The recently presented software zeoGAsolver is discussed, which is based on genetic algorithms, with domain-dependent crossover and selection operators that maintain the size of the population in successive iterations while improving the average fitness. Using the density, cell parameters, and symmetry (or candidate symmetries) of a zeolite sample whose resolution can not be achieved by analysis of the XRD (X-ray diffraction) data, the software attempts to locate the coordinates of the T-atoms of the zeolite unit cell employing a function of ‘fitness’ (F), which is defined through the different contributions to the ‘penalties’ (P) as F = 1/(1 + P). While testing the software to find known zeolites such as LTA (zeolite A), AEI (SSZ-39), ITW (ITQ-12) and others, the algorithm has found not only most of the target zeolites but also seven new hypothetical zeolites whose feasibility is confirmed by energetic and structural criteria.

中文翻译：

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使用遗传算法和几何优化确定沸石结构

讨论了最近发布的软件zeoGAsolver，该软件基于遗传算法，具有与域相关的交叉和选择运算符，可在连续迭代中维持种群大小，同时提高平均适应性。使用无法通过XRD（X射线衍射）数据分析获得分辨率的沸石样品的密度，晶胞参数和对称性（或候选对称性），该软件尝试查找T原子的坐标。使用“适应度”（F）函数的沸石晶胞，它是通过对“罚分”（P）的不同贡献来定义的，即F = 1 /（1 + P）。在测试软件以查找已知的沸石（例如LTA（沸石A），AEI（SSZ-39），ITW（ITQ-12）等）时，该算法不仅找到了大多数目标沸石，还发现了七种新的假想沸石，可行性由能量和结构标准证实。