论文标题
使用机器学习方法预测新型超导氢化物
Predicting novel superconducting hydrides using machine learning approaches
论文作者
论文摘要
迄今为止，寻找超导氢化物的目的是寻找表现出最高的临界温度（$ t_c $）的材料。这导致对在很高压力下稳定的材料有偏见，这在实验中引入了许多技术困难。在这里，我们应用机器学习方法，以识别可以更接近环境条件的超导氢化物。这些模型的输出为结构搜索提供了信息，我们在执行电子 - 音波计算之前从中识别并筛选稳定的候选者以获得$ T_C $。碱和碱土金属的氢化物被确定为特别有前途。在50 GPA时，计算RBH $ _ {12} $的$ T_C $最高为115 K，在100 GPA时计算了CSH $ _7 $的$ T_C $ 90 K的$ T_C $。
Searching for superconducting hydrides has so far largely focused on finding materials exhibiting the highest possible critical temperatures ($T_c$). This has led to a bias towards materials stabilised at very high pressures, which introduces a number of technical difficulties in experiment. Here we apply machine learning methods in an effort to identify superconducting hydrides which can operate closer to ambient conditions. The output of these models informs structure searches, from which we identify and screen stable candidates before performing electron-phonon calculations to obtain $T_c$. Hydrides of alkali and alkaline earth metals are identified as particularly promising; a $T_c$ of up to 115 K is calculated for RbH$_{12}$ at 50 GPa and a $T_c$ of up to 90 K is calculated for CsH$_7$ at 100 GPa.
