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Crystal Structure Prediction via Oblivious Local Search

Authors Dmytro Antypov, Argyrios Deligkas, Vladimir Gusev, Matthew J. Rosseinsky, Paul G. Spirakis, Michail Theofilatos

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Author Details

Dmytro Antypov
  • Department of Chemistry, University of Liverpool, UK
  • Leverhulme Research Centre for Functional Materials Design, University of Liverpool, UK
Argyrios Deligkas
  • Department of Computer Science, Royal Holloway University of London, UK
Vladimir Gusev
  • Leverhulme Research Centre for Functional Materials Design, University of Liverpool, UK
  • Department of Chemistry, University of Liverpool, UK
Matthew J. Rosseinsky
  • Department of Chemistry, University of Liverpool, UK
Paul G. Spirakis
  • Department of Computer Science, University of Liverpool, UK
  • Computer Engineering and Informatics Department, University of Patras, Greece
Michail Theofilatos
  • Leverhulme Research Centre for Functional Materials Design, University of Liverpool, UK
  • Department of Computer Science, University of Liverpool, UK

Funding

The authors were supported by the Leverhulme Trust.

Cite As Get BibTex

Dmytro Antypov, Argyrios Deligkas, Vladimir Gusev, Matthew J. Rosseinsky, Paul G. Spirakis, and Michail Theofilatos. Crystal Structure Prediction via Oblivious Local Search. In 18th International Symposium on Experimental Algorithms (SEA 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 160, pp. 21:1-21:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020) https://doi.org/10.4230/LIPIcs.SEA.2020.21

Abstract

We study Crystal Structure Prediction, one of the major problems in computational chemistry. This is essentially a continuous optimization problem, where many different, simple and sophisticated, methods have been proposed and applied. The simple searching techniques are easy to understand, usually easy to implement, but they can be slow in practice. On the other hand, the more sophisticated approaches perform well in general, however almost all of them have a large number of parameters that require fine tuning and, in the majority of the cases, chemical expertise is needed in order to properly set them up. In addition, due to the chemical expertise involved in the parameter-tuning, these approaches can be biased towards previously-known crystal structures. Our contribution is twofold. Firstly, we formalize the Crystal Structure Prediction problem, alongside several other intermediate problems, from a theoretical computer science perspective. Secondly, we propose an oblivious algorithm for Crystal Structure Prediction that is based on local search. Oblivious means that our algorithm requires minimal knowledge about the composition we are trying to compute a crystal structure for. In addition, our algorithm can be used as an intermediate step by any method. Our experiments show that our algorithms outperform the standard basin hopping, a well studied algorithm for the problem.

Subject Classification

ACM Subject Classification
  • Applied computing → Chemistry
Keywords
  • crystal structure prediction
  • local search
  • combinatorial neighborhood

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References

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