Wang Junjie, Gao Hao, Han Yu, Ding Chi, Pan Shuning, Wang Yong, Jia Qiuhan, Wang Hui-Tian, Xing Dingyu, Sun Jian
National Laboratory of Solid State Microstructures, School of Physics and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China.
Natl Sci Rev. 2023 May 8;10(7):nwad128. doi: 10.1093/nsr/nwad128. eCollection 2023 Jul.
Crystal structure predictions based on first-principles calculations have gained great success in materials science and solid state physics. However, the remaining challenges still limit their applications in systems with a large number of atoms, especially the complexity of conformational space and the cost of local optimizations for big systems. Here, we introduce a crystal structure prediction method, MAGUS, based on the evolutionary algorithm, which addresses the above challenges with machine learning and graph theory. Techniques used in the program are summarized in detail and benchmark tests are provided. With intensive tests, we demonstrate that on-the-fly machine-learning potentials can be used to significantly reduce the number of expensive first-principles calculations, and the crystal decomposition based on graph theory can efficiently decrease the required configurations in order to find the target structures. We also summarized the representative applications of this method on several research topics, including unexpected compounds in the interior of planets and their exotic states at high pressure and high temperature (superionic, plastic, partially diffusive state, etc.); new functional materials (superhard, high-energy-density, superconducting, photoelectric materials), etc. These successful applications demonstrated that MAGUS code can help to accelerate the discovery of interesting materials and phenomena, as well as the significant value of crystal structure predictions in general.
基于第一性原理计算的晶体结构预测在材料科学和固态物理领域取得了巨大成功。然而，尚存的挑战仍限制了它们在具有大量原子的系统中的应用，尤其是构象空间的复杂性以及大型系统局部优化的成本。在此，我们介绍一种基于进化算法的晶体结构预测方法MAGUS，它利用机器学习和图论解决上述挑战。详细总结了该程序中使用的技术并提供了基准测试。通过大量测试，我们证明即时机器学习势可用于显著减少昂贵的第一性原理计算的次数，并且基于图论的晶体分解能够有效减少寻找目标结构所需的构型数量。我们还总结了该方法在几个研究主题上的代表性应用，包括行星内部的意外化合物及其在高压和高温下的奇异状态（超离子态、塑性态、部分扩散态等）；新型功能材料（超硬、高能量密度、超导、光电材料）等。这些成功应用表明，MAGUS代码有助于加速有趣材料和现象的发现，以及晶体结构预测总体上的重要价值。
