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

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.

在化学系化学理论、模型和计算方法（CTMC）项目的支持下，特拉华大学的 Krzysztof Szalewicz 教授将对分子簇、分子凝聚相和生物分子系统的性质进行量子力学研究。这种系统受分子间（范德华）力控制：根据两个分子之间的距离，它们会相互吸引或排斥，这是理查德所提出的物理定律费曼被认为是人类最伟大的发现，萨勒维奇和他的同事开发了计算分子间力的方法，这些方法不仅是最准确和计算性最强的方法，而且还为高效的研究人员提供了解释依赖于分子间力的性质的独特能力。四种基本物理机制：静电、交换排斥、极化和色散相互作用，由于直接量子力学计算仅限于具有一百个左右原子的分子组装体。 Szalewicz 的团队将开发将量子结果外推到凝聚相的机器学习方法，这项工作的重要性源于其能够根据第一原理预测物质的性质，即从量子力学方程中推导出任意分子材料的性质。 Szalewicz 小组的这项研究预计将具有广泛的科学意义。这项研究对材料、生物分子、大气科学、计量学、分子光谱学和散射以及天体物理学等领域的影响更广泛，包括培训具有不同背景的研究生和博士后研究员、与其他研究小组的广泛合作、组织研究。会议和研讨会来传播知识，并使开发的软件可供其他研究人员使用。Szalewicz 小组将在该奖项下开发的方法利用对称适应的微扰理论(SAPT) 基于密度泛函理论 (DFT) 描述的单体，一种称为 SAPT(DFT) 的方法，用于生成源自 SAPT(DFT) 计算的力场，该方法将扩展到能够处理分子。这些力场将用于当前实验、观察或技术兴趣的多个系统，特别是根据第一原理预测晶体结构，包括与其他单体不同构象相关的困难情况。事态发展理论研究将包括改进 DFT 方法的工作，这些方法可以与精确的色散能量相结合，以及将机器学习力场生成方法扩展到三体非加性相互作用，这对于更好地理解分子间相互作用有可能产生更广泛的科学影响。推进电子结构方法和力场开发技术，以及晶体结构预测，总的来说，这些研究将有助于更好地物理理解分子材料的特性。该奖项被视为 NSF 的法定使命。值得支持通过使用基金会的智力价值和更广泛的影响审查标准进行评估。