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

基本信息

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

项目摘要

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.

通过该奖项，化学系的CTMC计划是赖斯大学的Cecelia Clementi教授，以开发用于在各种规模上研究物质属性的模型。化学科学的一个基本挑战是弥合以原子量表研究和操纵物质的能力之间的差距，并希望以宏观的规模理解和预测特性。最近，用于实验观察的高通量和高分辨率技术已经大大增加了。此外，高性能技术的增加，可以在微观水平上模拟分子系统。这些进步导致了大量的高维数据。因此，最近对数据分析技术引起了人们的兴趣。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。这些仿真数据必须与实验数据进行核对。但是，除少数例外，这些和解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，很难扩展到明显更大，更复杂的系统。该项目开发了一种应对这一挑战的新的通用方法。这些方法适用于非常不同的化学系统，从细胞的信号转导，到异质催化到聚合物刷的设计。拟议的研究影响了化学，物理和数学研究人员的大型跨学科社区。该项目在应用于化学系统的计算和数学方法中进行了课程开发。课程开发包括本科和研究生课程。该项目还在招募和指导妇女和少数族裔本科生和研究生。该活动是通过与赖斯大学（Rice University）的Tapia中心合作进行的。该项目正在开发一个通用框架，以获得分子系统的有效动力学模型（结构，方程式和参数）。关键假设是，为了能够理解和建模宏观系统，需要使用纯描述模型来定义数据中的生成模型。模型是通过微观模拟和多尺度实验数据在中尺度上开发的。该方法与可用的粗粒技术或模型还原方法根本不同。宏观模型的形式以及有效的动态方程都可以从数据中学到。为了弥合显微镜和宏观系统之间的间隙，可以生成可以嵌入高阶模拟中的功能构建块。该方法调查了是否以及如何从不同化学系统中多种原子自由度的相互作用中出现相对一般的组织原理。这种建模方法具有将大量化学数据整合到定量，机械和可理解模型中的基石的潜力。这样的模型能够解释不同的分子成分如何在宏观尺度执行函数的时间和空间的函数中组织和相互作用。

项目成果

期刊论文数量（29）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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
影响因子：
0
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