发表机构
Princeton University; University of Southampton; Lawrence Berkeley National Laboratory; University of California, Berkeley; Riven Systems; Federal Institute of Materials Research and Testing; Globus, University of Chicago; Instituto de Nanociencia y Materiales de Aragón, CSIC-Universidad de Zaragoza; Argonne National Laboratory; The University of Chicago; Korea Advanced Institute of Science and Technology; Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University; D-Wave Quantum Inc.; R. F. Smith School of Chemical and Biomolecular Engineering, Cornell University; Massachusetts Institute of Technology; Flatiron Institute; Elemynt Pte. Ltd.; Washington University in St. Louis（普林斯顿大学; 南安普顿大学; 劳伦斯伯克利国家实验室; 加州大学伯克利分校; Riven Systems; 联邦材料研究与测试研究所; 芝加哥大学 Globus; 阿拉贡纳米科学与材料研究所，CSIC-萨拉戈萨大学; 阿贡国家实验室; 芝加哥大学; 韩国科学技术院; 哈佛大学约翰·A·保尔森工程与应用科学学院; D-Wave量子公司; 康奈尔大学 R.F. 史密斯化学与生物分子工程学院; 麻省理工学院; 平塔研究所; Elemynt私人有限公司; 圣路易斯华盛顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
QuAcc是一个开源工作流库，通过分离科学逻辑与工作流引擎，支持多种原子模拟方法，并统一评估基础机器学习势，降低了开发门槛，推动了AI时代的原子模拟民主化。
AI 中文摘要
我们提出了量子加速器（QuAcc），一个面向原子模拟的开源工作流库，重点支持量子力学计算。QuAcc提供了预定义的工作流配方，涵盖第一性原理电子结构方法、半经验与紧束缚方法、经典势函数以及基础机器学习原子间势（MLIPs）。QuAcc的一个核心设计特点是其将领域特定的科学逻辑与用于编排和执行计算的工作流引擎分离。工作流以普通Python函数编写，可在不修改底层源代码的情况下，通过多种支持的工作流引擎执行，从而降低了开发和贡献新工作流的门槛。QuAcc还通过提供一个统一平台来生成与目标模型一致的从头算参考计算，简化了基础MLIPs的评估，减轻了评估模型性能时的方法学漂移。总之，这些特性使QuAcc成为原子模拟工作流的灵活且易用的框架，这些工作流在当前机器学习和人工智能时代已成为核心。
英文摘要
We present the Quantum Accelerator (QuAcc), an open-source workflow library for atomistic simulations with an emphasis on quantum-mechanical calculations. QuAcc provides predefined workflow recipes spanning first-principles electronic-structure methods, semiempirical and tight-binding approaches, classical potentials, and foundation machine-learned interatomic potentials (MLIPs). A central design feature of QuAcc is its separation of domain-specific scientific logic from the workflow engine used to orchestrate and execute calculations. Workflows are written as ordinary Python functions and can be executed with multiple supported workflow engines without modifying the underlying source code, lowering the barrier to developing and contributing new workflows. QuAcc also streamlines the evaluation of foundation MLIPs by providing a unified platform for generating ab initio reference calculations consistent with the model of interest, mitigating methodological drift when assessing model performance. Together, these features make QuAcc a flexible and accessible framework for atomistic simulation workflows that have become central to the current era of machine learning and artificial intelligence.
