Mimyria: Machine learned vibrational spectroscopy for aqueous systems made simple

Abstract

Vibrational spectroscopy provides a powerful connection between molecular dynamics (MD) simulations and experiment, but its routine use in condensed-phase systems remains limited. We introduce mimyria, a modular and automated framework that orchestrates electronic-structure reference calculations, trains atom-resolved machine-learning response models, and generates IR and Raman spectra from MD trajectories within a unified workflow. We introduce the polarizability gradient tensor (PGT) as a novel atom-resolved machine-learning target property for Raman spectroscopy, complementing the established atomic polar tensor (APT) for IR spectroscopy. As a necessary prerequisite, we demonstrate how both PGTs and APTs can accurately be computed from electronic-structure theory, validate them across formally equivalent derivative formulations, and thereby benchmark their numerical consistency. We then employ machine learning as an efficient surrogate to represent the validated APT and PGT response functions on aqueous benchmark systems. We validate the trained models directly at the level of the spectrum against explicit ab initio reference calculations and find that IR and Raman spectra converge with surprisingly small training sets. Moreover, spectral agreement improves more rapidly than the root-mean-square error (RMSE). While RMSE is straightforward to compute, statistically converged reference spectra are generally impractical to obtain, motivating the need to relate model-level errors to observable-level accuracy. By connecting these complementary error measures, we provide practical guidelines and early-stopping criteria for achieving sufficient spectral fidelity. By integrating response-tensor learning, automated training, and spectral-domain validation into a unified workflow, mimyria enables data-efficient and quantitatively reliable vibrational spectroscopy.