Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, USA.
J Chem Phys. 2011 Jul 28;135(4):044115. doi: 10.1063/1.3610907.
We describe a new method for constructing empirical valence bond potential energy surfaces using a parallel multilevel genetic program (PMLGP). Genetic programs can be used to perform an efficient search through function space and parameter space to find the best functions and sets of parameters that fit energies obtained by ab initio electronic structure calculations. Building on the traditional genetic program approach, the PMLGP utilizes a hierarchy of genetic programming on two different levels. The lower level genetic programs are used to optimize coevolving populations in parallel while the higher level genetic program (HLGP) is used to optimize the genetic operator probabilities of the lower level genetic programs. The HLGP allows the algorithm to dynamically learn the mutation or combination of mutations that most effectively increase the fitness of the populations, causing a significant increase in the algorithm's accuracy and efficiency. The algorithm's accuracy and efficiency is tested against a standard parallel genetic program with a variety of one-dimensional test cases. Subsequently, the PMLGP is utilized to obtain an accurate empirical valence bond model for proton transfer in 3-hydroxy-gamma-pyrone in gas phase and protic solvent.
我们描述了一种新的方法，使用并行多层次遗传程序（PMLGP）构建经验价键势能面。遗传程序可用于在函数空间和参数空间中进行有效的搜索，以找到最佳的函数和参数集，从而拟合从头算电子结构计算得到的能量。在传统遗传程序方法的基础上，PMLGP 在两个不同层次上利用分层遗传程序。较低层次的遗传程序用于并行优化共进化种群，而较高层次的遗传程序（HLGP）用于优化较低层次遗传程序的遗传操作概率。HLGP 允许算法动态学习最有效地提高种群适应性的突变或突变组合，从而显著提高算法的准确性和效率。该算法的准确性和效率通过与各种一维测试用例的标准并行遗传程序进行比较进行了测试。随后，利用 PMLGP 获得了气相和质子溶剂中 3-羟基-γ-吡喃酮中质子转移的精确经验价键模型。
