Global Optimization in Computational Chemistry Using Interval Arithmetic

小企业创新研究（SBIR）第一阶段项目 在计算化学的一般领域。 全球 优化在分子计算中起着关键作用， 计算化学 由于规模庞大，即使是温和的 分子，这些方法必须与无数的局部极小值相抗衡。 已经开发出了对参数空间进行采样的聪明算法 以减少在局部极小值中的捕获，但这些失败是因为提供了 统计而不是保证的解决方案。 有一类算法对于某些函数 保证找到全局最小值。这些方法 使用区间算术实现这个显著的结果。 区间全局优化已经有一段时间了，但是 从来没有被应用到系统，因为那些遇到的， 计算化学 这个项目将确定如何修改遇到的函数 在计算化学中， 适用. 它还将探讨并行编程技术 将计算规模扩大到所需的巨大规模是必要的 通过分子力学计算。

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.