CDS&E: D3SC: Applying Video Segmentation to Coarse-grain Mapping Operators in Molecular Simulations

罗切斯特大学的Andrew White和Chenliang Xu获得了化学学部化学理论、模型和计算方法项目的奖励，以应用计算机视觉的进步来改进化学中的多尺度系统模型。多尺度系统描述发生在许多不同时间和空间尺度上的化学和物理过程，例如，非常快和非常慢的运动都可能对整个过程有贡献。在视频的计算机处理和多尺度化学系统的建模中，通过去除无关细节来降低复杂性是必不可少的。如果不去除一些模型细节，模拟多尺度过程，如DNA转录或导致阿尔茨海默病斑块形成的肽聚集是不可能的。目前减少模型中原子数量的方法依赖于直觉和传统，因为原子可以被移除或组合的方式几乎是无限的。White、Xu和他们的研究小组正在开发一种基于视频分割技术进步的新方法。视频分割是在视频中识别前景、背景和对象的过程。令人惊讶的是，同样的数学结构可以应用于化学系统，这就是本研究的目标。怀特、徐和他们的合作者将通过一个面向学生的增强现实实验室，向更广泛的受众介绍这项研究。学生将能够决定如何简化分子模型，并通过将增强现实的视觉体验与分子模拟的交互性相结合来查看结果。粗粒度（CG）是一种用于更有效地模拟多尺度系统的降维技术。目前还没有一个严格的理论来生成从全原子（细颗粒）系统到CG系统的映射。这个缺失的部分是必不可少的，因为过去的CG工作表明，许多映射导致均匀的、弱相互作用的、类气体的CG模型，但可能的映射的数量是与原子数量相关的组合。Andrew White和他的合作者正在努力解决这个映射问题，他们(i)开发了一个理论来表示基于视频分割算法的映射算法；（ii）建立一个资料库，记录地图及其在基准模拟上的表现，以促进社区参与；（iii）在多蛋白表面相互作用方面研究和测试这些方法，这是目前制图方法难以做到的。在这方面取得成功，加上最近在计算CG势方面取得的进展，将更好地提高社区模拟复杂多尺度现象的能力。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。

Andrew White and Chenliang Xu of the University of Rochester is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to apply advances in computer vision to improve models of multiscale systems in chemistry. Multiscale systems describe chemical and physical processes that occur on many different time and spatial scales, for example, both very fast and very slow motions may contribute to the overall process. In both the computer processing of videos and the modeling of multiscale chemical systems, reducing complexity via removing extraneous details is essential. Without removing some model details, simulating multiscale processes like DNA transcription or the peptide aggregation which leads to plaque formation in Alzheimer's disease is impossible. Current approaches to reduce the number of atoms in a model rely on intuition and tradition due to the near infinite ways in which atoms can be removed or combined. White, Xu and their research groups are developing a novel approach built upon advances in video segmentation. Video segmentation is the process of identifying foreground, background, and objects in a video. Surprisingly, the same mathematical structure can be applied to chemical systems and that is the goal of this research. White, Xu and their collaborators will introduce the research to a broader audience via an augmented-reality laboratory for students. Students will be able to decide how to simplify molecular models and see the results by combining the visual experience of augmented-reality with the interactivity of molecular simulations. Coarse-graining (CG) is the dimension reduction technique used to simulate multiscale systems more efficiently. There is not a rigorous theory for generating mappings from all-atom (fine-grain) system to the CG system. This missing component is essential because past CG work shows that many mappings lead to homogeneous, weakly interacting, gas-like CG models but the number of possible mappings is combinatorial with respect to the number of atoms. Andrew White and his collaborators are working to solve this mapping problem by (i) developing a theory to represent mapping algorithms based on video segmentation algorithms; (ii) creating a database of mappings and their performance on benchmark simulations to foster community involvement; (iii) studying and testing these methods on multi-protein surface interactions, where current mapping approaches struggle. Achieving success here, along with recent advances in calculating CG potentials, will better advance the community's ability to model complex multiscale phenomena.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.