Synergistic Integration of Physical Embedding and Machine Learning Enabling Precise and Reliable Force Field,Journal of Chemical Theory and Computation

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Synergistic Integration of Physical Embedding and Machine Learning Enabling Precise and Reliable Force Field
Journal of Chemical Theory and Computation ( IF 5.7 ) Pub Date : 2024-09-12 , DOI: 10.1021/acs.jctc.4c00618
Lifeng Xu _{1,

2} , Jian Jiang _{1,

2}

Affiliation

Machine-learning force fields have achieved significant strides in accurately reproducing the potential energy surface with quantum chemical accuracy. However, this approach still faces several challenges, e.g., extrapolating to uncharted chemical spaces, interpreting long-range electrostatics, and mapping complex macroscopic properties. To address these issues, we advocate for a synergistic integration of physical principles and machine learning techniques within the framework of a physically informed neural network (PINN). This approach involves incorporating physical knowledge into the parameters of the neural network, coupled with an efficient global optimizer, the Tabu-Adam algorithm, proposed in this work to augment optimization under strict physical constraint. We choose the AMOEBA+ force field as the physics-based model for embedding and then train and test it using the diethylene glycol dimethyl ether (DEGDME) data set as a case study. The results reveal a breakthrough in constructing a precise and noise-robust machine learning force field. Utilizing two training sets with hundreds of samples, our model exhibits remarkable generalization and density functional theory (DFT) accuracy in describing molecular interactions and enables a precise prediction of the macroscopic properties such as the diffusion coefficient with minimal cost. This work provides valuable insight into establishing a fundamental framework of the PINN force field.

中文翻译：

物理嵌入和机器学习的协同集成实现精确可靠的力场

机器学习力场在以量子化学精度精确再现势能表面方面取得了重大进展。然而，这种方法仍然面临一些挑战，例如外推到未知的化学空间、解释长程静电以及绘制复杂的宏观特性。为了解决这些问题，我们主张在物理信息神经网络（PINN）的框架内协同整合物理原理和机器学习技术。这种方法涉及将物理知识纳入神经网络的参数中，再加上本工作中提出的高效全局优化器 Tabu-Adam 算法，以在严格的物理约束下增强优化。我们选择 AMOEBA+ 力场作为基于物理的嵌入模型，然后使用二甘醇二甲醚（DEGDME）数据集作为案例研究对其进行训练和测试。结果揭示了构建精确且抗噪声的机器学习力场的突破。我们的模型利用两个包含数百个样本的训练集，在描述分子相互作用方面表现出卓越的泛化性和密度泛函理论 (DFT) 准确性，并能够以最小的成本精确预测扩散系数等宏观特性。这项工作为建立 PINN 力场的基本框架提供了宝贵的见解。

更新日期：2024-09-12

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