Predictive quantum vibrational spectra through active learning 4G-NNPs

TL;DR

This paper introduces a new active learning-based neural network potential framework that accurately predicts vibrational spectra of complex condensed-phase systems by integrating quantum effects and anharmonicities without empirical fitting.

Contribution

The work develops 4G-HDCNNPs using active learning, enabling accurate vibrational spectra prediction including nuclear quantum effects and charge transfer effects without explicit dipole training.

Findings

Accurate infrared spectra for bulk water and interfaces.

Seamless integration of charge transfer effects with quantum nuclear effects.

Framework is simple, general, and parameter-free.

Abstract

Predictive simulation of vibrational spectra of complex condensed-phase and interface systems with thousands of degrees of freedom has long been a challenging task of modern condensed matter theory. In this work, fourth-generation high-dimensional committee neural network potentials (4G-HDCNNPs) are developed using active learning and query-by-committee, and introduced to the essential nuclear quantum effects (NQEs) as well as conformational entropy and anharmonicities from path integral (PI) molecular dynamics simulations. Using representative bulk water and air-water interface test cases, we demonstrate the accuracy of the developed framework in infrared spectral simulations. Specifically, by seamlessly integrating non-local charge transfer effects from 4G-HDCNNPs with the NQEs from PI methods, our introduced methodology yields accurate infrared spectra using predicted charges from the 4G-HDCNNP architecture without explicit training of dipole moments. The framework introduced in this work is simple and general, offering a practical paradigm for predictive spectral simulations of complex condensed phases and interfaces, free from empirical parameterizations and ad hoc fitting.