基于配位数约束的年龄适应度多目标遗传算法预测晶体结构
Crystal structure prediction using age-fitness multi-objective genetic algorithm and coordination number constraints
摘要：
晶体结构预测（CSP）已成为发现新材料的重要途径之一。基于进化算法和粒子群优化算法的CSP算法已经发现了大量的新材料。然而，这些算法的基础上从头计算的自由能是低效的。此外，它们在可扩展性方面有严重的限制。最近，我们提出了一种很有前途的晶体结构预测方法的基础上原子接触地图，使用全局优化算法搜索的Wyckoff位置，通过最大限度地提高预测结构的接触地图和接触地图的真实晶体结构之间的匹配。然而，我们以前的基于接触图的CSP算法存在两个主要的局限性：（1）由于陷入局部最优而失去搜索能力;（2）它只利用晶胞内原子的连接来预测晶体结构，忽略了晶胞外的化学环境，这可能导致不合理的配位环境。本文提出了一种新的基于接触图的晶体结构预测的多目标遗传算法，该算法通过优化接触图匹配精度、个体年龄和配位数匹配三个目标来实现。此外，我们分配的年龄值的GA的所有个人，并试图尽量减少年龄，以避免早熟收敛问题。我们的实验结果表明，与我们以前的CMCrystal算法相比，我们的多目标晶体结构预测算法（CMCrystalMOO）可以重建更高质量的晶体结构，并减轻过早收敛的问题。
英文摘要：
Crystal structure prediction (CSP) has emerged as one of the most important approaches for discovering new materials. CSP algorithms based on evolutionary algorithms and particle swarm optimization have discovered a great number of new materials. However, these algorithms based on ab initio calculation of free energy are inefficient. Moreover, they have severe limitations in terms of scalability. We recently proposed a promising crystal structure prediction method based on atomic contact maps, using global optimization algorithms to search for the Wyckoff positions by maximizing the match between the contact map of the predicted structure and the contact map of the true crystal structure. However, our previous contact map based CSP algorithms have two major limitations: (1) the loss of search capability due to getting trapped in local optima; (2) it only uses the connection of atoms in the unit cell to predict the crystal structure, ignoring the chemical environment outside the unit cell, which may lead to unreasonable coordination environments. Herein we propose a novel multi-objective genetic algorithms for contact map-based crystal structure prediction by optimizing three objectives, including contact map match accuracy, the individual age, and the coordination number match. Furthermore, we assign the age values to all the individuals of the GA and try to minimize the age aiming to avoid the premature convergence problem. Our experimental results show that compared to our previous CMCrystal algorithm, our multi-objective crystal structure prediction algorithm (CMCrystalMOO) can reconstruct the crystal structure with higher quality and alleviate the problem of premature convergence.
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