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

纽约大学的Mark Tuckerman和北达科他州大学的Jerome Delhommelle获得了化学系化学理论、模型和计算方法项目的奖励，以开发研究分子晶体的计算方法和软件。分子有序排列形成被称为分子晶体的结构在制药、农化、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有不止一种晶体结构，这种现象被称为多态。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纯或不纯结构。如果一个精心设计的分子晶体转变成另一种形式，或者随着时间的推移它吸收了杂质。，其性能可能会严重下降。例如，这种转变可能导致药物失效或杀虫剂失去效力。另一方面，分子晶体中的多态性和水合物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步，理论分子科学目前正准备推动分子晶体工程的新方向。在进行昂贵的实验或在制造特定材料上进行大量投资之前，计算方法有可能突出与结构和成分变化相关的潜在缺陷。为了实现这一潜力，Tuckerman教授和Delhommelle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晶结构并理解结构之间的转变。这些工具的广泛传播及其与材料设计和工程过程的结合将缩短具有理想最佳性能的晶体系统的概念和实现之间的时间，并将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和多晶体和/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。该项目的目标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，以及在化学计量和非化学计量晶体水合物中优选水分子的位置。提出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)创建晶体结构生成的拓扑理论，仅基于分子顺序参数，从而绕过了参数化分子间相互作用模型的需要；(2)在机器学习的帮助下，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研究多态转变。(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。这些工具和方法的广泛传播以及它们与晶体工程管道的结合可以为材料设计指明富有成效的方向，从而减少具有所需特性的系统的概念和实现之间的时间，并导致在统计力学、材料科学和分子科学中的机器学习等主题中为研究生水平课程创建新的学习模块。该奖项反映了美国国家科学基金会的法定使命，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。

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.