Enhanced deep potential model for fast and accurate molecular dynamics: application to the hydrated electron,Physical Chemistry Chemical Physics

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Enhanced deep potential model for fast and accurate molecular dynamics: application to the hydrated electron
Physical Chemistry Chemical Physics ( IF 2.9 ) Pub Date : 2024-08-23 , DOI: 10.1039/d4cp01483a
Ruiqi Gao ₁ , Yifan Li ₂ , Roberto Car ₂

Affiliation

In molecular simulations, neural network force fields aim at achieving ab initio accuracy with reduced computational cost. This work introduces enhancements to the Deep Potential network architecture, integrating a message-passing framework and a new lightweight implementation with various improvements. Our model achieves accuracy on par with leading machine learning force fields and offers significant speed advantages, making it well-suited for large-scale, accuracy-sensitive systems. We also introduce a new iterative model for Wannier center prediction, allowing us to keep track of electron positions in simulations of general insulating systems. We apply our model to study the solvated electron in bulk water, an ostensibly simple system that is actually quite challenging to represent with neural networks. Our trained model is not only accurate, but can also transfer to larger systems. Our simulation confirms the cavity model, where the electron's localized state is observed to be stable. Through an extensive run, we accurately determine various structural and dynamical properties of the solvated electron.

中文翻译：

用于快速准确分子动力学的增强深势模型：在水合电子中的应用

在分子模拟中，神经网络力场旨在以降低计算成本的方式实现从头计算的精度。这项工作引入了 Deep Potential 网络架构的增强功能，集成了消息传递框架和新的轻量级实现以及各种改进。我们的模型实现了与领先的机器学习力场相当的精度，并提供了显着的速度优势，使其非常适合大规模、精度敏感的系统。我们还引入了用于万尼尔中心预测的新迭代模型，使我们能够在一般绝缘系统的模拟中跟踪电子位置。我们应用我们的模型来研究大量水中的溶剂化电子，这是一个表面上简单的系统，实际上用神经网络表示相当具有挑战性。我们训练的模型不仅准确，而且还可以转移到更大的系统。我们的模拟证实了腔模型，其中观察到电子的局域态是稳定的。通过广泛的运行，我们准确地确定了溶剂化电子的各种结构和动力学特性。

更新日期：2024-08-23

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