Predicting Spectroscopic Properties of Solvated Nile Red with Automated Workflows for Machine Learned Interatomic Potentials

TL;DR

This paper presents an efficient workflow using machine learned interatomic potentials and active learning to accurately predict spectroscopic properties of solvated Nile Red dye across various solvents, reducing computational costs.

Contribution

It introduces an automated, iterative workflow for generating MLIPs for solvated dyes, comparing multiple methodologies, and validating spectral predictions against experimental data.

Findings

Larger solvent training data improves spectral prediction accuracy.

Delta-ML models effectively predict excitation energies.

The workflow achieves DFT-level accuracy at reduced computational cost.

Abstract

Machine Learned Interatomic Potentials (MLIPs) offer a powerful combination of abilities for accelerating theoretical spectroscopy calculations utilising both ensemble sampling and trajectory post-processing for inclusion of vibronic effects, which can be very challenging for traditional ab initio MD approaches. We demonstrate a workflow that enables efficient generation of MLIPs for the solvatochromic dye nile red system, in a variety of solvents. We use iterative active learning techniques to make this process as efficient as possible in terms of number and size of Density Functional Theory (DFT) calculations. Additionally, we compare the efficacy of various methodologies: generating distinct MLIPs for each adiabatic state, using one ground state MLIP in combination with delta-ML of excitation energies, and using a three-headed multiheaded ML model. To evaluate the validity of the resulting models, we compare predicted absorption and emission spectra to experimental spectra. We found that the incorporation of larger solvent systems into training data, and the use of delta models to predict the excitation energies, enables the accurate and affordable prediction of UV-Vis spectra with accuracy equivalent to the ground truth method, which is time-dependent DFT in this case.