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

摘要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。它自然为跨学科研究提供了样本机会，这些研究需要数学、统计学、计算机科学、物理学、材料和生物学方面的知识。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。开发的工具集将大大扩展时间和系统大小的限制，而不会影响ab-initio模拟结果的精度和质量。查宏远、杜强、李润泽和Jorge Sofo将研究学习和计算方法：1)通过从初始模拟的轨迹中学习流形来描述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，这些簇对应于构型空间中自然出现的区域，这些区域在整个模拟过程中经常被粒子访问；3)使用具有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，将对插值势能面进行评估，当其精度足够好时，它将取代代价高昂的从头计算。随着模拟的发展，将重新测试插入的势能面，以检测在模拟探索构型空间的新区域时最终需要重新训练。

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.