D3SC: EAGER: Data-driven design of molecular models from microscopic dynamics and experimental data

D3SC：EAGER：根据微观动力学和实验数据进行数据驱动的分子模型设计

• 批准号: 1738990
• 负责人: Anatoly Kolomeisky
• 金额: $ 30万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1738990&HistoricalAwards=false
• 关键词: D3SC; EAGER; Data; driven; design

With this award the CTMC program in the Division of Chemistry is funding Professor Cecelia Clementi at Rice University to develop models for studying properties of matter at various scales. A fundamental challenge for the chemical sciences is to bridge the gap between the ability to study and manipulate matter at the atomistic scale with the desire to understand and predict properties at a macroscopic scale. Recently, there has been an immense increase in high-throughput and high-resolution technologies for experimental observation. In addition there is an increase in high-performance techniques to simulate molecular systems at a microscopic level. These advances have resulted in a vast and ever-increasing amounts of high-dimensional data. Consequently, there is a recent surge of interest in data analysis techniques. In particular, techniques that extract essential features, collective variables or representative states from simulations. These simulation data have to be reconciles with experimental data. However, with very few exceptions, these reconciling techniques are purely descriptive and do not allow the formulation of general principles regulating the macroscopic behavior. Furthermore it is difficult to scale up towards significantly larger and more complex systems. This project develops a new and general approach to address this challenge. The approaches are applicable to very different chemical systems, ranging from signal transduction in cells, over heterogeneous catalysis to the design of polymer brushes. The proposed research impacts a large interdisciplinary community of students and researchers in Chemistry, Physics and Mathematics. The project undertakes curriculum development in computational and mathematical methods applied to chemical systems. Curriculum development includes undergraduate and graduate courses. The project is also recruiting and mentoring women and minority undergraduate and graduate students. This activity is conducted through a collaboration with the Tapia Center at Rice University.The project is developing a general framework to obtain the effective dynamical models (structure, equations and parameters) governing molecular systems. The key hypothesis is that, in order to be able to understand and model macroscopic systems, there is a need to use purely descriptive models to define generative models from data. Models are developed at the mesoscale from microscale simulations and multiscale experimental data. The approach is fundamentally different from available coarse-graining techniques or model reduction methods. Both the form of the macroscopic model as well as the effective dynamical equations are learned from data. Functional building blocks that can be embedded in higher order simulations are generated in order to bridge the gap between microscopic and macroscopic systems. The method investigates if and how relatively general organizing principles emerge from the interactions of a multitude of atomic degrees of freedoms in different chemical systems. This modeling approach has the potential to serve as a keystone to integrate vast amounts of chemical data into quantitative, mechanistic and comprehensible models. Such models are able to explain how different molecular components organize and interact as a function of time and space in performing functions at the macroscopic scale.

化学系的CTMC项目将资助莱斯大学的Cecelia Clementi教授开发模型，用于研究不同尺度下物质的性质。化学科学面临的一个基本挑战是弥合在原子尺度上研究和操纵物质的能力与在宏观尺度上理解和预测物质性质的愿望之间的差距。近年来，用于实验观测的高通量和高分辨率技术有了很大的发展。此外，在微观水平上模拟分子系统的高性能技术也有所增加。这些进步导致了大量且不断增加的高维数据。因此，最近对数据分析技术的兴趣激增。特别是从模拟中提取基本特征、集体变量或代表性状态的技术。这些模拟数据必须与实验数据相一致。然而，除了极少数例外，这些调和技术纯粹是描述性的，不允许制定调节宏观行为的一般原则。此外，它很难扩展到更大更复杂的系统。本项目开发了一种新的通用方法来应对这一挑战。这些方法适用于非常不同的化学系统，从细胞中的信号转导、多相催化到聚合物刷的设计。拟议的研究影响了化学，物理和数学领域的学生和研究人员的大型跨学科社区。该项目负责开发应用于化学系统的计算和数学方法的课程。课程开发包括本科和研究生课程。该项目还招募和指导女性和少数民族本科生和研究生。这项活动是通过与莱斯大学的塔皮亚中心合作进行的。该项目正在开发一个总体框架，以获得控制分子系统的有效动力学模型（结构、方程和参数）。关键的假设是，为了能够理解和建模宏观系统，需要使用纯粹的描述性模型来定义数据中的生成模型。中尺度模式是根据微尺度模拟和多尺度实验数据建立的。该方法从根本上不同于现有的粗粒度技术或模型简化方法。从数据中学习了宏观模型的形式和有效的动力学方程。可以嵌入高阶模拟的功能构建块是为了弥合微观和宏观系统之间的差距而生成的。该方法研究了在不同的化学系统中，是否以及如何从众多原子自由度的相互作用中产生相对一般的组织原理。这种建模方法有可能作为一个基石，将大量的化学数据整合到定量的、机械的和可理解的模型中。这样的模型能够解释不同的分子组分如何组织和相互作用作为时间和空间的函数在宏观尺度上执行功能。

项目成果

Preface: Special Topic on Reaction Pathways

前言：反应途径专题

• DOI: 10.1063/1.5007080
• 发表时间: 2017
• 期刊: The Journal of Chemical Physics
• 作者: Clementi, Cecilia;Henkelman, Graeme
• 通讯作者: Henkelman, Graeme

A Data-Driven Perspective on the Hierarchical Assembly of Molecular Structures

• DOI: 10.1021/acs.jctc.7b00990
• 发表时间: 2018-01-01
• 期刊: JOURNAL OF CHEMICAL THEORY AND COMPUTATION
• 影响因子: 5.5
• 作者: Boninsegna, Lorenzo;Banisch, Ralf;Clementi, Cecilia
• 通讯作者: Clementi, Cecilia

Extensible and Scalable Adaptive Sampling on Supercomputers

• DOI: 10.1021/acs.jctc.0c00991
• 发表时间: 2020-12-08
• 期刊: JOURNAL OF CHEMICAL THEORY AND COMPUTATION
• 影响因子: 5.5
• 作者: Hruska, Eugen;Balasubramanian, Vivekanandan;Clementi, Cecilia
• 通讯作者: Clementi, Cecilia

On the origin of phase transitions in the absence of symmetry-breaking

• DOI: 10.1016/j.physa.2018.10.001
• 发表时间: 2019-02-15
• 期刊: PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
• 影响因子: 3.3
• 作者: Pettini, Giulio;Gori, Matteo;Pettini, Marco
• 通讯作者: Pettini, Marco

Rapid Calculation of Molecular Kinetics Using Compressed Sensing

利用压缩感知快速计算分子动力学

• DOI: 10.1021/acs.jctc.8b00089
• 发表时间: 2018
• 期刊: Journal of Chemical Theory and Computation
• 影响因子: 5.5
• 作者: Litzinger, Florian;Boninsegna, Lorenzo;Wu, Hao;Nüske, Feliks;Patel, Raajen;Baraniuk, Richard;Noé, Frank;Clementi, Cecilia
• 通讯作者: Clementi, Cecilia