Fortnet, a software package for training Behler-Parrinello neural networks
收藏资源简介：
A new, open source, parallel, stand-alone software package (Fortnet) has been developed, which implements Behler-Parrinello neural networks. It covers the entire workflow from feature generation to the evaluation of generated potentials, coupled with higher-level analysis such as the analytic calculation of atomic forces. The functionality of the software package is demonstrated by driving the training for the fitted correction functions of the density functional tight binding (DFTB) method, which are commonly used to compensate the inaccuracies resulting from the DFTB approximations to the Kohn-Sham Hamiltonian. The usual two-body form of those correction functions limits the transferability of the parametrizations between very different structural environments. The recently introduced DFTB+ANN approach strives to lift these limitations by combining DFTB with a near-sighted artificial neural network (ANN). After investigating various approaches, we have found the combination of DFTB with an ANN acting on-top of some baseline correction functions (delta learning) the most accurate one. It allowed to introduce many-body corrections on top of two-body parametrizations, while excellent transferability to chemical environments with deviating energetics could be demonstrated.
本研究开发了一款全新的开源、并行式独立软件包（Fortnet），该软件实现了Behler-Parrinello神经网络（Behler-Parrinello neural networks）。其覆盖从特征生成到生成势评估的完整工作流程，同时集成了原子力解析计算等高阶分析功能。本研究通过开展密度泛函紧束缚（density functional tight binding, DFTB）方法拟合校正函数的训练，验证了该软件包的功能——这类校正函数通常用于弥补DFTB近似处理Kohn-Sham哈密顿量（Kohn-Sham Hamiltonian）时产生的误差。这类校正函数多采用二体形式，这限制了其参数化方案在差异极大的结构环境间的迁移性。近期提出的DFTB+ANN方法通过将DFTB与局域人工神经网络（artificial neural network, ANN）相结合，旨在突破上述局限。经对多种方案的调研，我们发现将DFTB与基于基准校正函数之上的人工神经网络相结合的增量学习（delta learning）方案精度最高。该方案可在二体参数化的基础上引入多体校正，同时已被证实对能量偏离的化学环境具备优异的迁移性能。
