All-in-one foundational models learning across quantum chemical levels

TL;DR

This paper introduces a versatile all-in-one machine learning model for quantum chemistry that learns across multiple levels of theory, providing scalable, accurate, and robust foundational models for organic molecules.

Contribution

The paper presents the first all-in-one multimodal ML architecture capable of learning multiple quantum chemical levels, surpassing transfer learning in flexibility and scalability.

Findings

Achieved generalization comparable to semi-empirical and DFT methods.

Demonstrated learning across semi-empirical to coupled cluster QC levels.

Developed a more accurate { extDelta}-AIO-ANI foundational model.

Abstract

Machine learning (ML) potentials typically target a single quantum chemical (QC) level while the ML models developed for multi-fidelity learning have not been shown to provide scalable solutions for foundational models. Here we introduce the all-in-one (AIO) ANI model architecture based on multimodal learning which can learn an arbitrary number of QC levels. Our all-in-one learning approach offers a more general and easier-to-use alternative to transfer learning. We use it to train the AIO-ANI-UIP foundational model with the generalization capability comparable to semi-empirical GFN2-xTB and DFT with a double-zeta basis set for organic molecules. We show that the AIO-ANI model can learn across different QC levels ranging from semi-empirical to density functional theory to coupled cluster. We also use AIO models to design the foundational model {\Delta}-AIO-ANI based on {\Delta}-learning with increased accuracy and robustness compared to AIO-ANI-UIP. The code and the foundational models are available at https://github.com/dralgroup/aio-ani; they will be integrated into the universal and updatable AI-enhanced QM (UAIQM) library and made available in the MLatom package so that they can be used online at the XACS cloud computing platform (see https://github.com/dralgroup/mlatom for updates).