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

• 批准号: 2240526
• 负责人: Jerome Delhommelle
• 金额: $ 19.39万
• 依托单位: University of Massachusetts Lowell
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2240526&HistoricalAwards=false
• 关键词: Collaborative; Research; CDS& D3SC; Topology

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·德尔霍梅勒获得了化学部化学理论、模型和计算方法项目的支持，以开发研究分子晶体的计算方法和软件。有序的分子阵列形成了被称为分子晶体的结构，在制药、农用化学品、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不止一种晶体结构，这一现象称为多态。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。这些材料以期望的方式发挥作用的能力可能取决于它们形成的结构，无论是纯的还是不纯的。如果设计良好的分子晶体转变为另一种形式，或者如果随着时间的推移它吸收了杂质，它的性能可能会严重退化。例如，这种转变可能会导致药物失效或杀虫剂失去效力。另一方面，分子晶体中的多态和水合物的形成是可以用来提高这些材料性能的特征。利用高性能计算和人工智能的进展，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笔投资之前，突出与结构和成分变化相关的潜在陷阱。为了实现这一潜力，塔克曼和德尔霍梅勒教授建议创造新的计算方法和软件组件，以快速预测分子晶体中的多态结构，并了解结构之间的转变。广泛传播这些工具并将其纳入材料设计和工程过程，将影响缩短从构想到实现具有所需最佳性能的晶体系统的时间，并将促进为加强STEM教育而编写新的课程材料。有机分子材料在固体状态下的基本性质往往受到其晶体结构的细节以及水等多晶型和/或杂质的存在的强烈影响。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高性能计算机器学习方法的进步变得更加重要。本项目的目的是开发一套新的方法和软件工具来预测有机分子的晶体结构，包括多种多晶型，阐明多晶型和固-液相转变的机理和热力学，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图。拟议的发展结合了拓扑分析、机器学习、增强的分子动力学、热力学和溶剂化理论等技术。该项目的主要目标是(1)建立一个完全基于分子序参数的晶体结构生成的拓扑理论，从而避免对分子间相互作用模型进行参数化的需要；(2)在机器学习的辅助下，开发新的基于熵和路径的集体变量，用于通过最先进的增强采样技术来研究多态转变；以及(3)设计新的理论和计算技术来绘制非化学计量晶体水合物中水分子的位置图。广泛传播这些工具和方法，并将它们纳入晶体工程管道，可以在材料设计方面指明富有成效的方向，从而缩短具有所需性能的系统的概念和实现之间的时间，并导致为分子科学中的统计力学、材料科学和机器学习等主题的研究生课程创建新的学习模块。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。