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

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 can provide. 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 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.

An artificial intelligence-aided virtual screening recipe for two-dimensional materials discovery

“…An exciting arena of materials design is the development of new functional materials for energy conversion and storage. Multi-scale high-throughput computational screening studies have recently been utilized as systematic approaches 31,32 in order to accelerate the discovery of new 2D energy materials for photovoltaics [33][34][35] as well as photocatalytic solar fuel generation through the conversion of feedstock molecules, including H 2 O 12,31,[36][37][38] , CO 2 37,39,40 , and N 2 37 . To illustrate the use of AI methods for the virtual screening of candidate 2D materials, Fig.…”

Transition Metal Dichalcogenides for the Application of Pollution Reduction: A Review

“…The results obtained from the experiment can be validated by conducting techno-economic analysis, such as Monte Carlo analysis for economic and risk feasibility [289,290]. Furthermore, advanced machine learning methods can also be used to accelerate experimental designs [291,292], obtain optimal performances [293,294], and for the discovery of new TMDCs composite structures [295,296].…”

Development Status and Prospects of Artificial Intelligence in the Field of Energy Conversion Materials

“…But they do not perform well in the electrocatalytic process of OER\HER. Ge et al (2020) tried to predict the structure of new materials by optimizing the descriptor and combining density functional theory. Ge et al (2020) used LASSO algorithm to select characteristic descriptors and proposed the prediction equation of catalytic performance.…”

“…Ge et al (2020) tried to predict the structure of new materials by optimizing the descriptor and combining density functional theory. Ge et al (2020) used LASSO algorithm to select characteristic descriptors and proposed the prediction equation of catalytic performance. 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 can provide.…”