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

量子效应是材料性质和化学过程的内在因素。从头算半经典分子动力学模拟以良好的定量精度捕获量子效应，是广泛的化学和材料科学研究的普遍适用的研究工具，包括污染物对肺部健康的影响的研究，酶催化，臭氧消耗，航天器表面涂层，太阳能电池，以及许多有望推进国民健康和制药科学的研究，国防材料设计研究，能源与环保研究等。但是，半经典动力学模拟的计算成本非常高，使得半经典动力学非常具有挑战性，在许多情况下，对于大分子系统甚至是不可行的。本项目提出了在保持模拟精度的同时降低计算成本的方法，将半经典动力学研究的范围扩大到更广泛的具有国家和科学重要性的研究。从头算半经典分子动力学模拟在量子力学电子结构理论基础上从头算黑森数的计算成本巨大。Hessian建模使用一组保存的从头算数据在最近的时间距离内的训练数据，成功地降低了Hessian计算的成本，同时保持了仿真的准确性。观察到在最近的空间距离上使用训练数据有进一步降低计算成本的机会，这为Hessian模型替代从头算Hessian模型提供了更多的机会。由于新的从头算数据的频繁传入，从头算数据集不断增长。为了搜索频繁更新的增长数据集，一个挑战是算法不仅需要实现高搜索效率，而且必须能够有效地重新组织频繁插入新数据的数据集。现有的搜索算法都是针对静态或不频繁更新的数据集设计的，搜索效率高，但数据组织效率不高。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来提供高效率的搜索和数据组织的算法。使用新搜索算法返回的最近空间距离训练数据的Hessian建模有可能进一步降低计算成本，有望加快动力学模拟，并能够模拟更大的分子系统和/或使用更高精度的电子结构理论来捕获分子系统的更好细节。该奖项反映了美国国家科学基金会的法定使命，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。

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.