Trainable Adaptive Activation Function Structure (TAAFS) Enhances Neural Network Force Field Performance with Only Dozens of Additional Parameters

• URL: http://arxiv.org/abs/2412.14655v1
• Date: Thu, 19 Dec 2024 09:06:39 GMT
• Title: Trainable Adaptive Activation Function Structure (TAAFS) Enhances Neural Network Force Field Performance with Only Dozens of Additional Parameters
• Authors: Enji Li,
• Abstract summary: Trainable Adaptive Function Activation Structure (TAAFS)<n>We introduce a method that selects distinct mathematical formulations for non-linear activations.<n>In this study, we integrate TAAFS into a variety of neural network models, resulting in observed accuracy improvements.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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