FLAME: A library of atomistic modeling environments

M Amsler and S Rostami and H Tahmasbi and ER Khajehpasha and S Faraji and R Rasoulkhani and SA Ghasemi, COMPUTER PHYSICS COMMUNICATIONS, 256, 107415 (2020).

DOI: 10.1016/j.cpc.2020.107415

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. Program summary Program Title: FLAME CPC Library link to program files: http://dx.doi.org/10.17632/2t5c59rrrf.1 Developer's repository link: http://flame-code.org, https://github.com/flame-code/FLAME Licensing provisions: GPLv3 Programming language: Fortran2003, C/C++, Python External routines/libraries: BigDFT PSolver1-4, Spglib5, MPI, LaPack, Blas Nature of problem: Exploring the potential energy landscapes of complex condensed matter systems, their stationary points, and their global minima. Solution method: A neural network representation of the potential energy landscape in conjunction with a library of methods to explore its features, most notably the minima hopping approach. (C) 2020 Elsevier B.V. All rights reserved.

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