We propose a scheme to construct predictive models for Hamiltonian matrices in atomic orbital representation from ab initio data as a function of atomic and bond environments.The scheme goes beyond conventional tight ...We propose a scheme to construct predictive models for Hamiltonian matrices in atomic orbital representation from ab initio data as a function of atomic and bond environments.The scheme goes beyond conventional tight binding descriptions as it represents the ab initio model to full order,rather than in two-centre or three-centre approximations.We achieve this by introducing an extension to the atomic cluster expansion(ACE)descriptor that represents Hamiltonian matrix blocks that transform equivariantly with respect to the full rotation group.The approach produces analytical linear models for the Hamiltonian and overlap matrices.Through an application to aluminium,we demonstrate that it is possible to train models from a handful of structures computed with density functional theory,and apply them to produce accurate predictions for the electronic structure.The model generalises well and is able to predict defects accurately from only bulk training data.展开更多
基金This work was financially supported by a Leverhulme Trust Research Project Grant (RPG-2017-191)the Engineering and Physical Science Research Council (EPSRC) under grant EP/R043612/1+3 种基金the NOMAD Centre of Excellence (European Commission grant agreement ID 951786)the UKRI Future Leaders Fellowship programme (MR/S016023/1)We acknowledge computational resources provided by the Scientific Computing Research Technology Platform of the University of Warwick,the EPSRC-funded HPC Midlands+ consortium (EP/P020232/1,EP/T022108/1)on ARCHER2 via the UK Car-Parinello consortium (EP/P022065/1).
文摘We propose a scheme to construct predictive models for Hamiltonian matrices in atomic orbital representation from ab initio data as a function of atomic and bond environments.The scheme goes beyond conventional tight binding descriptions as it represents the ab initio model to full order,rather than in two-centre or three-centre approximations.We achieve this by introducing an extension to the atomic cluster expansion(ACE)descriptor that represents Hamiltonian matrix blocks that transform equivariantly with respect to the full rotation group.The approach produces analytical linear models for the Hamiltonian and overlap matrices.Through an application to aluminium,we demonstrate that it is possible to train models from a handful of structures computed with density functional theory,and apply them to produce accurate predictions for the electronic structure.The model generalises well and is able to predict defects accurately from only bulk training data.
