A universal preconditioner for simulating condensed phase materials

2016/04/09 by David Packwood, James Kermode, Letif Mones +6 · 90 citations
Materials Science · Physics and Astronomy · #Advanced Chemical Physics Studies #Block Copolymer Self-Assembly #Computation #Machine Learning in Materials Science #Point (geometry) #Preconditioner #Python (programming language) #Range (aeronautics) #Saddle point #cond-mat.mtrl-sci #physics.comp-ph

paper · pdf · doi:10.1063/1.4947024

published in The Journal of Chemical Physics 144(16), 164109 (American Institute of Physics)

arxiv created 2016/04/09 · openalex publication_date 2016/04/26 · arxiv updated 2016/04/27 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05

Abstract

We introduce a universal sparse preconditioner that accelerates geometry optimisation and saddle point search tasks that are common in the atomic scale simulation of materials. Our preconditioner is based on the neighbourhood structure and we demonstrate the gain in computational efficiency in a wide range of materials that include metals, insulators, and molecular solids. The simple structure of the preconditioner means that the gains can be realised in practice not only when using expensive electronic structure models but also for fast empirical potentials. Even for relatively small systems of a few hundred atoms, we observe speedups of a factor of two or more, and the gain grows with system size. An open source Python implementation within the Atomic Simulation Environment is available, offering interfaces to a wide range of atomistic codes.

Citations

• A dimer method for finding saddle points on high dimensional potential surfaces using only first derivatives
• Quickstep: Fast and accurate density functional calculations using a mixed Gaussian and plane waves approach
• Relaxation of Crystals with the Quasi-Newton Method

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