Data-Driven Reduced-Order Modeling of Ab Initio Molecular Dynamics

从头算分子动力学的数据驱动降阶建模

• 批准号: 1953120
• 负责人: Xiantao Li
• 金额: $ 12万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1953120&HistoricalAwards=false
• 关键词: Data; Driven; Reduced; Order; Modeling

Many problems in modern material science, chemistry, and cell biology involve the structural changes at the microscopic level driven by external electrical fields. A quantitative description must take into account the underlying electronic structures and the induced atomic motions, leading to complex, large-dimensional dynamical systems, for which direct simulations are expensive. This project tackles this fundamental practical difficulty by developing efficient mathematical models to significantly reduce the computational cost. The reduction also enables the application of these models to much larger systems that are of direct practical interest. The project also provides research training opportunities for graduate students.This project aims to develop reduced-order modeling techniques within the framework of ab initio calculations. The goal is to avoid repeated calculations of the electronic structures so that the overall computation can be drastically sped up. Formulated as a Galerkin projection, the techniques project the electron dynamics to subspaces with much fewer degrees of freedom, while still retaining the important mapping between the external field and the dynamics of molecules and atoms. A statistical approach is proposed so that the models be inferred from a dataset containing atomic trajectories whenever they are available. This project also includes applications to materials with complex compositions and electrical properties of biological systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

现代材料科学、化学和细胞生物学中的许多问题都涉及到外部电场驱动下微观水平的结构变化。定量描述必须考虑到潜在的电子结构和诱导的原子运动，导致复杂的，大尺寸的动力学系统，直接模拟是昂贵的。该项目通过开发有效的数学模型来解决这一基本的实际困难，以显着降低计算成本。减少也使这些模型的应用程序更大的系统是直接的实际利益。该项目还为研究生提供研究培训机会。该项目旨在从头计算框架内开发降阶建模技术。这样做的目的是避免重复计算电子结构，从而大大加快整体计算速度。公式化为伽辽金投影，该技术将电子动力学投影到自由度少得多的子空间，同时仍然保留了外场与分子和原子动力学之间的重要映射。提出了一种统计方法，以便从包含原子轨迹的数据集推断模型，只要它们可用。该项目还包括生物系统的复杂成分和电性能材料的应用。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Petrov–Galerkin methods for the construction of non-Markovian dynamics preserving nonlocal statistics

用于构建保留非局部统计的非马尔可夫动力学的 PetrovGalerkin 方法

• DOI: 10.1063/5.0042679
• 发表时间: 2021
• 期刊: The Journal of Chemical Physics
• 作者: Lei, Huan;Li, Xiantao
• 通讯作者: Li, Xiantao

Random Batch Algorithms for Quantum Monte Carlo simulations

• DOI: 10.4208/cicp.oa-2020-0168
• 发表时间: 2020-06
• 期刊: ArXiv
• 作者: Shi Jin;Xiantao Li

Stochastic Algorithms for Self-consistent Calculations of Electronic Structures

• DOI: 10.1090/mcom/3826
• 发表时间: 2021-07
• 期刊: ArXiv
• 作者: Tae-Eon Ko;Xiantao Li