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
Tags
2D materials
virtual screening
artificial neural networks
energy conversion
energy storage
stable compounds
database
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