CAMLET: A Combined Ab-initio Manifold Learning Toolbox for Nanostructure Simulations

CAMLET：用于纳米结构模拟的组合从头算流形学习工具箱

• 批准号: 0430349
• 负责人: Runze Li
• 金额: --
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-10-01 至 2008-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0430349&HistoricalAwards=false
• 关键词: CAMLET; Combined; Ab; initio; Manifold

AbstractMolecular Dynamics simulations are powerfultools to study problems of materials science, nanoscience, and biology. It naturally providesample opportunities for interdisciplinary researchthat requires knowledge in mathematics, statistics, computer science, physics, materials and biology.The focus of this project is on developing learning-based computational and statistical methods for potential energy landscape modeling to accelerate ab-initio molecular dynamics simulations. The set of tools developed will substantially expand the limits of time and system size without compromising the precision andquality of the ab-initio simulation results.Hongyuan Zha, Qiang Du, Runze Li and Jorge Sofo willinvestigate learning andcomputational methods 1) to characterize both the local and globalstructures of the low-dimensional manifold in which the simulationreally occurs through manifold learning from the trajectories of theab-initio simulation; 2) to identify and extract suitable clusters inthe reduced dimension spaces corresponding to regions in theconfiguration space that naturally emerge from the ab-initiosimulation and are visited frequently by the particles throughout thesimulation; 3) to conduct efficient energy and force interpolationusing Gaussian Kriging models with penalized likelihood. In thislearning and computationalframework, the interpolated potential energy surface will beevaluated and it will replace the costly ab-initio evaluation when itsprecision is good enough. As the simulation evolves, the interpolatedpotential energy surface will be retested to detect theeventual need of a retraining in case the simulation is exploringnew regions of the configuration space.

分子动力学模拟是研究材料科学、纳米科学和生物学问题的有力工具。它自然为需要数学、统计学、计算机科学、物理学、材料和生物学知识的跨学科研究提供了充足的机会。该项目的重点是开发基于学习的计算和统计方法，用于势能景观建模，以加速从头算分子动力学模拟。Hongyuan Zha，Qiang Du，Runze Li and Jorge Sofo将研究学习和计算方法1）通过从从头算模拟的轨迹中进行流形学习来表征模拟真实发生的低维流形的局部和全局结构; 2）在降维空间中识别和提取合适的簇，这些簇对应于从头算模拟中自然出现的、粒子在整个模拟过程中频繁访问的配置空间区域; 3）使用惩罚似然的高斯克里格模型进行有效的能量和力插值。在这个学习和计算框架中，插值势能面将被评估，当它的精度足够好时，它将取代昂贵的从头计算评估。随着模拟的发展，插值势能面将被重新测试，以检测在模拟正在探索配置空间的新区域的情况下重新训练的最终需要。