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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：
1955403
负责人：
Jerome Delhommelle
金额：
$ 19.39万
依托单位：
University of North Dakota Main Campus
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-04-01 至 2022-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1955403&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和北达科他州大学的杰罗姆德尔霍梅勒得到了化学系化学理论、模型和计算方法项目的一个奖项的支持，以开发研究分子晶体的计算方法和软件。分子有序排列形成的结构称为分子晶体，在制药、农业化学、电子和国防工业中发挥着重要作用。在许多情况下，一种给定的化合物可能具有一种以上的晶体结构，这种现象称为多晶型。晶体也可能含有杂质，其中最重要的是水。这种结构被称为晶体水合物。这些材料以所需方式起作用的能力可取决于它们形成的结构，纯的或不纯的。如果一个精心设计的分子晶体转化为另一种形式，或者随着时间的推移它吸收了杂质，其性能可能严重降低。例如，这种转化可能导致药物失效或杀虫剂失去效力。另一方面，分子晶体中的多晶型和水合物形成是可以用来增强这些材料的性能的特征。利用高性能计算和人工智能的进步，理论分子科学目前正准备推动分子晶体工程的新方向。计算方法有可能在进行昂贵的实验或在制造特定材料方面进行大量投资之前突出与结构和成分可变性相关的潜在陷阱。为了实现这一潜力，塔克曼教授和Delhommelle教授提出创建新的计算方法和软件组件，用于快速预测分子晶体中的多晶型结构，并了解结构之间的转换。这些工具的广泛传播以及它们在材料设计和工程过程中的应用将缩短晶体系统的概念和实现之间的时间，并促进新课程材料的创建，以加强STEM教育。固态有机分子材料的基本性质通常受到其晶体结构的细节以及多晶型物和/或杂质（如水）的存在的强烈影响。这些结构的实验确定是昂贵和耗时的，这使得理论和计算的作用以及利用高性能计算机器学习方法的进步变得越来越重要。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，阐明多晶型和固-液相变的机制和热力学，以及绘制化学计量和非化学计量晶体水合物中水分子的有利位置。拟议的发展汇集了拓扑分析，机器学习，增强分子动力学，热力学和溶剂化理论的技术。该项目的主要目标是（1）创建一个拓扑理论，用于仅基于分子有序参数的晶体结构生成，从而绕过了对分子间相互作用模型参数化的需要，（2）开发新的基于熵和路径的集体变量，通过机器学习辅助，用于通过最先进的增强采样技术研究多态转变，以及（3）设计新的理论和计算技术来映射非化学计量晶体水合物中水分子的位置。这些工具和方法的广泛传播以及它们在晶体工程管道中的结合可以指示材料设计中富有成效的方向，从而减少具有所需特性的系统的概念和实现之间的时间，并导致为研究生水平课程创建新的学习模块，例如统计力学，材料科学，该奖项反映了NSF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。