We introduce an end-to-end computational framework that allows for hyperparameter optimization using the DeepHyper library, accelerated model training, and interpretable AI inference. The framework is based on state-of-the-art AI models including CGCNN, PhysNet, SchNet, MPNN, MPNN-transformer, and TorchMD-NET. We employ these AI models along with the benchmark QM9, hMOF, and MD17 datasets to showcase how the models can predict user-specified material properties within modern computing environments. We demonstrate transferable applications in the modeling of small molecules, inorganic crystals and nanoporous metal organic frameworks with a unified, standalone framework. We have deployed and tested this framework in the ThetaGPU supercomputer at the Argonne Leadership Computing Facility, and in the Delta supercomputer at the National Center for Supercomputing Applications to provide researchers with modern tools to conduct accelerated AI-driven discovery in leadership-class computing environments. We release these digital assets as open source scientific software in GitLab, and ready-to-use Jupyter notebooks in Google Colab.

翻译：我们提出了一种端到端计算框架，该框架通过DeepHyper库实现超参数优化、加速模型训练与可解释人工智能推理。该框架基于包括CGCNN、PhysNet、SchNet、MPNN、MPNN-transformer和TorchMD-NET在内的前沿AI模型。我们采用这些AI模型，结合基准数据集QM9、hMOF和MD17，展示了模型在现代计算环境中预测用户指定材料特性的能力。我们通过统一且独立的框架，在小分子、无机晶体和纳米多孔金属有机框架的建模中展示了可迁移的应用。该框架已在阿贡领导力计算设施（ALCF）的ThetaGPU超级计算机和国家超级计算应用中心（NCSA）的Delta超级计算机上部署并测试，旨在为研究人员提供现代化工具，使其能够在领导级计算环境中开展加速型AI驱动的科学发现。我们将这些数字资产以开源科学软件形式发布在GitLab，并提供可在Google Colab中直接使用的Jupyter笔记本。