Algorithms for Global Optimization, Enhanced Sampling and Reaction Pathway Determination

全局优化、增强采样和反应途径确定的算法

• 批准号: 9632236
• 负责人: John Straub
• 金额: $ 27.2万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-07-15 至 1999-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9632236&HistoricalAwards=false
• 关键词: Algorithms; Global; Optimization; Enhanced; Sampling

John Straub is supported by a grant from the Theoretical and Computational Chemistry Program to continue his work on developing new algorithms for global optimization, enhanced sampling and the determination of reaction pathways. Two areas of algorithmic development will be explored. The first area involves the use of generalized statistics, as opposed to Gibbs-Boltzmann statistics, which can be used to describe highly delocalized distributions of configurations for systems that are strongly localized in the standard statistics. This technique leads to a higher likelihood of finding low energy configurations. The second area of research involves an optimization technique known as quantum mechanical annealing, in which the value of Planck's constant is greatly exaggerated causing the system to `spread out` in space and more effectively seek the lowest energy configurations by tunneling through energy barriers that would otherwise be hard to climb over. This technique employs imaginary-time path integral quantum simulations in a novel way. Applications include: 1) model protein folding; 2) optimal reaction path determination; and 3) enhanced sampling of molecular configurations for the computation of average thermodynamic properties. Computer simulations have provided a number of molecular level insights into chemical processes which occur in solution and on surfaces. The technique is very powerful, but is limited by statistical sampling problems associated with the speed of current supercomputers and the time available to perform such simulations. Straub is developing a new algorithm for performing molecular simulations which should be a major improvement in statistical sampling. If successful, this new algorithm could provide a significant decrease in the amount of computational resources required to obtain meaningful sampling statistics in molecular simulations.

约翰·斯特劳布得到了理论和 计算化学计划，继续他的工作，开发新的 全局优化算法，增强采样和 反应途径的确定。 算法开发的两个领域 将被探索。 第一个领域涉及使用广义 统计，而不是吉布斯-玻尔兹曼统计，它可以使用 来描述高度离域的组态分布， 这些系统在标准统计中具有很强的局部性。 这 技术导致找到低能量的可能性更高 配置. 第二个研究领域涉及优化 技术称为量子力学退火，其中的值 普朗克常数被大大夸大，导致系统“扩散 在太空中，更有效地寻求最低的能量配置 通过穿透能量屏障，否则很难 爬过去。 该技术采用时间路径积分量子 以一种新颖的方式进行模拟。 应用包括：1）模型蛋白 折叠; 2）最佳反应路径确定; 3）增强采样 计算分子构型的平均热力学 特性. 计算机模拟提供了许多分子水平的见解 转化为发生在溶液和表面的化学过程。 的 技术非常强大，但受到统计抽样的限制 与当前超级计算机的速度和时间相关的问题 可以进行这样的模拟。 斯特劳布正在开发一种新的 进行分子模拟的算法，这应该是一个主要的 改进统计抽样。 如果成功，这个新算法 可以显著减少计算量， 获得有意义的分子抽样统计所需的资源 模拟