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The screening of novel materials is an important topic in the field ofmaterials science. Although traditional computational modeling, especiallyfirst-principles approaches, is a very useful and accurate tool to predict theproperties of novel materials, it still demands extensive and expensivestate-of-the-art computational resources. Additionally, they can be oftenextremely time consuming. We describe a time and resource-efficient machinelearning approach to create a large dataset of structural properties of van derWaals layered structures. In particular, we focus on the interlayer energy andthe elastic constant of layered materials composed of two different2-dimensional (2D) structures, that are important for novel solid lubricant andsuper-lubricant materials. We show that machine learning models canrecapitulate results of computationally expansive approaches (i.e. densityfunctional theory) with high accuracy.

Impressive computational acceleration by using machine learning for 2-dimensional super-lubricant materials discovery