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

研究成果: Article査読

2 被引用数 (Scopus)

抄録

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.

本文言語English
論文番号2000029
ジャーナルAdvanced Theory and Simulations
3
11
DOI
出版ステータスPublished - 2020 11 1
外部発表はい

ASJC Scopus subject areas

  • 統計学および確率
  • 数値解析
  • モデリングとシミュレーション
  • 一般

フィンガープリント

「High Throughput Screening of Millions of van der Waals Heterostructures for Superlubricant Applications」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル