Algorithms for enhanced sampling and global optimization

增强采样和全局优化的算法

• 批准号: 9975494
• 负责人: John Straub
• 金额: $ 37.19万
• 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: 1999
• 资助国家: 美国
• 起止时间: 1999-09-01 至 2003-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9975494&HistoricalAwards=false
• 关键词: Algorithms; enhanced; sampling; global; optimization

John Straub of Boston University is supported by a grant from the Theoretical and Computational Chemistry Program to continue his research in algorithms for enhanced sampling and global optimization. Such algorithms have extensive applications in simulations involving biomolecules, low temperature clusters, and supercooled liquids. The goal of this research is to develop search algorithms that can sample complex multidimensional potential energy surfaces efficiently. Ideally such a technique would 1) isolate the thermodynamically important low lying energy basins, and 2) effectively sample those basins and move between them in a way that produces exact thermodynamic averages. Two new algorithms are proposed. The first involves a novel Monte Carlo method that allows long range moves between neighborhoods of low energy minima in a way that satisfies detailed balance. The second algorithm involves the continued development of quantum mechanical annealing methods for global optimization. This involves a reformulation of the Feynman path integral representation that uses an effective potential to allow enhanced sampling beyond that of the standard quantum delocalization. A number of applications are proposed including: 1) the simulation of solvated peptides; 2) the computation of thermodynamic averages for low temperature molecular clusters; and 3) the evaluation of the mechanism for ligand rebinding in myoglobin.The simulation of condensed matter systems has been moved forward by dramatic increases in the availability and speed of computers and the development of effective algorithms for the simulation of dynamical and thermodynamic phenomena. Of particular fundamental and practical interest are the properties of biomolecular, supercooled and glass forming systems. For such systems, the task of isolating conformations of lowest free energy or computing thermodynamic averages is frustrated by the rugged energy landscape. The new techniques being developed by Straub should find widespread applications in the simulation of biological systems.

波士顿大学的John Straub得到了理论和计算化学项目的资助，继续他在增强采样和全局优化算法方面的研究。 这种算法在涉及生物分子、低温团簇和过冷液体的模拟中具有广泛的应用。 本研究的目标是开发搜索算法，可以有效地采样复杂的多维势能面。 理想情况下，这种技术将1）隔离重要的低洼能源盆地，2）有效地对这些盆地进行采样，并以产生精确热力学平均值的方式在它们之间移动。 提出了两种新的算法。 第一个涉及到一种新的蒙特卡罗方法，它允许在满足详细平衡的方式下在低能量最小值的邻域之间进行长距离移动。 第二种算法涉及量子力学退火方法的持续发展，用于全局优化。 这涉及到重新制定的费曼路径积分表示，使用一个有效的潜力，允许增强采样超过标准的量子离域。 提出了一些应用，包括：1）溶剂化肽的模拟，2）低温分子团簇的热力学平均值的计算;和3）对肌红蛋白中配体再结合机制的评价。由于计算机的可用性和速度的急剧增加以及有效的算法的发展，凝聚态系统的模拟已经向前推进。动力学和热力学现象。 生物分子、过冷和玻璃形成系统的性质是特别基本和实际的兴趣。 对于这样的系统，分离最低自由能的构象或计算热力学平均值的任务被崎岖的能量景观所挫败。 斯特劳布正在开发的新技术应该在生物系统的模拟中得到广泛的应用。