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

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Machine Learning Meets Quantum Physics

Part of the book series: Lecture Notes in Physics ((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.

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Notes

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    The term was recently popularized by a 2006 book by Kurzweil [21], but its use goes back to a 1958 account by Stanislaw Ulam of a discussion with John von Neumann that references a point in time of fundamental change due to runaway technological development [22].

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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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Authors and Affiliations

  1. Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden

    Rickard Armiento

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  1. Rickard Armiento
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Editors and Affiliations

  1. Machine Learning, Technical University of Berlin, Berlin, Germany

    Kristof T. Schütt

  2. Machine Learning Group, Technical University of Berlin, Berlin, Germany

    Stefan Chmiela

  3. Institute of Physical Chemistry and MARVEL, University of Basel, Basel, Switzerland

    O. Anatole von Lilienfeld

  4. Department of Physics and Materials Science, University of Luxembourg, Luxembourg, Luxembourg

    Alexandre Tkatchenko

  5. Graduate School of Frontier Sciences, University of Tokyo, Kashiwa, Japan

    Koji Tsuda

  6. Computer Science, Technical University of Berlin, Berlin, Germany

    Klaus-Robert Müller

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Armiento, R. (2020). Database-Driven High-Throughput Calculations and Machine Learning Models for Materials Design. In: Schütt, K., Chmiela, S., von Lilienfeld, O., Tkatchenko, A., Tsuda, K., Müller, KR. (eds) Machine Learning Meets Quantum Physics. Lecture Notes in Physics, vol 968. Springer, Cham. https://doi.org/10.1007/978-3-030-40245-7_17

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