AI 中文总结
研究针对现有MLIP软件碎片化问题，提出可扩展统一框架AI2Pot。通过重新设计核心计算，集成模型训练等与PyTorch生态，实现快速推理与灵活开发。提供工具包和API，为大规模MD的MLIP开发等提供用户友好的端到端框架。
AI 中文摘要
机器学习原子间势（MLIPs）兼具第一性原理计算的准确性和大规模分子动力学（MD）模拟所需的效率。然而，现有MLIP软件在不同模型架构上仍呈碎片化，难以建立支持灵活模型开发、高效训练和可扩展MD部署的统一工作流程。本文提出AI2Pot，一个可扩展的统一MLIP框架，它将模型训练、评估和大规模MD模拟与PyTorch兼容生态系统无缝集成。AI2Pot通过手工编写的C++/CUDA代码重新设计矩张量势（MTP）和神经进化势（NEP）的核心计算用于训练和推理，构成统一计算后端，提高训练-推理一致性并减少内存使用。结果表明，AI2Pot能在单个GPU上对包含数百万原子的大规模原子系统进行快速推理，同时保留PyTorch在模型构建、训练和评估方面的灵活性。训练好的模型可部署在ASE和LAMMPS中进行MD模拟。此外，AI2Pot还提供了配套的命令行工具包（AI2Pot-cli）和Python API以促进实际的MLIP工作流程。通过将高性能原子计算与现代机器学习生态系统统一起来，AI2Pot为大规模MD开发、训练和部署MLIP提供了一个用户友好的端到端框架。
英文摘要
Machine-learning interatomic potentials (MLIPs) bridge the accuracy of first-principles calculations and the efficiency required for large-scale molecular dynamics (MD) simulations. However, existing MLIP software remains fragmented across different model architectures, making it difficult to establish unified workflows that support flexible model development, efficient training, and scalable MD deployment. Here, we present AI2Pot, a scalable and unified MLIP framework that seamlessly integrates model training, evaluation, and large-scale MD simulations with PyTorch-compatible ecosystem. Instead of relying on generic automatic differentiation for expensive atomistic operators, AI2Pot re-engineers the core computations of Moment tensor potential (MTP) and Neuroevolution potential (NEP) for both training and inference using hand-crafted C++/CUDA code. These specialized operators constitute a unified computational backend shared by training and inference, improving training-inference consistency and reducing memory usage by avoiding large intermediate caches. As a result, AI2Pot enables fast inference for large-scale atomic systems containing millions of atoms on a single GPU, while retaining the flexibility of PyTorch for model construction, training, and evaluation. Trained models can be deployed in ASE and LAMMPS for MD simulations. Furthermore, AI2Pot provides a companion command-line toolkit (AI2Pot-cli) and Python APIs to facilitate practical MLIP workflows. By unifying high-performance atomistic computing with modern machine-learning ecosystems, AI2Pot offers an user-friendly end-to-end framework for the developing, training, and deploying MLIPs for large scale MD.
