分子设计的梯度引导蒙特卡罗方法
A gradient-directed Monte Carlo approach to molecular design
摘要：
最近开发的原子势线性组合（LCAP）方法[M.Wang等人，J. Am.美国化学学会，128，3228（2006）]允许在离散的化学空间中连续优化，因此在针对目标性质的分子设计中非常有用。为了解决LCAP方法中崎岖，连续的属性表面所带来的进一步挑战，我们开发了一种梯度导向蒙特卡罗（GDMC）策略作为对原始LCAP优化方法的增强。GDMC方法通过利用从LCAP方法计算的局部梯度信息在离散分子结构之间跳跃来保留探索分子空间的能力。它还允许随机蒙特卡罗移动，以克服属性表面上的局部最优之间的障碍。结合GDMC和LCAP方法在这里被证明优化非线性光学（NLO）性能在一类供体-受体取代的苯和卟啉框架。具体而言，一个分子与四个氮原子的卟啉环被发现有一个更大的第一超极化率比结构与传统的卟啉基序。1
英文摘要：
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 discrete chemical space and thus is quite 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 Monte Carlo moves to overcome barriers between local optima on property surfaces. The combined GDMC and LCAP approach is demonstrated here for optimizing nonlinear optical (NLO) 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. 1
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
