We present a comprehensive theoretical study of conventional superconductivity in cubic antiperovskites materials with composition XYZ₃ where X and Z are metals, and Y is H, B, C, N, O, and P. Our starting point are electron–phonon calculations for 397 materials performed with density-functional perturbation theory. While 43% of the materials are dynamically unstable, we discovered 16 compounds close to thermodynamic stability and with T_c higher than 5 K. Using these results to train interpretable machine-learning models, leads us to predict a further 57 (thermodynamically unstable) materials with superconducting transition temperatures above 5 K, reaching a maximum of 17.8 K for PtHBe₃. Furthermore, the models give us an understanding of the mechanism of superconductivity in antiperovskites. The combination of traditional approaches with interpretable machine learning turns out to be a very efficient methodology to study and systematize whole classes of materials and is easily extendable to other families of compounds or physical properties.

我们对立方反钙钛矿材料中的常规超导性进行了全面的理论研究，其成分为 XYZ ₃，其中 X 和 Z 是金属，Y 是 H、B、C、N、O 和 P。我们的出发点是 397 的电子-声子计算用密度泛函微扰理论进行的材料。虽然 43% 的材料是动态不稳定的，但我们发现了 16 种接近热力学稳定性且T _c高于 5 K 的化合物。使用这些结果来训练可解释的机器学习模型，我们可以预测另外 57 种（热力学不稳定）材料超导转变温度高于 5 K，PtHBe _{3最高可达 17.8 K}. 此外，这些模型让我们了解了反钙钛矿的超导机制。传统方法与可解释的机器学习相结合被证明是一种非常有效的方法来研究和系统化整个类别的材料，并且很容易扩展到其他化合物家族或物理特性。