Predictions of Properties of Matter using Physics-Based Force Fields Derived from First Principles

使用源自第一原理的基于物理的力场预测物质的性质

• 批准号: 2313826
• 负责人: Krzysztof Szalewicz
• 金额: $ 52.99万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2313826&HistoricalAwards=false
• 关键词: Predictions; Properties; Matter; using; Physics; Predictions; Properties; Matter; using; Physics

With support from the Chemical Theory, Models and Computational Methods (CTMC) program in the Division of Chemistry, Professor Krzysztof Szalewicz of University of Delaware will perform quantum-mechanical investigations of clusters of molecules, molecular condensed phases, and biomolecular systems. The properties of such systems are governed by intermolecular (van der Waals) forces: depending on the distance between two molecules, they will either attract or repel each other, a physical law that Richard Feynman considered to be the biggest finding of humanity. Szalewicz and coworkers have developed methods for computing intermolecular forces that are not only among the most accurate and computationally efficient ones, but also provide researchers with a unique ability to interpret properties dependent on intermolecular forces in terms of the four fundamental physical mechanisms: the electrostatic, exchange-repulsion, polarization, and dispersion interactions. Since direct quantum-mechanical calculations are limited to molecular assemblies with a hundred or so atoms, Szalewicz’s group will develop machine learning methods of extrapolating quantum results to condensed phases. The importance of this work stems from its ability to predict properties of matter from first principles, i.e., deriving them from equations of quantum mechanics, for arbitrary molecular materials. One example would be the reliable predictions of crystal structures. Computational design of crystals is of significant importance for pharmaceutical, agrochemical, semiconductor, and energetic materials industries. This research in the Szalewicz group is expected to have broad scientific impact on fields ranging from materials, biomolecular, and atmospheric science to metrology, molecular spectroscopy and scattering, and astrophysics. Broader impacts of this research will include training of graduate students and postdoctoral associates with diverse backgrounds, extensive collaborations with other research groups, organization of conferences and workshops to disseminate knowledge, and in making developed software available for use by other researchers.The methods that the Szalewicz group will develop under this award utilize symmetry-adapted perturbation theory (SAPT) based on monomers described by density-functional theory (DFT), an approach denoted as SAPT(DFT). Machine-learning methods for the generation of force fields derived from SAPT(DFT) calculations will be extended to enable treatment of molecules with soft internal degrees of freedom. These force fields will be used for several systems of current experimental, observational, or technological interest, in particular for predictions of crystal structures from first principles, including difficult cases with polymorphism related to varying conformations of monomers. Other developments of theory will include work on improved DFT methods that can be paired with accurate dispersion energies and extensions of machine-learning force-field generation methods to three-body nonadditive interactions. There is potential for scientific broader impact in better understanding intermolecular interactions, in significantly advancing electronic structure methods and force-field development techniques, and in crystal structure predictions. It is expected that, in general, these studies will contribute to a better physical understanding of the properties of molecular materials.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.

在化学理论的支持下，在化学划分中，模型和计算方法（CTMC）计划，特拉华大学的Krzysztof Szalewicz教授将对分子，分子凝结相和生物分子系统的分子簇，分子凝结相和生物分子系统进行量子力学研究。此类系统的性质受分子间（范德华）力控制：根据两个分子之间的距离，它们将互相吸引或驱逐，这是理查德·费曼（Richard Feynman）认为是人类最大发现的物理定律。 Szalewicz及其同事开发了计算分子间力的方法，这些力不仅是最准确和计算上有效的力之一，而且还为研究人员提供了独特的能力，可以根据四种基本物理机制来解释依赖分子间力的特性：静电，交换，交换，换层，偏振，偏振和分散互动。由于直接的量子力学计算仅限于具有一百个原子的分子组件，因此Szalewicz的组将开发机器学习方法，将量子结果推出到凝结相。该工作工厂的重要性来自其从第一原理中预测物质的性质的能力，即，它们从量子力学方程中得出任意分子材料。一个例子是晶体结构的可靠预测。晶体的计算设计对于药物，农业化学，半导体和能量材料行业至关重要。预计Szalewicz组的这项研究对从材料，生物分子和大气科学到计量学，分子光谱和散射以及天体物理学等领域具有广泛的科学影响。 Broader impacts of this research will include training of graduate students and postdoctoral associations With divers backgrounds, extensive collaborations with other research groups, organization of conferences and workshops to disseminate knowledge, and in making developed software available for use by other researchers.The methods that the Szalewicz group will develop under this award utilize symmetry-adapted perturbation theory (SAPT) based on monomers described by density-functional theory （DFT），一种表示为SAPT（DFT）的方法。将扩展从SAPT（DFT）计算得出的力场生成的机器学习方法，以实现具有软内部自由度的分子处理。这些力场将用于当前的实验，观察性或技术兴趣的几种系统，尤其是针对第一原理的晶体结构的预测，包括与单体不同考虑因素有关的困难病例。理论的其他发展将包括改进的DFT方法的工作，这些方法可以与精确的分散能和机器学习力量生成方法扩展到三体无染色相互作用。在更好地理解分子间相互作用，显着推进电子结构方法和力场发展技术以及晶体结构预测中，有可能对科学广泛的影响进行更广泛的影响。总的来说，总的来说，这些研究将有助于更好地理解分子材料的特性。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响来审查标准，被认为是珍贵的支持。