Amp: A modular approach to machine learning in atomistic simulations

Published: 1 October 2016| Version 1 | DOI: 10.17632/rhrbt5ddkk.1
Alireza Khorshidi, Andrew A. Peterson


This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018) Abstract Electronic structure calculations, such as those employing Kohn–Sham density functional theory or ab initio wavefunction theories, have allowed for atomistic-level understandings of a wide variety of phenomena and properties of matter at small scales. However, the computational cost of electronic structure methods drastically increases with length and time scales, which makes these methods difficult for long time-scale molecular dynamics simulations or large-sized systems. Machine-learning te... Title of program: Amp Catalogue Id: AFAK_v1_0 Nature of problem Atomic interactions within many-body systems typically have complicated functional forms, difficult to represent in simple pre-decided closed-forms. Versions of this program held in the CPC repository in Mendeley Data AFAK_v1_0; Amp; 10.1016/j.cpc.2016.05.010



Atomic Physics, Physical Chemistry, Molecular Physics, Computational Physics