Finding superionic conductors (SICs) has always been an arduous task in material science, not to mention large accessible SIC databases. How to obtain SICs by directed design in the high-dimensional complex chemical space is also an unexplored challenge. To reduce experimental and computational effort associated with directed SIC design and to explore the chemical space encoded by structure, component and site simultaneously, we introduce IonML, a physically inspired machine learning (ML) platform to efficiently directed design SICs. In the case of Li⁺ conductors, it features an ensemble learning model that identifies SICs in 0.8 s with a high precision of 90.4% and reveals the effects of chemical factors on Li⁺ migration; it integrates an active learning with physically inspired model that enables directed SIC design within only 2 min. IonML possesses a database of over 19,000 SICs in which we rapidly identify 61 new stable SICs. Importantly, for the remaining non-SICs with low ionic conductivities, IonML directed designs 65 new SICs by regulating the chemical space to “turn waste into treasure”. IonML highlights the utility of combining fingerprint, prediction, screening, and directed design with explainable ML, and can be extended to all types of SICs (Na⁺, Mg²⁺, Al³⁺) or other purpose-driven material design systems.

寻找超离子导体 (SIC) 一直是材料科学中的一项艰巨任务，更不用说大型可访问 SIC 数据库了。如何在高维复杂化学空间中通过定向设计获得SIC也是一个未探索的挑战。为了减少与定向 SIC 设计相关的实验和计算工作量，并同时探索由结构、组件和位点编码的化学空间，我们引入了 IonML，这是一种受物理启发的机器学习 (ML) 平台，可有效地定向设计 SIC。对于 Li ⁺导体，它具有集成学习模型，可在 0.8 秒内识别 SIC，精度高达 90.4%，并揭示化学因素对 Li ^+的影响移民; 它将主动学习与受物理启发的模型相结合，仅需 2 分钟即可实现定向 SIC 设计。IonML 拥有一个包含超过 19,000 个 SIC 的数据库，我们可以在其中快速识别 61 个新的稳定 SIC。重要的是，对于剩余的离子电导率较低的非 SIC，IonML 指导设计了 65 个新的 SIC，通过调节化学空间来“变废为宝”。IonML 突出了将指纹、预测、筛选和定向设计与可解释的 ML 相结合的实用性，并且可以扩展到所有类型的 SIC（Na ⁺、Mg ²⁺、Al ³⁺）或其他目的驱动的材料设计系统。