Selective Control of Rugged Energy Landscapes: Structural and Dynamical Studies of Encapsulated Clusters

布朗大学的Jimmy Doll和罗德岛大学的David Freeman得到了理论和计算化学计划的支持，他们的研究涉及到基于簇的前体的结构和动力学的预测、表征和控制的理论问题。这一努力试图通过重塑能源格局，从而对选定的物理系统进行控制，从而颠倒最小化问题的逻辑。特别是，本研究旨在学习稳定和/或化学捕获不稳定或亚稳的簇构象。该项目的目标是：(1)确定实现所选簇结构的热力学和动力学控制的可能方法，(2)建立提供相关簇结构和动力学最敏感特征的可观测值，以及(3)协助解释与这些可观测值相关的实验数据。为了促进实验和理论观察之间的相互作用，计划与阿贡国家实验室的实验人员合作。计算机模拟已被证明是帮助预测新材料性能的有用工具。随着使用纳米级团簇作为基本构建块的新合成方法的发展，团簇的计算研究预计将变得越来越重要。本研究的结果有望提供必要的经验和背景，以帮助确定控制由簇组成的材料组成部分的结构和性能的方法，并可能应用于化学，生物学和材料科学。

Jimmy Doll of Brown University and David Freeman of the University of Rhode Island are supported by the Theoretical and Computational Chemistry Program for research that addresses theoretical issues concerning the prediction, characterization, and control of the structure and dynamics of cluster-based precursors. This effort seeks to invert the logic of the minimization problem, by undertaking to reshape energy landscapes and thereby to exercise control over selected physical systems. In particular, this research aims to learn to stabilize and/or chemically trap cluster conformers that are otherwise either unstable or metastable. The project goals are to (1) identify possible approaches for achieving thermodynamic and kinetic control of selected cluster structures, (2) establish those observables that provide the most sensitive signatures of relevant cluster structure and dynamics, and (3) assist in the interpretation of experimental data relevant to those observables. To promote the interplay between experimental and theoretical observations, collaboration with experimentalists at Argonne National Laboratory is planned. Computer simulations have proved to be useful tools that aid in predicting the properties of new materials. With the development of new synthetic methods that use nanoscale clusters as fundamental building blocks, computational studies of clusters are expected to grow in importance. The outcomes of this research are expected to provide the necessary experience and background to help determine methods for controlling structures and properties of the component parts for materials composed of clusters, with likely applications in chemistry, biology, and materials science.