Collaborative Research:CDS&E:D3SC:Topology, Rare-event Simulation, and Machine Learning as Routes to Predicting Molecular Crystal Structures and Understanding Their Phase Behav

合作研究：CDS

基本信息

批准号：
1955381
负责人：
Mark Tuckerman
金额：
$ 55.65万
依托单位：
New York University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-04-01 至 2024-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1955381&HistoricalAwards=false
关键词：
Collaborative Research CDS&amp D3SC Topology

项目摘要

Mark Tuckerman of New York University and Jerome Delhommelle of the University of North Dakota are supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop computational methods and software to study molecular crystals. Ordered arrays of molecules forming structures known as molecular crystals play an essential role in the pharmaceutical, agrochemical, electronics, and defense industries. In many instances, a given chemical compound may have more than one crystal structure, a phenomenon known as polymorphism. A crystal may also contain impurities, the most important among these being water. Such structures are referred to as crystal hydrates. The ability of these materials to function in a desired manner may depend on which structure, pure or impure, they form. If a well-engineered molecular crystal converts to another form or if it absorbs impurities over time., its performance may be seriously degraded. Such transformations can, for example, cause drugs to fail or insecticides to lose their potency. On the other hand, polymorphism and hydrate formation in molecular crystals are features that can be exploited to enhance the performance of these material. Utilizing advances in high-performance computing and artificial intelligence, the theoretical molecular sciences are currently poised to drive new directions in molecular crystal engineering. Computational approaches have the potential to highlight potential pitfalls associated with structural and compositional variability before expensive experiments are performed or large investments in manufacturing a particular material are made. With the aim of realizing this potential, Professors Tuckerman and Delhommelle propose to create new computational approaches and software components for rapidly predicting polymorphic structures in molecular crystals and understanding the transitions between structures. Broad dissemination of these tools and their incorporation into the materials design and engineering processes will affect a reduction in time between concept and realization of crystal systems with desired optimal properties and will catalyze the creation of new course materials for enhancing STEM education. The basic properties of organic molecular materials in the solid state are often strongly influenced by the details of their crystal structures and the existence of polymorphs and/or impurities such as water. Experimental determination of these structures is costly and time-consuming, which places increased importance on the role of theory and computation and the leveraging of advances in high-performance computing machine learning methods. The aim of this project is to develop a suite of new methods and software tools for the prediction of organic molecular crystal structures, including multiple polymorphs, elucidation of the mechanisms and thermodynamics of polymorphic and solid-liquid phase transitions, and the mapping of favored locations for water molecules in stoichiometric and non-stoichiometric crystal hydrates. The proposed developments bring together techniques of topological analysis, machine learning, enhanced molecular dynamics, thermodynamics, and solvation theories. The main goals of the project are (1) to create a topological theory for crystal structure generation based on solely on molecular order parameters, thus bypassing the need to parameterize an intermolecular interaction model, (2) to develop new entropy- and path-based collective variables, aided by machine learning , for studying polymorphic transitions via state-of-the-art enhanced sampling techniques, and (3) to devise new theoretical and computational techniques for mapping the locations of water molecules in non-stoichiometric crystal hydrates. Broad dissemination of these tools and methods and their incorporation into crystal engineering pipelines could indicate fruitful directions in materials design, thus effecting a reduction in time between concept and realization of systems with desired properties and lead to the creation of new learning modules for graduate level courses in topics such as statistical mechanics, science of materials, and machine learning in the molecular sciences.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.

