Ai2-Kit: 为复杂化学系统简化AI加速从头算工作流
Ai2-Kit: Streamlining AI-Accelerated Ab Initio Workflows for Complex Chemical Systems
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中文总结 AI 辅助
提出ai2-kit工具包，通过命令行和Python API整合第一性原理计算、机器学习势训练、分子动力学、增强采样和HPC编排，实现复杂化学系统的AI加速从头算工作流自动化与可扩展。
中文摘要 AI 辅助
复杂化学系统（如催化、电化学和能量存储）的分子模拟通常需要捕捉电子结构、有限温度涨落和电场响应等效应的相互作用。这种复杂性难以通过传统的从头算计算解决，因为后者受限于其所能达到的时间和长度尺度。AI加速从头算（AI2）方法使用基于第一性原理数据训练的机器学习势来替代昂贵的电子结构计算，将从头算精度扩展到这些领域，但其常规应用需要可靠的工作流，连接第一性原理计算、模型训练、分子动力学、增强采样、轨迹分析和HPC编排。这里我们介绍ai2-kit，一个用于开发可访问、可重现和可扩展的AI2工作流的软件工具包。ai2-kit提供高语义密度的命令行界面和Python API，用于结构和数据集转换、批量任务生成、主动学习筛选、作业编排和工作流恢复。我们在四个代表性应用中演示了ai2-kit：基于主动学习的机器学习势构建、氧化还原和酸碱过程的自由能微扰、带电界面的电化学机器学习势，以及来自机器学习分子动力学的光谱学。ai2-kit还提供AI代理技能，帮助用户将这些用例适应为其自身化学系统和计算软件栈定制的自定义工作流。总之，ai2-kit帮助将AI2方法从特制的计算协议转变为复杂化学系统的可重用和可扩展工作流，从模型构建到性质预测。
英文摘要
Molecular simulations of complex chemical systems, such as catalysis, electrochemistry, and energy storage, often need to capture the interplay of effects such as electronic structure, finite-temperature fluctuations, and electric-field response. Such complexity is difficult to address with traditional ab initio calculations, which are limited by the time and length scales they can reach. AI-accelerated ab initio (AI2) methods use machine learning potentials trained on first-principles data to replace expensive electronic-structure calculations, extending ab initio accuracy to these regimes, but their routine application requires reliable workflows that connect first-principles calculations, model training, molecular dynamics, enhanced sampling, trajectory analysis, and HPC orchestration. Here we present ai2-kit, a software toolkit for developing accessible, reproducible, and extensible AI2 workflows. ai2-kit provides high-semantic-density command-line interfaces and Python APIs for structure and dataset conversion, batch task generation, active-learning screening, job orchestration, and workflow recovery. We demonstrate ai2-kit in four representative applications: active-learning-based machine learning potential construction, free-energy perturbation for redox and acid-base processes, electrochemical machine learning potentials for electrified interfaces, and spectroscopies from machine learning molecular dynamics. ai2-kit also provides AI-agent skills that help users adapt these use cases into customized workflows for their own chemical systems and computational software stacks. Together, ai2-kit helps turn AI2 methods from bespoke computational protocols into reusable and extensible workflows for complex chemical systems, from model construction to property prediction.
