FLAME: a library of atomistic modeling environments

TL;DR

FLAME is a versatile software package for atomistic simulations that combines traditional methods with neural network potentials and structure prediction algorithms to efficiently explore complex energy landscapes and identify stable structures.

Contribution

It introduces a comprehensive framework integrating molecular dynamics, saddle point searches, neural network potentials, and the minima hopping method for atomistic modeling.

Findings

Efficient identification of ground and metastable states.

Successful application to molecules, crystals, and nanostructures.

Framework for training neural network potentials from ab initio data.

Abstract

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 range of 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 $\textit{ab initio}$ 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.