用少量新增参数提升神经网络力场精度，无需加深或加宽模型。
Trainable Adaptive Activation Function Structure (TAAFS) Enhances Neural Network Force Field Performance with Only Dozens of Additional Parameters
- 引入可训练的自适应激活结构，动态选择最优非线性函数。
- 在多种模型上实现精度提升，仅增加数十个参数。
- 适合追求高精度且受限于参数量的分子模拟研究者。
神经网络力场（NNFFs）的核心在于神经网络架构，通常通过拓宽或加深多层感知机（MLPs）或增加图神经网络（GNNs）层数来增强建模复杂相互作用的能力。然而，这些改进常伴随参数量大幅增加。本文提出可训练自适应激活函数结构（TAAFS），通过为非线性激活函数选择不同的数学表达式，在几乎不增加参数数量的前提下，显著提升NNFF的精度。我们将在多种神经网络模型中集成TAAFS，观察到性能提升，并通过DeepMD分子动力学模拟进一步验证其有效性。
原文摘要 · Abstract (English)
At the heart of neural network force fields (NNFFs) is the architecture of neural networks, where the capacity to model complex interactions is typically enhanced through widening or deepening multilayer perceptrons (MLPs) or by increasing layers of graph neural networks (GNNs). These enhancements, while improving the model's performance, often come at the cost of a substantial increase in the number of parameters. By applying the Trainable Adaptive Activation Function Structure (TAAFS), we introduce a method that selects distinct mathematical formulations for non-linear activations, thereby increasing the precision of NNFFs with an insignificant addition to the parameter count. In this study, we integrate TAAFS into a variety of neural network models, resulting in observed accuracy improvements, and further validate these enhancements through molecular dynamics (MD) simulations using DeepMD.
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