Hu Xiangqian, Beratan David N, Yang Weitao
Department of Chemistry, Duke University, Durham, North Carolina 27708-0354, USA.
J Chem Phys. 2008 Aug 14;129(6):064102. doi: 10.1063/1.2958255.
The recently developed linear combination of atomic potentials (LCAP) approach [M. Wang et al., J. Am. Chem. Soc. 128, 3228 (2006)] allows continuous optimization in a discrete chemical space, and thus is useful in the design of molecules for targeted properties. To address further challenges arising from the rugged, continuous property surfaces in the LCAP approach, we develop a gradient-directed Monte Carlo (GDMC) strategy as an augmentation to the original LCAP optimization method. The GDMC method retains the power of exploring molecular space by utilizing local gradient information computed from the LCAP approach to jump between discrete molecular structures. It also allows random MC moves to overcome barriers between local optima on property surfaces. The combined GDMC-LCAP approach is demonstrated here for optimizing nonlinear optical properties in a class of donor-acceptor substituted benzene and porphyrin frameworks. Specifically, one molecule with four nitrogen atoms in the porphyrin ring was found to have a larger first hyperpolarizability than structures with the conventional porphyrin motif.
最近开发的原子势线性组合（LCAP）方法[M. Wang等人，《美国化学会志》128, 3228 (2006)]允许在离散化学空间中进行连续优化，因此在设计具有目标性质的分子方面很有用。为应对LCAP方法中崎岖连续的性质表面带来的进一步挑战，我们开发了一种梯度导向蒙特卡罗（GDMC）策略，作为对原始LCAP优化方法的补充。GDMC方法通过利用从LCAP方法计算出的局部梯度信息在离散分子结构之间跳跃，保留了探索分子空间的能力。它还允许随机蒙特卡罗移动来克服性质表面上局部最优之间的障碍。这里展示了结合GDMC-LCAP的方法用于优化一类供体-受体取代苯和卟啉骨架中的非线性光学性质。具体而言，发现卟啉环中有四个氮原子的一个分子比具有传统卟啉基序的结构具有更大的第一超极化率。
