2024/09/18 by Yuxinxin Chen, Pavlo O. Dral, Chen, Yuxinxin +1 · 3 citations
Materials Science · #Chemical Physics (physics.chem-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science
paper · pdf · doi:10.48550/arxiv.2409.12015
openalex publication_date 2024/09/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 Δ-AIO-ANI based on Δ-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).