FLAME: A library of atomistic modeling environments

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.FLAME是一款可实现各类原子尺度模拟的软件包，用于探究复杂凝聚态系统的势能面（potential energy surfaces, PES）。其支持的方法涵盖：用于采样自由能景观的分子动力学模拟、用于识别过渡态的鞍点搜索，以及用于获取动力学稳定几何结构的梯度弛豫。除上述常规任务外，FLAME还集成了基于极小值跳跃法（minima hopping method, MHM）的结构预测算法，可仅通过化学组成确定任意体系的基态结构；同时搭建了一套可基于从头算（ab initio）计算训练神经网络势函数，以复现势能面的框架。FLAME中神经网络势函数与极小值跳跃法的结合，可高效且可靠地识别分子、晶体，以及包括表面、界面与二维材料在内的纳米结构的基态与亚稳结构。本文详细阐述了FLAME代码所实现的各类方法及其功能，并辅以多个示例进行说明。