Journal
ADVANCED THEORY AND SIMULATIONS
Volume 3, Issue 11, Pages -Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/adts.202000029
Keywords
machine learning; Density Functional Theory; 2D materials; van der Waals heterostructures
Categories
Funding
- Australian Government through the Australian Research Council [ARC DP16010130]
- Australian Government
- Pawsey Supercomputing Centre
- Government of Western Australia
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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 often be extremely time consuming. A time and resource efficient machine learning approach to create a dataset of structural properties of 18 million van der Waals layered structures is described. In particular, the authors focus on the interlayer energy and the elastic constant of layered materials composed of two different 2D structures that are important for novel solid lubricant and super-lubricant materials. It is shown that machine learning models can predict results of computationally expansive approaches (i.e., density functional theory) with high accuracy.
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