Flexible On-the-Fly Active Learning for Molecular Infrared Spectra.
- 1Software for Chemistry & Materials BV , De Boelelaan 1109, Amsterdam1081 HV, The Netherlands.
- 2Vrije Universiteit Amsterdam , De Boelelaan 1108, Amsterdam1081 HZ, The Netherlands.
Abstract
Infrared spectroscopy serves as an indispensable analytical tool across materials science, pharmaceutical development, and the life sciences. Computational insights derived from first-principles methods enhance the interpretation of vibrational spectra, yet their application remains hindered by high computational cost. Machine-learned interatomic potentials offer a route to accelerate these predictions by multiple orders of magnitude without sacrificing ab initio accuracy. Here, we present an on-the-fly active learning framework for the automated training of such potentials specifically tailored to infrared spectral prediction. Our approach employs a multitask acquisition strategy, in which model performance is evaluated iteratively on designated objectives, avoiding the dependence on fixed training sets. We demonstrate that this multitask scheme creates interatomic potentials that exhibit robustness and fidelity in complex workflows for calculating infrared spectra.
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BibTeX
@article{benedini2026flexible,
title = {{Flexible On-the-Fly Active Learning for Molecular Infrared Spectra.}},
author = {Giulio Benedini and Matti Hellström and Lucas Visscher},
journal = {Journal of chemical theory and computation},
year = {2026},
month = oct,
doi = {10.1021/acs.jctc.6c00756},
url = {https://europepmc.org/article/MED/42917289},
note = {CC BY},
}
RIS
TY - JOUR
TI - Flexible On-the-Fly Active Learning for Molecular Infrared Spectra.
AU - Giulio Benedini
AU - Matti Hellström
AU - Lucas Visscher
PY - 2026
DA - 2026/10/01
JO - Journal of chemical theory and computation
DO - 10.1021/acs.jctc.6c00756
UR - https://europepmc.org/article/MED/42917289
AB - Infrared spectroscopy serves as an indispensable analytical tool across materials science, pharmaceutical development, and the life sciences. Computational insights derived from first-principles methods enhance the interpretation of vibrational spectra, yet their application remains hindered by high computational cost. Machine-learned interatomic potentials offer a route to accelerate these predictions by multiple orders of magnitude without sacrificing ab initio accuracy. Here, we present an on-the-fly active learning framework for the automated training of such potentials specifically tailored to infrared spectral prediction. Our approach employs a multitask acquisition strategy, in which model performance is evaluated iteratively on designated objectives, avoiding the dependence on fixed training sets. We demonstrate that this multitask scheme creates interatomic potentials that exhibit robustness and fidelity in complex workflows for calculating infrared spectra.
SN - 1549-9618
SN - 1549-9626
ER -