All-in-one foundational models learning across quantum chemical levels
<|reference_start|>All-in-one foundational models learning across quantum chemical levels: 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).<|reference_end|>
@article{chen2024all-in-one,
    title={All-in-one foundational models learning across quantum chemical levels},
    author={Yuxinxin Chen and Pavlo O. Dral},
    journal={arXiv preprint arXiv:2409.12015},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12015},
    primaryClass={physics.chem-ph cs.LG}
}