Issue 1, 2024

Unlocking the predictive power of quantum-inspired representations for intermolecular properties in machine learning

Abstract

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.

Graphical abstract: Unlocking the predictive power of quantum-inspired representations for intermolecular properties in machine learning

Supplementary files

Article information

Article type
Paper
Submitted
20 sep 2023
Accepted
13 nov 2023
First published
14 nov 2023
This article is Open Access
Creative Commons BY-NC license

Digital Discovery, 2024,3, 99-112

Unlocking the predictive power of quantum-inspired representations for intermolecular properties in machine learning

R. Santiago, S. Vela, M. Deumal and J. Ribas-Arino, Digital Discovery, 2024, 3, 99 DOI: 10.1039/D3DD00187C

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. You can use material from this article in other publications, without requesting further permission from the RSC, provided that the correct acknowledgement is given and it is not used for commercial purposes.

To request permission to reproduce material from this article in a commercial publication, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party commercial publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements