摘要
Defects dictate the properties of many functional materials.To understand the behaviour of defects and their impact on physical properties,it is necessary to identify themost stabledefect geometries.However,global structure searching is computationally challenging for high-throughput defect studies ormaterials with complex defect landscapes,like alloys or disordered solids.Here,we tackle this limitation by harnessing a machine-learning surrogate model to qualitatively explore the structural landscape of neutral point defects.
基金
S.R.K.acknowledges the EPSRC Centre for Doctoral Training in the Advanced Characterisation of Materials(CDT-ACM)(EP/S023259/1)for funding a PhD studentship
A.M.G.is supported by EPSRC Fellowship EP/T033231/1
A.W.is supported by EPSRC project EP/X037754/1
We are grateful to the UK Materials and Molecular Modelling Hub for computational resources,which are partially funded by EPSRC(EP/P020194/1 and EP/T022213/1)
This work used the ARCHER2 UK National Supercomputing Service(https://www.archer2.ac.uk)via our membership of the UK’s HEC Materials Chemistry Consortium,which is funded by EPSRC(EP/L000202).
