基于机器学习势的多组分无机晶体平衡结构快速识别
Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials
摘要：
新的多组分无机化合物的发现可以为许多科学和工程挑战提供直接解决方案，但未知材料空间的巨大规模使目前的合成产量相形见绌。虽然计算晶体结构预测有望减轻这种挫折，但密度泛函理论（DFT）计算的NP-硬度和陡峭的成本阻止了大规模的材料探索。在这里，我们介绍了SPINNER，一个高效可靠的结构预测框架，基于穷举随机搜索和进化算法，这是完全免费的。通过精确的神经网络势能，该程序可以比DFT快10倍以上的$^{2}$导航配置空间。在对从实验数据库中选择的60种三元组合物的盲测中，SPINNER在5000代内成功地识别了约80%的材料的实验（或理论上更稳定）相，对每种组合物进行了多达50万次的结构评估。当以以前的数据挖掘或基于DFT的进化预测为基准时，SPINNER在大多数情况下识别出更稳定的阶段。通过开发一个可靠和快速的结构预测框架，这项工作为未发现的无机晶体的大规模，无界计算探索打开了大门。
英文摘要：
The discovery of new multicomponent inorganic compounds can provide direct solutions to many scientific and engineering challenges, yet the vast size of the uncharted material space dwarfs current synthesis throughput. While the computational crystal structure prediction is expected to mitigate this frustration, the NP-hardness and steep costs of density functional theory (DFT) calculations prohibit material exploration at scale. Herein, we introduce SPINNER, a highly efficient and reliable structure-prediction framework based on exhaustive random searches and evolutionary algorithms, which is completely free from empiricism. Empowered by accurate neural network potentials, the program can navigate the configuration space faster than DFT by more than 10$^{2}$-fold. In blind tests on 60 ternary compositions diversely selected from the experimental database, SPINNER successfully identifies experimental (or theoretically more stable) phases for ~80% of materials within 5000 generations, entailing up to half a million structure evaluations for each composition. When benchmarked against previous data mining or DFT-based evolutionary predictions, SPINNER identifies more stable phases in the majority of cases. By developing a reliable and fast structure-prediction framework, this work opens the door to large-scale, unbounded computational exploration of undiscovered inorganic crystals.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
