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Evolutionary niching in the GAtor genetic algorithm for molecular crystal structure prediction
Faraday Discussions ( IF 3.3 ) Pub Date : 2018-04-03 , DOI: 10.1039/c8fd00067k
Farren Curtis 1, 2, 3, 4, 5 , Timothy Rose 1, 2, 3, 4 , Noa Marom 1, 2, 3, 4, 5
Affiliation  

The goal of molecular crystal structure prediction (CSP) is to find all the plausible polymorphs for a given molecule. This requires performing global optimization over a high-dimensional search space. Genetic algorithms (GAs) perform global optimization by starting from an initial population of structures and generating new candidate structures by breeding the fittest structures in the population. Typically, the fitness function is based on relative lattice energies, such that structures with lower energies have a higher probability of being selected for mating. GAs may be adapted to perform multi-modal optimization by using evolutionary niching methods that support the formation of several stable subpopulations and suppress the over-sampling of densely populated regions. Evolutionary niching is implemented in the GAtor molecular crystal structure prediction code by using techniques from machine learning to dynamically cluster the population into niches of structural similarity. A cluster-based fitness function is constructed such that structures in less populated clusters have a higher probability of being selected for breeding. Here, the effects of evolutionary niching are investigated for the crystal structure prediction of 1,3-dibromo-2-chloro-5-fluorobenzene. Using the cluster-based fitness function increases the success rate of generating the experimental structure and additional low-energy structures with similar packing motifs.

中文翻译:

GAtor遗传算法中的进化小生境用于分子晶体结构预测

分子晶体结构预测(CSP)的目标是找到给定分子的所有可能的多晶型物。这需要在高维搜索空间上执行全局优化。遗传算法(GA)通过从初始结构种群开始,然后通过繁殖种群中最适合的结构来生成新的候选结构,来进行全局优化。通常,适应度函数是基于相对晶格能量的,因此具有较低能量的结构具有较高的被选为配对的可能性。GA可通过使用进化小生境方法来执行多模式优化,这些方法支持几种稳定亚群的形成并抑制人口稠密区域的过采样。通过使用机器学习中的技术将种群动态聚类为结构相似的小生境,在GAtor分子晶体结构预测代码中实现了进化小生境。构建基于聚类的适应度函数,以使人口较少的聚类中的结构具有较高的被选中进行繁殖的可能性。在这里,研究了进化小生境对1,3-二溴-2-氯-5-氟苯的晶体结构预测的影响。使用基于聚类的适应度函数可以提高生成实验结构以及具有类似堆积图案的其他低能结构的成功率。构建基于聚类的适应度函数,以使人口较少的聚类中的结构具有较高的被选中进行繁殖的可能性。在这里,研究了进化小生境对1,3-二溴-2-氯-5-氟苯的晶体结构预测的影响。使用基于聚类的适应度函数可以提高生成实验结构以及具有类似堆积图案的其他低能结构的成功率。构建基于聚类的适应度函数,以使人口较少的聚类中的结构具有较高的被选中进行繁殖的可能性。在这里,研究了进化小生境对1,3-二溴-2-氯-5-氟苯的晶体结构预测的影响。使用基于聚类的适应度函数可以提高生成实验结构以及具有类似堆积图案的其他低能结构的成功率。
更新日期:2018-10-26
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