Bootstrap Embedding for Molecules, Materials and Electrocatalysis

用于分子、材料和电催化的自举嵌入

• 批准号: 2154938
• 负责人: Troy Van Voorhis
• 金额: $ 51万
• 依托单位: Massachusetts Institute of Technology
• 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: 2022-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2154938&HistoricalAwards=false
• 关键词: Bootstrap; Embedding; Molecules; Materials; Electrocatalysis

Professor Troy Van Voorhis of Massachusetts Institute of Technology is supported by an award from the Chemical Theory, Models and Computational Methods (CTMC) program in the Division of Chemistry to study computational methods for combining calculations on small molecular fragments to make predictions about complex materials. The primary tool is a concept known as Bootstrap embedding, which allows researchers for the first time to divide a large system (say with thousands of atoms) into a number of overlapping fragments (each containing, say, ten atoms) and then stitch together those calculations to understand properties of the whole system. The resulting tools will apply to a wide range of molecules and materials and Van Voorhis will specifically use these tools to study fuel cell catalysts. Computational tools have revolutionized chemistry in recent decades and the work in this proposal will train part of the next generation of computer-savvy chemists. As part of the proposal, Van Voorhis will engage in activities aimed at improving women and under-represented minorities pursuing careers in science. In this project, we will develop new quantum mechanical fragment embedding tools that are specifically designed to be able to accurately treat both complex molecules and solids. The starting point for the work is Bootstrap Embedding (BE), a method that fundamentally solves the issue of embedding overlapping fragments. In this project, we will extend BE by while improving its accuracy and efficiency in treating large basis sets and intermolecular interactions. In a parallel effort, we will implement BE for extended systems (e.g. surfaces and solids) by incorporating periodic boundary conditions. The bootstrap conditions also naturally lead to a new form of boundary conditions (which we call “impurity boundary conditions”) that approximate the situation in which a single substrate reacts at a semi-infinite surface. As a signature application of these tools, we will study the energetics of O2 binding and subsequent proton coupled electron transfer (PCET) steps in a set of potential ORR catalysts. As the ORR half-reaction is the rate- and potential- determining step in all modern fuel cells, advances in this area have the potential for significant impacts in future fuel cell technologies. In a direct sense, the work outlined here will contribute to the training of the undergraduate, graduate and postdoctoral scholars involved in the project. In a wider sense, the project proposes a set of education, training and outreach activities aimed at increasing the participation of women and underrepresented minorities in STEM fields.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.

麻省理工学院的 Troy Van Voorhis 教授获得化学系化学理论、模型和计算方法 (CTMC) 项目的资助，研究结合小分子片段计算来预测复杂材料的计算方法。主要工具是一个称为 Bootstrap 嵌入的概念，它允许研究人员首次将大型系统（例如具有数千个原子）划分为许多重叠的片段（例如每个片段包含十个原子），然后缝合将这些计算结合起来以了解整个系统的特性，所得工具将适用于各种分子和材料，Van Voorhis 将专门使用这些工具来研究燃料电池催化剂，近几十年来，计算工具已经彻底改变了化学和相关工作。该提案将培训部分下一代精通计算机的化学家作为该提案的一部分，Van Voorhis 将从事旨在改善女性和代表性不足的少数族裔追求科学的活动。在该项目中，我们将开发新的量子。机械片段嵌入专门设计用于能够准确处理复杂分子和固体的工具。这项工作的起点是 Bootstrap Embedding (BE)，这是一种从根本上解决嵌入重叠片段问题的方法。在这个项目中，我们将扩展 BE。通过同时提高其处理大基组和分子间相互作用的准确性和效率，我们将通过合并周期性边界条件来实现扩展系统（例如表面和固体）的BE。新形式的边界条件（我们称之为“杂质边界条件”）近似于单个基质在半无限表面上发生反应的情况作为这些工具的标志性应用，我们将研究 O2 结合的能量学和后续研究。一组潜在的 ORR 催化剂中的质子耦合电子转移 (PCET) 步骤 由于 ORR 半反应是所有现代燃料电池中的速率和潜力决定步骤，因此该领域的进步有可能对未来燃料产生重大影响。直接细胞技术。从某种意义上说，这里概述的工作将有助于培训参与该项目的本科生、研究生和博士后学者。从更广泛的意义上说，该项目提出了一系列教育、培训和推广活动，旨在增加妇女和代表性不足的人的参与。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。