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High Throughput Screening of Millions of van der Waals Heterostructures for Superlubricant Applications
Author(s) -
Fronzi Marco,
Tawfik Sherif Abdulkader,
Ghazaleh Mutaz Abu,
Isayev Olexandr,
Winkler David A.,
Shapter Joe,
Ford Michael J.
Publication year - 2020
Publication title -
advanced theory and simulations
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.068
H-Index - 17
ISSN - 2513-0390
DOI - 10.1002/adts.202000029
Subject(s) - expansive , van der waals force , computer science , lubricant , resource (disambiguation) , focus (optics) , field (mathematics) , density functional theory , nanotechnology , computational science , artificial intelligence , machine learning , materials science , physics , mathematics , computer network , compressive strength , composite material , quantum mechanics , molecule , pure mathematics , optics
Abstract 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.