遗传算法在催化体系的全局结构优化中的应用

doi:10.11949/0438-1157.20201037

摘要/Abstract

摘要：

对催化体系进行全局结构优化，搜寻基态结构对预测催化剂结构、分析反应物的吸附特性、研究多相催化反应机理、构建实际反应路径等方面至关重要。遗传算法通过交叉、变异和选择等操作，模拟了自然淘汰进化过程，来搜索势能面上的基态结构。作为一种无偏优化算法，遗传算法的优化过程不依赖于输入结构，具有很强的全局搜索能力。对遗传算法在催化体系的全局结构优化问题中的应用进行了综述，介绍了遗传算法在实空间上进行全局结构优化的基本程序框架以及近年来结合并行计算、机器学习等技术发展的改进框架，并讨论了它们在团簇优化、负载型催化剂的结构优化问题上的相关应用，为遗传算法的进一步改进以及更广泛的应用提供理论指导。

关键词: 遗传算法, 全局优化, 催化, 势能面, 纳米结构, 分子模拟, 机器学习

Abstract:

Genetic algorithm is widely used to search for the global minimum structure that is important for analyzing the catalyst structure, the mechanism of heterogeneous catalytic reaction, and actual reaction pathway. By performing crossover, mutation and selection, genetic algorithm reflects the process of natural selection where the fittest individuals are selected for reproduction to explore the potential energy surface. As an unbiased optimization algorithm, the optimization process of genetic algorithm does not depend on the input structure and has strong global search capabilities. This review summarizes the recent progress of the design and application of genetic algorithm, as a global structure optimizer, in the catalytic system. Starting with introducing the standard genetic algorithm framework for global structure optimization, this review also includes the advanced framework developed by introducing parallel computing and machine learning technique. Finally, some examples about the reported application of genetic algorithm in catalytic structure optimization are presented, such as the optimization of metallic clusters, supported catalysts, etc. This review might provide a significant insight into the further improvement of genetic algorithm and the wider application in catalytic system.

Key words: genetic algorithm, global optimization, catalysis, potential energy surface, nanostructure, molecular simulation, machine learning

中图分类号:

O 643

石向成, 赵志坚, 巩金龙. 遗传算法在催化体系的全局结构优化中的应用[J]. 化工学报, 2021, 72(1): 27-41.

SHI Xiangcheng, ZHAO Zhijian, GONG Jinlong. Application of genetic algorithm in the global structure optimization of catalytic system[J]. CIESC Journal, 2021, 72(1): 27-41.

图/表 10

图1 包含和不包含对称性时随机产生的LJ38(a)和LJ100(b)团簇的能量分布（能量值是相对基态结构的相对值）[29]

Fig.1 Energetic distributions of randomly generated structures for LJ38 (a) and LJ100 (b) clusters with and without symmetry（energies are shown relative to the global minimum）[29]

图2 父代通过几何剪贴法交叉形成新子代的过程

Fig.2 The cut-and-splice pairing used on the crossover process for the generation of new structure

图3 基于池的遗传算法框架及其性能测试[21]

Fig.3 The framework of pool-GA and its performance test results

图4 结合主动学习进行加速的遗传算法框架及性能表现

Fig.4 Flowchart for the active learning-accelerated genetic algorithm method and its performance test results

图5 不同尺寸和电荷下的Au团簇的基态结构

Fig.5 The global minimum structure of Au cluster with different size

图6 不同尺寸硫醇配体保护的Au团簇及其成核、成长过程[4]

Fig.6 Schematic illustrations of the size-evolution of Aum(SR)n clusters

图7 遗传算法所得到的各类负载型催化剂的基态结构

Fig.7 The global minimum structure of supported catalyst generated by genetic algorithm

图8 Pt7团簇在α-Al2O3表面上的演化过程[9]

Fig.8 The evolutionary process of Pt7 cluster on α-Al2O3 surface[9]

图9 α-Al2O3(0001)吸附的Pt8H4团簇(a)，γ-Al2O3(100)吸附的Pt8H5团簇(b)，α-Al2O3(0001)吸附的Pt8H24团簇(c)和γ-Al2O3(100)吸附的Pt8H24团簇(d)的基态结构[91]

Fig.9 Global minimum structure of Pt8H4 on α-Al2O3(0001) (a), Pt8H5 on γ-Al2O3(100) (b), Pt8H24 on α-Al2O3(0001) (c), and Pt8H24 on γ-Al2O3(100) (d)[91]

图10 O2在MgO(100)负载的Au12团簇的吸附过程[92]

Fig.10 The adsorption process of O2 on the MgO(100)-supported Au12 clusters[92]

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