Global Optimization in Computational Chemistry Using Interval Arithmetic

使用区间算术的计算化学全局优化

• 批准号: 9161367
• 负责人: Andrew Sherman
• 金额: $ 4.98万
• 依托单位: Scientific Computing Associates Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-01-01 至 1992-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9161367&HistoricalAwards=false
• 关键词: Global; Optimization; Computational; Chemistry; Using

This Small Business Innovation Research (SBIR) Phase 1 project is in the general area of Computational Chemistry. Global optimization plays a key role in molecular computations in computational chemistry. Due to the large size of even modest molecules, these methods must contend with countless local minima. Clever algorithms have been developed that sample parameter space to reduce trapping in local minima, but these fail by providing statistical rather than guaranteed solutions. There is a class of algorithms that for certain functions guarantee that a global minimum has been found. These methods achieve this remarkable result using interval arithmetic. Interval global optimization has been known for some time, but has never been applied to systems as large as those encountered in computational chemistry. This project will identify how to modify the functions encountered in computational chemistry such that interval global analysis applies. It will also explore the parallel programming techniques necessary to scale the calculations up to the huge sizes demanded by molecular mechanics calculations.

这个小型企业创新研究(SBIR)第一阶段项目属于计算化学的一般领域。全局优化在计算化学中的分子计算中起着关键作用。由于分子的大小，即使是中等大小的分子，这些方法也必须与无数的局部极小作斗争。为了减少陷入局部极小值的陷阱，已经开发了一些聪明的算法，即采样参数空间，但由于提供了统计而不是有保证的解决方案，这些算法失败了。对于某些函数，有一类算法可以保证找到全局最小值。这些方法采用区间算法，取得了显著的效果。区间全局优化已经被知道了一段时间，但从来没有被应用于像计算化学中遇到的那样大的系统。这个项目将确定如何修改计算化学中遇到的函数，以便应用区间全局分析。它还将探索必要的并行编程技术，以将计算扩大到分子力学计算所需的巨大规模。