Impressive computational acceleration by using machine learning for 2-dimensional super-lubricant materials discovery
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The screening of novel materials is an important topic in the field of
materials science. Although traditional computational modeling, especially
first-principles approaches, is a very useful and accurate tool to predict the
properties of novel materials, it still demands extensive and expensive
state-of-the-art computational resources. Additionally, they can be often
extremely time consuming. We describe a time and resource-efficient machine
learning approach to create a large dataset of structural properties of van der
Waals layered structures. In particular, we focus on the interlayer energy and
the elastic constant of layered materials composed of two different
2-dimensional (2D) structures, that are important for novel solid lubricant and
super-lubricant materials. We show that machine learning models can
recapitulate results of computationally expansive approaches (i.e. density
functional theory) with high accuracy.
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