Fortnet, a software package for training Behler-Parrinello neural networks

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.

一款新型的开源并行独立软件包（Fortnet）已成功开发，该软件包实现了Behler-Parrinello神经网络。它涵盖了从特征生成到生成势能评估的整个工作流程，并辅以如原子力的解析计算等高级分析。该软件包的功能通过驱动密度泛函紧束缚（DFTB）方法的拟合校正函数的训练得到体现，这些校正函数常用于补偿DFTB对Kohn-Sham哈密顿量的近似所带来的不准确。这些校正函数的常规双体形式限制了参数化在不同结构环境之间的迁移性。最近引入的DFTB+ANN方法旨在通过将DFTB与一种近似的艺术神经网络（ANN）相结合来克服这些限制。在调查了多种方法后，我们发现DFTB与作用于某些基线校正函数之上的ANN（delta学习）组合是最准确的。这允许在双体参数化之上引入多体校正，同时展示了在具有不同能量学的化学环境中的卓越迁移性。