FLAME: A library of atomistic modeling environments

Published: 10 July 2020| Version 1 | DOI: 10.17632/2t5c59rrrf.1
Maximilian Amsler,
Samare Rostami,
Hossein Tahmasbi,
Ehsan Rahmatizad Khajehpasha,
Somayeh Faraji,
Robabe Rasoulkhani,
S. Alireza Ghasemi


FLAME is a software package to perform a wide range of atomistic simulations for exploring the potential energy surfaces (PES) of complex condensed matter systems. The available methods include molecular dynamics simulations to sample free energy landscapes, saddle point searches to identify transition states, and gradient relaxations to find dynamically stable geometries. In addition to such common tasks, FLAME implements a structure prediction algorithm based on the minima hopping method (MHM) to identify the ground state structure of any system given solely the chemical composition, and a framework to train a neural network potential to reproduce the PES from ab initio calculations. The combination of neural network potentials with the MHM in FLAME allows a highly efficient and reliable identification of the ground state as well as metastable structures of molecules and crystals, as well as of nano structures, including surfaces, interfaces, and two-dimensional materials. In this manuscript, we provide detailed descriptions of the methods implemented in the FLAME code and its capabilities, together with several illustrative examples.



Computational Physics, Structure Prediction, Potential Energy Surface, Neural Network