一个模型搞定多种量子化学精度，让分子模拟更高效。
All-in-one foundational models learning across quantum chemical levels
- 用多模态学习统一建模不同量子化学精度
- 可跨半经验、DFT到耦合簇方法，性能媲美GFN2-xTB和高精度DFT
- 适合需要多精度模拟的分子研究者，支持在线使用
机器学习势能通常只针对单一量子化学级别，而现有的多保真度学习方法尚未为基础模型提供可扩展的解决方案。本文提出基于多模态学习的全一体化（AIO）ANI模型架构，可学习任意数量的量子化学级别。该方法为迁移学习提供了更通用、更易用的替代方案。我们基于此训练了AIO-ANI-UIP基础模型，其对有机分子的泛化能力与半经验方法GFN2-xTB及采用双zeta基组的密度泛函理论（DFT）相当。结果表明，AIO-ANI模型可跨越从半经验方法到密度泛函理论再到耦合簇方法的不同量子化学级别。此外，我们还利用Δ-学习构建了更高精度与鲁棒性的Δ-AIO-ANI基础模型。代码与模型已开源（https://github.com/dralgroup/aio-ani），并将集成至通用可更新的AI增强量子化学库（UAIQM）和MLatom工具包，支持在XACS云平台在线调用。
原文摘要 · Abstract (English)
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).
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