A Bond-Based Machine Learning Model for Molecular Polarizabilities and A Priori Raman Spectra
arXiv:2410.14498 · doi:10.1021/acs.jctc.4c01086
Abstract
The use of machine learning (ML) algorithms in molecular simulations has become commonplace in recent years. There now exists, for instance, a multitude of ML force field algorithms that have enabled simulations approaching ab initio level accuracy at time scales and system sizes that significantly exceed what is otherwise possible with traditional methods. Far fewer algorithms exist for predicting rotationally equivariant, tensorial properties such as the electric polarizability. Here, we introduce a kernel ridge regression algorithm for machine learning of the polarizability tensor. This algorithm is based on the bond polarizability model and allows prediction of the tensor components at the cost similar to that of scalar quantities. We subsequently show the utility of this algorithm by simulating gas phase Raman spectra of biphenyl and malonaldehyde using classical molecular dynamics simulations of these systems performed with the recently developed MACE-OFF23 potential. The calculated spectra are shown to agree very well with the experiments and thus confirm the expediency of our algorithm as well as the accuracy of the used force field. More generally, this work demonstrates the potential of physics-informed approaches to yield simple yet effective machine learning algorithms for molecular properties.
References in corpus (16)
- ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
- Machine learning for molecular simulation
- NWChem: Past, Present, and Future
- Machine-learning based interatomic potential for amorphous carbon
- Symmetry-Adapted Machine-Learning for Tensorial Properties of Atomistic Systems
- Deep Potentials for Materials Science
- Raman Spectrum and Polarizability of Liquid Water from Deep Neural Networks
- Boltzmann-conserving classical dynamics in quantum time-correlation functions: Matsubara dynamics
- Universal Machine Learning for the Response of Atomistic Systems to External Fields
- Interlayer bond polarizability model for stacking-dependent low-frequency Raman scattering in layered materials
- Spectroscopy from Machine Learning by Accurately Representing the Atomic Polar Tensor
- Predicting tensorial molecular properties with equivariant machine-learning models
- Tensorial properties via the neuroevolution potential framework: Fast simulation of infrared and Raman spectra
- Transfer-Learned Potential Energy Surfaces: Towards Microsecond-Scale Molecular Dynamics Simulations in the Gas Phase at CCSD(T) Quality
- IR Spectroscopy of Carboxylate-Passivated Semiconducting Nanocrystals: Simulation and Experiment
- A simple approach to rotationally invariant machine learning of avector quantity