While crystal structure prediction (CSP) remains a longstanding challenge, we introduce ParetoCSP, a novel algorithm for CSP, which combines a multi-objective genetic algorithm (MOGA) with a neural network inter-atomic potential (IAP) model to find energetically optimal crystal structures given chemical compositions. We enhance the NSGA-III algorithm by incorporating the genotypic age as an independent optimization criterion and employ the M3GNet universal IAP to guide the GA search. Compared to GN-OA, a state-of-the-art neural potential based CSP algorithm, ParetoCSP demonstrated significantly better predictive capabilities, outperforming by a factor of $2.562$ across $55$ diverse benchmark structures, as evaluated by seven performance metrics. Trajectory analysis of the traversed structures of all algorithms shows that ParetoCSP generated more valid structures than other algorithms, which helped guide the GA to search more effectively for the optimal structures

翻译：尽管晶体结构预测（CSP）仍是一项长期挑战，但我们提出ParetoCSP这一新型算法，它结合多目标遗传算法（MOGA）与神经网络原子间势能（IAP）模型，在给定化学组分条件下寻找能量最优的晶体结构。我们通过将基因型年龄作为独立优化准则引入NSGA-III算法，并采用M3GNet通用原子间势能指导遗传算法（GA）搜索。相较于基于最新神经网络势能的CSP算法GN-OA，ParetoCSP在55种多样性基准结构上展现出显著更优的预测能力，性能提升达2.562倍（基于七项性能指标评估）。对所有算法遍历结构的轨迹分析表明，ParetoCSP生成的有效结构数量多于其他算法，这有助于更有效地引导遗传算法搜索最优结构。