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

在化学系化学理论、模型和计算方法（CTMC）项目的支持下，特拉华州大学的Krzysztof Szalewicz教授将对分子簇、分子凝聚相和生物分子系统进行量子力学研究。这类系统的性质受分子间（货车德瓦尔斯）力的支配：根据两个分子之间的距离，它们会相互吸引或排斥，理查德·费曼认为这是人类最大的发现。Szalewicz及其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效率最高的方法之一，而且还为研究人员提供了一种独特的能力，可以根据四种基本物理机制来解释依赖于分子间力的性质：静电，交换排斥，极化和分散相互作用。由于直接量子力学计算仅限于具有100个左右原子的分子组装，因此Szalewicz的小组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它能够从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。一个例子是对晶体结构的可靠预测。晶体的计算设计对于制药、农药、半导体和含能材料工业具有重要意义。Szalewicz小组的这项研究预计将对从材料，生物分子和大气科学到计量学，分子光谱学和散射以及天体物理学等领域产生广泛的科学影响。 这项研究的更广泛的影响将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，组织会议和研讨会以传播知识，Szalewicz小组将在该奖项下开发的方法利用了自适应微扰理论（SAPT），基于密度泛函理论（DFT）描述的单体，一种表示为SAPT（DFT）的方法。从SAPT（DFT）计算中产生力场的机器学习方法将被扩展，以处理具有软内部自由度的分子。这些力场将用于当前实验，观测或技术兴趣的几个系统，特别是用于从第一原理预测晶体结构，包括与单体的不同构象有关的多态性的困难情况。理论的其他发展将包括改进DFT方法的工作，这些方法可以与精确的色散能配对，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。在更好地理解分子间相互作用、显著推进电子结构方法和力场开发技术以及晶体结构预测方面，有可能产生更广泛的科学影响。一般来说，这些研究将有助于更好地理解分子材料的物理性质。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。

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.