (China)
California Institute of Technology (CalTech), Pasadena, CA (United States)

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 work 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.

View Accepted Manuscript (DOE)

Research Organization:: California Institute of Technology (CalTech), Pasadena, CA (United States)

Sponsoring Organization:: National Key R&D Program of China; National Natural Science Foundation of China (NSFC); National Science Foundation (NSF); USDOE Office of Science (SC)

Grant/Contract Number:: SC0004993

OSTI ID:: 1801619

Journal Information:: Journal of Physical Chemistry Letters, Journal Name: Journal of Physical Chemistry Letters Journal Issue: 3 Vol. 11; ISSN 1948-7185

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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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

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