Atomistic calculations and materials informatics: A review. Issue 3 (June 2017) - Ward, Logan; Wolverton, Chris
Highlights: Procedure for using machine learning models with atomistic calculation data. Many examples of how machine learning can accelerate atomistic calculations. Discussion of future challenges and opportunities in this field. Abstract: 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.