纽约大学的马克·塔克曼（Mark Tuckerman）和北达科他大学（University of North Dakota）大学的杰罗姆·德尔霍梅尔（Jerome Delhommelle）得到了化学理论，模型和计算方法计划的奖项，以开发用于研究分子晶体的计算方法和软件。分子的有序阵列形成称为分子晶体的结构在药物，农业，电子和防御工业中起着至关重要的作用。在许多情况下，给定的化合物可能具有多个晶体结构，一种被称为多态性的现象。晶体也可能包含杂质，其中最重要的是水。这样的结构称为晶体水合物。这些材料以所需方式运行的能力可能取决于它们形成的结构，纯或不纯净。如果设计良好的分子晶体转化为另一种形式，或者随着时间的推移吸收杂质。它的性能可能会严重降解。例如，这种转化可能导致药物失败或杀虫剂失去效力。另一方面，分子晶体中的多态性和水合形成是可以利用的特征，以增强这些材料的性能。利用高性能计算和人工智能的进步，理论分子科学目前有望驱动分子晶体工程的新方向。计算方法有可能突出与结构和组成变异性相关的潜在陷阱，然后进行昂贵的实验或进行特定材料的大量投资。为了意识到这一潜力，塔克曼教授和德尔霍姆尔教授建议创建新的计算方法和软件组件，以快速预测分子晶体中的多态性结构，并了解结构之间的过渡。这些工具及其在材料设计和工程过程中的广泛传播将影响概念和具有所需最佳特性的晶体系统之间的时间的减少，并将催化创建新课程材料来增强STEM教育。固态中有机分子材料的基本特性通常受其晶体结构的细节以及多晶型和/或杂质（例如水）的存在的强烈影响。这些结构的实验性确定是昂贵且耗时的，这使得理论和计算的作用以及在高性能计算机学习方法中的进步的杠杆作用提高了重要性。该项目的目的是开发一套新的方法和软件工具，用于预测有机分子晶体结构，包括多种多晶型物，阐明机制和固液相过渡的机制和热力学，以及在石化和非晶体中的水分分子的偏爱位置映射的映射。提出的发展汇集了拓扑分析，机器学习，增强的分子动力学，热力学和溶剂化理论的技术。 The main goals of the project are (1) to create a topological theory for crystal structure generation based on solely on molecular order parameters, thus bypassing the need to parameterize an intermolecular interaction model, (2) to develop new entropy- and path-based collective variables, aided by machine learning , for studying polymorphic transitions via state-of-the-art enhanced sampling techniques, and (3) to devise new theoretical and computational绘制水分子位置中非化学计量水合水合物的位置的技术。这些工具和方法的广泛传播及其将其纳入水晶工程管道中可能表明材料设计中的富有成效的方向，从而影响概念和实现具有所需特性的系统之间的时间，并导致为研究生水平的新学习模块创建新的学习模块。值得通过基金会的智力优点和更广泛的影响审查标准来通过评估来支持。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Crystal Structure Predictions for 4-Amino-2,3,6-trinitrophenol Using a Tailor-Made First-Principles-Based Force Field

使用定制的基于第一性原理的力场预测 4-氨基-2,3,6-三硝基苯酚的晶体结构

DOI：
10.1021/acs.cgd.1c01117
发表时间：
2022
期刊：
Crystal Growth & Design
影响因子：
3.8
作者：
Metz, Michael P.;Shahbaz, Muhammad;Song, Hongxing;Vogt-Maranto, Leslie;Tuckerman, Mark E.;Szalewicz, Krzysztof
通讯作者：
Szalewicz, Krzysztof

Insights into the Polymorphic Structures and Enantiotropic Layer-Slip Transition in Paracetamol Form III from Enhanced Molecular Dynamics

DOI：
10.1021/acs.cgd.0c01250
发表时间：
2021-01-05
期刊：
CRYSTAL GROWTH & DESIGN
影响因子：
3.8
作者：
Hong, Richard S.;Chan, Eric J.;Tuckerman, Mark E.
通讯作者：
Tuckerman, Mark E.

Generating Cocrystal Polymorphs with Information Entropy Driven by Molecular Dynamics-Based Enhanced Sampling

基于分子动力学的增强采样驱动的信息熵生成共晶多晶型物

DOI：
10.1021/acs.jpclett.0c02647
发表时间：
2020
期刊：
The Journal of Physical Chemistry Letters
影响因子：
0
作者：
Song, Hongxing;Vogt-Maranto, Leslie;Wiscons, Ren;Matzger, Adam J.;Tuckerman, Mark E.
通讯作者：
Tuckerman, Mark E.

Imaginary-Time Open-Chain Path-Integral Approach for Two-State Time Correlation Functions and Applications in Charge Transfer

二态时间相关函数的虚时间开链路径积分方法及其在电荷转移中的应用