CDS&E: Fast Search of Growing High-Dimensional Big Data to Enable Accurate Semiclassical Molecular Dynamics Studies of Large Molecular Systems

CDS

• 批准号: 2103563
• 负责人: Yu Zhuang
• 金额: $ 27.83万
• 依托单位: Texas Tech University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2103563&HistoricalAwards=false
• 关键词: CDS& Fast; Search; Growing; Dimensional

Quantum effects are inherent factors for material properties and chemical processes. By capturing quantum effects with good quantitative accuracy, ab initio semiclassical molecular dynamics simulation is a generally applicable investigation tool for a broad range of chemical and material science studies, including studies on pollutant effects on lung health, enzyme catalysis, ozone depletion, space craft surface coating, solar cells, and a lot more studies that promise to advance national health and pharmaceutical sciences, material design investigations for national defense, energy and environmental protection researches, etc. But the computation cost of semiclassical dynamics simulations is enormously high, making semiclassical dynamics highly challenging, and even infeasible in many cases, for large molecular systems. This project proposed methods for reducing computation cost while maintaining simulation accuracy, which will expand the reach of semiclassical dynamics study to a broader range of studies of national and scientific importance. Ab initio semiclassical molecular dynamics simulation has enormous computation cost in calculating ab initio Hessians from quantum mechanical electronic structure theories. Hessian modeling using training data in the closest time distances from a set of saved ab initio data has been successful in reducing the cost of Hessian calculations while maintaining simulation accuracy. It was observed that opportunities exist for further reduction of computation cost by using training data in the closest spatial distances, which offers more chances for Hessian modeling to replace ab initio Hessian. Due to the frequent incoming of new ab initio data, the ab initio data set is constantly growing. To search frequently updated growing datasets, a challenge is that the algorithms not only need to achieve high search efficiency but also have to be efficient for re-organizing the dataset with frequent insertions of new data. Existing searching algorithms are good in search efficiency but not so good in data-organizing efficiency since they were designed for static or infrequently updated datasets. This project develops search algorithms that will be the first to leverage the growing process of datasets to deliver high efficiency in both searching and data organizing. Hessian modeling using training data of closest spatial distance returned by the new search algorithms has the potential for further reduction of computation cost, promising to speed up dynamics simulations and enable simulations of larger molecular systems and/or the use of higher-accuracy electronic structure theories to capture better details of the molecular 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.

量子效应是材料性质和化学过程的内在因素。通过以良好的定量精度捕获量子效应，从头算半经典分子动力学模拟是广泛的化学和材料科学研究的普遍适用的调查工具，包括污染物对肺部健康的影响，酶催化，臭氧消耗，航天器表面涂层，太阳能电池的研究，以及更多有望推动国家健康和制药科学的研究，但是半经典动力学模拟的计算成本非常高，使得半经典动力学对于大分子系统具有很大的挑战性，甚至在许多情况下是不可行的。该项目提出了在保持模拟精度的同时降低计算成本的方法，这将把半经典动力学研究的范围扩大到具有国家和科学重要性的更广泛的研究领域。从头算半经典分子动力学模拟在从量子力学电子结构理论计算从头算海森时，计算量巨大。Hessian建模使用来自一组保存的从头算数据的最近时间距离中的训练数据已经成功地降低了Hessian计算的成本，同时保持了模拟精度。据观察，通过使用最近空间距离的训练数据，存在进一步降低计算成本的机会，这为Hessian建模提供了更多机会来取代从头Hessian。由于新的从头算数据的频繁到来，从头算数据集不断增长。 为了搜索频繁更新的不断增长的数据集，一个挑战是，算法不仅需要实现高搜索效率，而且还必须有效地重新组织数据集与频繁插入的新数据。现有的搜索算法在搜索效率上是很好的，但由于它们是针对静态或不频繁更新的数据集而设计的，因此在数据组织效率上并不那么好。该项目开发的搜索算法将是第一个利用数据集不断增长的过程，以提供高效率的搜索和数据组织。使用由新搜索算法返回的最近空间距离的训练数据的海森建模具有进一步降低计算成本的潜力，有望加速动力学模拟，并使模拟更大的分子系统和/或使用更高的-准确的电子结构理论，以捕捉更好的分子系统的细节。这一奖项反映了NSF的法定使命，并已被认为是值得支持，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。

项目成果

Machine Learning-based Vulnerability Study of Interpose PUFs as Security Primitives for IoT Networks

基于机器学习的插入 PUF 作为物联网网络安全原语的漏洞研究

• DOI: 10.1109/nas51552.2021.9605405
• 发表时间: 2021
• 期刊: Architecture and Storage (NAS
• 作者: Thapaliya, Bipana;Mursi, Khalid T.;Zhuang, Yu
• 通讯作者: Zhuang, Yu

A Permutation Challenge Input Interface for Arbiter PUF Variants Against Machine Learning Attacks

• DOI: 10.1109/isvlsi54635.2022.00094
• 发表时间: 2022-07
• 期刊: 2022 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)
• 作者: Yu Zhuang;Gaoxiang Li;Khalid T. Mursi