Parallel Multistream Training of High-Dimensional Neural Network Potentials

Over
the past years high-dimensional neural network potentials
(HDNNPs), fitted to accurately reproduce ab initio potential energy
surfaces, have become a powerful tool in chemistry, physics and materials
science. Here, we focus on the training of the neural networks that
lies at the heart of the HDNNP method. We present an efficient approach
for optimizing the weight parameters of the neural network via multistream
Kalman filtering, using potential energies and forces as reference
data. In this procedure, the choice of the free parameters of the
Kalman filter can have a significant impact on the fit quality. Carrying
out a large parameter study, we determine optimal settings and demonstrate
how to optimize training results of HDNNPs. Moreover, we illustrate
our HDNNP training approach by revisiting previously presented fits
for water and developing a new potential for copper sulfide. This
material, accessible in computer simulations so far only via first-principles
methods, forms a particularly complex solid structure at low temperatures
and undergoes a phase transition to a superionic state upon heating.
Analyzing MD simulations carried out with the Cu2S HDNNP,
we confirm that the underlying ab initio reference method indeed reproduces
this behavior.

近年来，针对高维神经网络势（HDNNPs）的拟合，以精确再现从头算势能面，已成为化学、物理学和材料科学领域的一项强大工具。在此，我们专注于HDNNP方法核心的神经网络训练。我们提出了一种通过多流Kalman滤波优化神经网络权重参数的高效方法，以势能和力作为参考数据。在此过程中，Kalman滤波自由参数的选择对拟合质量具有显著影响。通过进行大规模参数研究，我们确定了最佳设置，并展示了如何优化HDNNPs的训练结果。此外，我们通过重新审视先前提出的水的拟合，并开发了一种新的铜硫化物势能，展示了我们的HDNNP训练方法。这种材料在计算机模拟中至今仅可通过第一性原理方法获得，在低温下形成了一种特别复杂的固体结构，并在加热后发生相变至超离子态。通过使用Cu2S HDNNP进行的MD模拟分析，我们证实了基于从头算参考方法确实再现了这一行为。