论文标题
端到端的AI框架，用于可解释的分子和晶体特性的预测
End-to-end AI framework for interpretable prediction of molecular and crystal properties
论文作者
论文摘要
我们介绍了一个端到端的计算框架，该框架允许使用Deephyper库，加速模型训练和可解释的AI推理进行超参数优化。该框架基于最新的AI模型，包括CGCNN，Physnet，Schnet，MPNN，MPNN-TransFormer和Torchmd-Net。我们使用这些AI模型以及基准QM9，HMOF和MD17数据集来展示模型如何在现代计算环境中预测用户指定的材料属性。我们证明了具有统一的独立框架的小分子，无机晶体和纳米多孔有机框架的模型中的可转移应用。我们已经在Argonne领导力计算设施的Thetagpu超级计算机中部署并测试了该框架，并在国家超级计算应用中心的Delta SuperComputer中，以为研究人员提供现代工具，以在领导级计算环境中进行加速AI-drive Discovery。我们将这些数字资产作为GitLab中的开源科学软件发布，并在Google Colab中发布了现成的Jupyter笔记本。
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.
