Atomistic calculations and materials informatics: A review

Logan Ward, Chris Wolverton*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

90 Scopus citations


In recent years, there has been a large effort in the materials science community to employ materials informatics to accelerate materials discovery or to develop new understanding of materials behavior. Materials informatics methods utilize machine learning techniques to extract new knowledge or predictive models out of existing materials data. In this review, we discuss major advances in the intersection between data science and atom-scale calculations with a particular focus on studies of solid-state, inorganic materials. The examples discussed in this review cover methods for accelerating the calculation of computationally-expensive properties, identifying promising regions for materials discovery based on existing data, and extracting chemical intuition automatically from datasets. We also identify key issues in this field, such as limited distribution of software necessary to utilize these techniques, and opportunities for areas of research that would help lead to the wider adoption of materials informatics in the atomistic calculations community.

Original languageEnglish (US)
Pages (from-to)167-176
Number of pages10
JournalCurrent Opinion in Solid State and Materials Science
Issue number3
StatePublished - Jun 2017


  • Atomistic simulations
  • Machine learning
  • Materials informatics

ASJC Scopus subject areas

  • Materials Science(all)

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