Advanced learning strategies for potential energy surfaces applied to organic electrolytes

应用于有机电解质的势能表面的高级学习策略

• 批准号: 497249646
• 负责人: Professor Dr. Christian Holm
• 金额: --
• 依托单位: Institut für Computerphysik (ICP)
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/497249646?language=en
• 关键词: Advanced; learning; strategies; potential; energy

In this project we will develop efficient inter-atomic potentials for organic electrolytes via machine-learning techniques from accurate DFT data. Our workflow aims at optimizing each stage of the development process including data generation and selection, model training and refinement through active learning, as well as uncertainty estimation for accurate deployment in production simulations. The outcome of this project will include a fully optimized workflow for the development of arbitrary inter-atomic potentials for molecular modelling, including long range interactions, as well as high-performance software suites capable of implementing this workflow. Beyond methodological improvements, our project will facilitate the development of almost ab-initio accurate inter-atomic potentials for organic electrolytes and therefore, highly accurate MD simulation which should yield important insights into the structure and dynamics of complex molecular liquids at the nanoscale and beyond.

在这个项目中，我们将通过机器学习技术从精确的DFT数据中开发有机电解质的有效原子间势。 我们的工作流程旨在优化开发过程的每个阶段，包括数据生成和选择，通过主动学习进行模型训练和改进，以及在生产模拟中准确部署的不确定性估计。 该项目的成果将包括一个完全优化的工作流程，用于开发用于分子建模的任意原子间势，包括长程相互作用，以及能够实现该工作流程的高性能软件套件。 除了方法上的改进，我们的项目将促进有机电解质的几乎从头算精确的原子间势的发展，因此，高度精确的MD模拟应该产生重要的见解复杂分子液体的结构和动力学在纳米级及以上。