Impressive computational acceleration by using machine learning for 2-dimensional super-lubricant materials discovery
This work addresses the need for faster and more resource-efficient materials discovery in materials science, though it appears incremental as it applies existing machine learning methods to a specific domain.
The authors tackled the problem of computationally expensive screening of novel 2D super-lubricant materials by developing a machine learning approach to predict structural properties like interlayer energy and elastic constants, achieving high accuracy comparable to density functional theory methods.
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.