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Database-Driven High-Throughput Calculations and Machine Learning Models for Materials Design

  • Rickard ArmientoEmail author
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Part of the Lecture Notes in Physics book series (LNP, volume 968)

Abstract

This chapter reviews past and ongoing efforts in using high-throughput ab-initio calculations in combination with machine learning models for materials design. The primary focus is on bulk materials, i.e., materials with fixed, ordered, crystal structures, although the methods naturally extend into more complicated configurations. Efficient and robust computational methods, computational power, and reliable methods for automated database-driven high-throughput computation are combined to produce high-quality data sets. This data can be used to train machine learning models for predicting the stability of bulk materials and their properties. The underlying computational methods and the tools for automated calculations are discussed in some detail. Various machine learning models and, in particular, descriptors for general use in materials design are also covered.

Notes

Acknowledgements

The author thanks Anatole von Lilienfeld and Felix Faber for many insightful discussions on topics in the overlap of machine learning and materials design. Joel Davidsson is acknowledged for help with supervising the master’s thesis discussed in the text as Ref. [106]. The author acknowledges support from the Swedish e-Science Centre (SeRC), Swedish Research Council (VR) Grants No. 2016-04810, and the Centre in Nano science and Nanotechnology (CeNano) at Linköping University. Some of the discussed computations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at the National Supercomputer Centre (NSC) at Linköping University.

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© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Physics, Chemistry and BiologyLinköping UniversityLinköpingSweden

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