Quasicrystals predicted and discovered by machine learning
Quasicrystals predicted and discovered by machine learning
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DOI：
10.1103/physrevmaterials.7.093805
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发表时间：
2023-09-25
影响因子：
3.4
通讯作者：
Yoshida, Ryo
中科院分区：
文献类型：
--
作者：
Liu, Chang;Kitahara, Koichi;Yoshida, Ryo
准晶代表了一类有序的材料，具有周期性晶体中禁止的衍射对称性。自从1984年首次发现准晶以来，已经合成了大约100种晶体稳定的准晶。新的准晶的发现导致了新的物理现象的观察，如稳健的量子临界性，分形超导性和特殊的长程磁有序。然而，新准晶的发现速度明显放缓，这归因于缺乏探索新准晶的设计原则。在这里，我们证明了机器学习可以大大加快材料发现的过程。我们的模型可以预测稳定的准晶相与高精度。在此基础上，通过对1000多个三元铝合金体系的穷尽筛选，我们发现了三个稳定的十次准晶。
Quasicrystals represent a class of ordered materials that have diffraction symmetry forbidden in periodic crystals. Since the first discovery of quasicrystals in 1984, approximately 100 thermodynamically stable quasicrystals have been synthesized. The discovery of new quasicrystals has led to the observation of novel physical phenomena, such as robust quantum criticality, fractal superconductivity, and peculiar long-range magnetic ordering. However, the pace of discovery of new quasicrystals has significantly slowed down, which is attributed to the lack of design principles for exploring new quasicrystals. Here, we demonstrate that machine learning can greatly accelerate the process of material discovery. Our model can predict stable quasicrystalline phases with high accuracy. With this model, we discovered three stable decagonal quasicrystals through an exhaustive screening of more than 1000 ternary aluminum alloy systems.