Computing Bulk Phase IR Spectra from Finite Cluster Data via Equivariant Neural Networks.

IF 5.7 1区化学 Q2 CHEMISTRY, PHYSICAL

Journal of Chemical Theory and Computation Pub Date : 2025-05-17 DOI:10.1021/acs.jctc.5c00420

Aman Jindal,Philipp Schienbein,Banshi Das,Dominik Marx

引用次数: 0

Abstract

Calculating accurate IR spectra from molecular dynamics simulations is crucial for understanding structural dynamics and benchmarking simulations. While machine learning has accelerated such calculations, leveraging finite-cluster data to compute condensed-phase IR spectra remains unexplored. In this work, we address a fundamental question: Can a machine learning model trained exclusively on electronic structure calculations of finite-size clusters reproduce the bulk IR spectrum? Using the atomic polar tensor as a target training property, we demonstrate that the corresponding equivariant neural network accurately recovers the bulk IR spectrum of liquid water, establishing the key link between finite-cluster data and bulk properties.

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用等变神经网络从有限簇数据计算体相红外光谱。

从分子动力学模拟中计算精确的红外光谱对于理解结构动力学和基准模拟至关重要。虽然机器学习加速了这种计算，但利用有限簇数据来计算凝聚态红外光谱仍未被探索。在这项工作中，我们解决了一个基本问题：机器学习模型能否在有限大小的簇的电子结构计算上进行训练，以重现整体红外光谱？利用原子极性张量作为目标训练性质，我们证明了相应的等变神经网络能够准确地恢复液态水的体红外光谱，建立了有限簇数据与体性质之间的关键联系。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Chemical Theory and Computation 化学-物理：原子、分子和化学物理

CiteScore

9.90

自引率

16.40%

发文量

568

审稿时长

1 months

期刊介绍： The Journal of Chemical Theory and Computation invites new and original contributions with the understanding that, if accepted, they will not be published elsewhere. Papers reporting new theories, methodology, and/or important applications in quantum electronic structure, molecular dynamics, and statistical mechanics are appropriate for submission to this Journal. Specific topics include advances in or applications of ab initio quantum mechanics, density functional theory, design and properties of new materials, surface science, Monte Carlo simulations, solvation models, QM/MM calculations, biomolecular structure prediction, and molecular dynamics in the broadest sense including gas-phase dynamics, ab initio dynamics, biomolecular dynamics, and protein folding. The Journal does not consider papers that are straightforward applications of known methods including DFT and molecular dynamics. The Journal favors submissions that include advances in theory or methodology with applications to compelling problems.