High Throughput Screening of Millions of van der Waals Heterostructures for Superlubricant Applications

Marco Fronzi, Sherif Abdulkader Tawfik, Mutaz Abu Ghazaleh, Olexandr Isayev, David A. Winkler, Joe Shapter, Michael J. Ford

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Article number2000029
Number of pages9
JournalAdvanced Theory and Simulations
DOIs
Publication statusAccepted/In press - 9 Sep 2020

Keywords

  • 2D materials
  • Density Functional Theory
  • machine learning
  • van der Waals heterostructures

Cite this