2014/01/29 by Diego González, Sergio Davis
Chemistry · Materials Science · Physics and Astronomy · #Ab initio #Advanced Chemical Physics Studies #Algorithm #Atom (system on chip) #Computer science #Crystallography and molecular interactions #Interatomic potential #Machine Learning in Materials Science #Molecular dynamics #Particle (ecology) #Particle swarm optimization #Physics #Quantum mechanics #Statistical physics #cond-mat.mtrl-sci
paper · pdf · doi:10.1016/j.cpc.2014.07.019
published as Comp. Phys. Comm. 185, 3090-3093 (2014)
arxiv created 2014/01/29 · openalex publication_date 2014/08/12 · openalex created_date 2016/06/24 · arxiv updated 2016/08/01 · openalex updated_date 2026/08/05
We present a methodology for fitting interatomic potentials to ab initio data, using the particle swarm optimization (PSO) algorithm, needing only a set of positions and energies as input. The prediction error of energies associated with the fitted parameters can be close to 1 meV/atom or lower, for reference energies having a standard deviation of about 0.5 eV/atom. We tested our method by fitting a Sutton-Chen potential for copper from ab initio data, which is able to recover structural and dynamical properties, and obtain a better agreement of the predicted melting point versus the experimental value, as compared to the prediction of the standard Sutton-Chen parameters.