Fitxers
Tipus de document
ArticleVersió
Versió publicadaData de publicació
Llicència de publicació
Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/228405
Unlocking the predictive power of quantum-inspired representations for intermolecular properties in machine learning
Títol de la revista
Director/Tutor
ISSN de la revista
Títol del volum
Recurs relacionat
Resum
The quest for accurate and efficient Machine Learning (ML) models to predict complex molecular properties has driven the development of new quantum-inspired representations (QIR). This study introduces MODA (Molecular Orbital Decomposition and Aggregation), a novel QIR-class descriptor with enhanced predictive capabilities. By incorporating wave-function information, MODA is able to capture electronic structure intricacies, providing deeper chemical insight and improving performance in unsupervised and supervised learning tasks. Specially designed to be separable, the multi-moiety regularization technique unlocks the predictive power of MODA for both intra- and intermolecular properties, making it the first QIR-class descriptor capable of such distinction. We demonstrate that MODA shows the best performance for intermolecular magnetic exchange coupling (JAB) predictions among the descriptors tested herein. By offering a versatile solution to address both intra- and intermolecular properties, MODA showcases the potential of quantum-inspired descriptors to improve the predictive capabilities of ML- based methods in computational chemistry and materials discovery.
Citació
Citació
SANTIAGO, Raul, et al. Unlocking the predictive power of quantum-inspired representations for intermolecular properties in machine learning. Digital Discovery. 2024. Vol. 3, núm. 1, pàgs. 99-112. ISSN 2635-098X. [consulta: 13 de maig de 2026]. Disponible a: https://hdl.handle.net/2445/228405