Spectroscopy from Machine Learning by Accurately Representing the Atomic Polar Tensor

Abstract

Vibrational spectroscopy is a key technique to elucidate microscopic structure and dynamics. Without the aid of theoretical approaches, it is however, often difficult to understand such spectra at a microscopic level. Ab initio molecular dynamics have repeatedly proved to be suitable for this purpose, however, the computational cost can be daunting. Here, the E(3)-equivariant neural network e3nn is used to fit the atomic polar tensor of liquid water a posteriori on top of existing molecular dynamics simulations. Notably, the introduced methodology is general and thus transferable to any other system as well. The target property is most fundamental, gives access to the IR spectrum and, more importantly, it is a highly powerful tool to directly assign IR spectral features to nuclear motion -- a connection which has been pursued in the past but only using severe approximations due to the prohibitive computational cost. The herein introduced methodology overcomes this bottleneck. To benchmark the machine learning model, the IR spectrum of liquid water is calculated, indeed showing excellent agreement with the explicit reference calculation. In conclusion, the presented methodology gives a new route to calculate accurate IR spectra from molecular dynamics simulations and will facilitate the understanding of such spectra on a microscopic level.

References in corpus (19)

• CP2K: An Electronic Structure and Molecular Dynamics Software Package -- Quickstep: Efficient and Accurate Electronic Structure Calculations
• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
• Quantum-Chemical Insights from Deep Tensor Neural Networks
• A beginner's guide to the modern theory of polarization
• Machine Learning Molecular Dynamics for the Simulation of Infrared Spectra
• How van der Waals interactions determine the unique properties of water
• Symmetry-Adapted Machine-Learning for Tensorial Properties of Atomistic Systems
• A Transferable Machine-Learning Model of the Electron Density
• Accurate molecular polarizabilities with coupled-cluster theory and machine learning
• Committee neural network potentials control generalization errors and enable active learning
• Self-Consistent Determination of Long-Range Electrostatics in Neural Network Potentials
• Automated Fitting of Neural Network Potentials at Coupled Cluster Accuracy: Protonated Water Clusters as Testing Ground
• The Interplay of Structure and Dynamics in the Raman Spectrum of Liquid Water over the Full Frequency and Temperature Range
• Using Gaussian Process Regression to Simulate the Vibrational Raman Spectra of Molecular Crystals
• Deep Learning for UV Absorption Spectra with SchNarc: First Steps Towards Transferability in Chemical Compound Space
• Inexpensive modelling of quantum dynamics using path integral generalized Langevin equation thermostats
• Infrared spectra at coupled cluster accuracy from neural network representations
• Modeling electronic response properties with an explicit-electron machine learning potential

Cited by in corpus (7)

• Universal Machine Learning for the Response of Atomistic Systems to External Fields
• Molecular dynamics simulation with finite electric fields using Perturbed Neural Network Potentials
• Fully First-Principles Surface Spectroscopy with Machine Learning
• Revealing the molecular structures of a-Al2O3(0001)-water interface by machine learning based computational vibrational spectroscopy
• Atomistic Simulations of Oxide-Water Interfaces using Machine Learning Potentials
• A Bond-Based Machine Learning Model for Molecular Polarizabilities and A Priori Raman Spectra
• Probing the Temporal Response of Liquid Water to a THz Pump Pulse Using Machine Learning-Accelerated Non-Equilibrium Molecular Dynamics