Machine learning in materials modeling-fundamentals and the opportunities in 2D materials

Shreeja Das, Hansraj Pegu, Kisor Kumar Sahu, Ameeya Kumar Nayak, Seeram Ramakrishna, Dibakar Datta, S. Swayamjyoti

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Scopus citations

Abstract

The application of machine learning in materials presents a unique challenge of dealing with scarce and varied materials data - both experimental and theoretical. Nevertheless, several state-of-the-art machine learning models for materials have been successfully developed to predict material properties for various applications such as materials for photovoltaic cells, thermoelectric materials, dielectrics, batteries, fuel cells, etc. The necessary steps of data representation, choice of algorithm and optimisation needed to develop a successful machine learning model pertaining to materials are discussed at length. The setup of comprehensive materials databases, and openly accessible algorithm frameworks have also spurred the usage of machine learning for solving some of the most pressing problems in materials science. Some such recent implementations are discussed in this chapter. A multitude of two-dimensional (2D) materials exist with the potential to replace the conventional materials for energy storage and nanodevices. The challenges faced in designing batteries and how machine learning tools can help in screening and narrowing down on the best composition, as well as the synthesis of air-stable 2D materials, are also discussed.

Original languageEnglish (US)
Title of host publicationSynthesis, Modelling and Characterization of 2D Materials and their Heterostructures
PublisherElsevier
Pages445-468
Number of pages24
ISBN (Electronic)9780128184752
DOIs
StatePublished - Jan 1 2020

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Materials Science(all)

Keywords

  • 2D materials
  • Battery materials
  • Coulomb matrix
  • Machine learning
  • Neural networks

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