Exploring chemical space with discrete, gradient, and hybrid optimization methods
Exploring chemical space with discrete, gradient, and hybrid optimization methods
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DOI：
10.1063/1.2987711
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发表时间：
2008-11-07
影响因子：
4.4
通讯作者：
Beratan, David N.
中科院分区：
文献类型：
--
作者：
Balamurugan, D.;Yang, Weitao;Beratan, David N.
离散、梯度和混合优化方法被应用于发现具有优化性质的分子的挑战。利用紧结合模型研究了这些方法的成本和性能，以最大化分子的静态第一电子超极化率。我们的分析表明，离散分支和定界方法为涉及不同化学结构的逆向化学设计提供了稳健的策略。基于原子势线性组合的混合离散梯度优化策略显著提高了梯度方法的性能。混合算法优于死角消除算法，可与分支定界算法和遗传算法相竞争。对于中等大小的分子优化，这些模型哈密顿量的分支定界方法比遗传算法更具成本效益。
Discrete, gradient, and hybrid optimization methods are applied to the challenge of discovering molecules with optimized properties. The cost and performance of the approaches were studied using a tight-binding model to maximize the static first electronic hyperpolarizability of molecules. Our analysis shows that discrete branch and bound methods provide robust strategies for inverse chemical design involving diverse chemical structures. Based on the linear combination of atomic potentials, a hybrid discrete-gradient optimization strategy significantly improves the performance of the gradient methods. The hybrid method performs better than dead-end elimination and competes with branch and bound and genetic algorithms. The branch and bound methods for these model Hamiltonians are more cost effective than genetic algorithms for moderate-sized molecular optimization.