Engineering and Technology

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

M. C. Sorkun, S. Astruc, et al.

This paper delves into a revolutionary data-driven strategy for accelerating the virtual screening of two-dimensional materials, leading to a comprehensive database of over 316,000 stable materials with key properties for energy applications. The research was conducted by Murat Cihan Sorkun, Séverin Astruc, J. M. Vianney A. Koelman, and Süleyman Er.

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Abstract

This paper introduces a data-driven strategy for accelerating the virtual screening of two-dimensional (2D) materials. The authors generate a vast library of 2D compounds, filter for likely stable materials, and predict key properties using artificial neural networks (ANNs). This process resulted in a database (V2DB) containing 316,505 stable 2D materials with predicted properties relevant to energy conversion and storage applications.

Publisher

npj Computational Materials

Published On

Jul 24, 2020

Authors

Murat Cihan Sorkun, Séverin Astruc, J. M. Vianney A. Koelman, Süleyman Er

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