The search 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 subsequent crystal structure searches, from which we identify stable metallic candidates prior to performing electron-phonon calculations to obtain $T_c$. Hydrides of alkali and alkaline earth metals are identified as especially promising; of particular note, a $T_c$ of up to 115 K is calculated for RbH${}_{12}$ at 50 GPa, which extends the operational pressure-temperature range occupied by hydride superconductors towards ambient conditions.

中文翻译：

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使用机器学习方法预测新型超导氢化物

迄今为止，对超导氢化物的研究主要集中在寻找具有最高可能临界温度的材料（${Ť}_C$）。这导致偏向于在非常高的压力下稳定的材料，这在实验中引入了许多技术难题。在这里，我们运用机器学习方法来确定可以在更接近环境条件下运行的超导氢化物。这些模型的输出为后续的晶体结构搜索提供了信息，在进行电子声子计算以得到之前，我们从中确定稳定的金属候选物${Ť}_C$。碱金属和碱土金属的氢化物被认为特别有前景。特别值得注意的是，${Ť}_C$ RbH的最大计算值为115 K${}_{12}$ 在50 GPa的压力下，氢化物超导体所占据的工作压力-温度范围向环境条件延伸。