Paper ID: 2409.12015

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

Yuxinxin Chen, Pavlo O. Dral

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 this https URL 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 this https URL for updates).

Submitted: Sep 18, 2024