Preconditioners for Large-Scale Atomistic Simulations

Lead Research Organisation: University of Cambridge
Department Name: Engineering

Abstract

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Publications

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Bartók AP (2017) Machine learning unifies the modeling of materials and molecules. in Science advances

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Mones L (2018) Preconditioners for the geometry optimisation and saddle point search of molecular systems. in Scientific reports

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Packwood D (2016) A universal preconditioner for simulating condensed phase materials. in The Journal of chemical physics

 
Description We have developed a general strategy to speeding up the optimisation of the geometry of atomic scale models. Our strategy leads to a gain in computational efficiency of at least a factor of 2 and sometimes more than 10 in a very wide range of examples. The original strategy applied to solids, we have subsequently extended the optimisation strategy to molecules, molecular crystals, and also to finding saddle points.
Exploitation Route Two of the PIs have committed to keep working towards the original objective. The software algorithm has been incorporated in a commonly used framework for atomistic modelling (ASE: Atomic Simulation Environment) and recently also Castep (a density functional simulation code in wide commercial and academic use)
Sectors Aerospace, Defence and Marine,Chemicals,Energy,Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology
 
Title module for preconditioned optimisation in the ASE software package 
Description We implemented a module in the Atomic Simulation Environment (ASE) python package, which enables preconditioned optimisation of atomic configurations. This enhances the efficiency of such optimisations by a significant factor. 
Type Of Technology Software 
Year Produced 2016 
Open Source License? Yes  
Impact No impacts yet. 
URL https://gitlab.com/ase/ase