ChemRefine:一个开源自动化和可互操作的平台,用于机器学习和量子化学模拟
Ignacio Migliaro1, Markus G S Weiss1,2, Alistair J Sterling1
1Department of Chemistry and Biochemistry, The University of Texas at Dallas, 800 W Campbell Road, Richardson, Texas 75080, United States.
Journal of chemical theory and computation
|February 10, 2026
概括
ChemRefine将机器学习的原子间潜力 (MLIP) 集成到计算化学工作流程中,加速模拟. 一个定制的大型语言模型,ChemRefineGPT,简化了使用自然语言描述创建工作流.
科学领域:
- 计算化学的计算化学
- 机器学习 机器学习
- 科学软件开发科学软件开发
背景情况:
- 机器学习在计算化学中越来越多地用于加快任务和改进预测.
- 将新的机器学习的原子间潜力 (MLIP) 集成到现有的量子力学 (QM) 工作流中是一个重大挑战.
研究的目的:
- 介绍ChemRefine,这是一个用于自动化计算化学工作流程的模块化平台.
- 能够将先进的MLIP无集成到复杂和高通量计算任务中.
- 使用自然语言处理简化工作流的创建.
主要方法:
- 开发了ChemRefine,一个模块化自动化计算化学平台.
- 集成的MLIP用于自动化培训,微调和在工作流程中部署.
- 使用ChemRefineGPT,一个定制的大型语言模型,用于从自然语言描述生成输入和配置文件.
主要成果:
- 成功地在路上测试了ChemRefine在各种任务上:宿主-客串绑定,属性预测,构造性采样和过渡状态发现.
- 使用MLIPs证明了加速模拟,并保持了预测准确度.
- 将多个计算任务缩小到单个,可重复的工作流.
结论:
- ChemRefine提供了一个强大的解决方案,用于将下一代MLIP集成到计算化学中.
- 该平台提高了各种科学应用的效率和可重复性.
- ChemRefineGPT显著降低了利用复杂的计算工作流的进入障碍.
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