The discovery of novel superconducting materials is a longstanding challenge in materials science, with a wealth of potential for applications in energy, transportation, and computing. Recent advances in artificial intelligence (AI) have enabled expediting the search for new materials by efficiently utilizing vast materials databases. In this study, we developed an approach based on deep learning (DL) to predict new superconducting materials. We have synthesized a compound derived from our DL network and confirmed its superconducting properties in agreement with our prediction. Our approach is also compared to previous work based on random forests (RFs). In particular, RFs require knowledge of the chem-ical properties of the compound, while our neural net inputs depend solely on the chemical composition. With the help of hints from our network, we discover a new ternary compound $\textrm{Mo}_{20}\textrm{Re}_{6}\textrm{Si}_{4}$, which becomes superconducting below 5.4 K. We further discuss the existing limitations and challenges associated with using AI to predict and, along with potential future research directions.

翻译：新型超导材料的发现是材料科学领域长期存在的挑战，其在能源、交通和计算领域具有广阔的应用前景。人工智能（AI）的最新进展通过高效利用庞大的材料数据库，加速了新材料的探索。本研究开发了一种基于深度学习（DL）的方法来预测新型超导材料。我们合成了从DL网络推导出的一种化合物，并证实其超导特性与我们的预测一致。我们的方法也与先前基于随机森林（RF）的研究进行了比较。特别地，RF需要了解化合物的化学性质，而我们的神经网络输入仅依赖于化学成分。借助我们网络提供的线索，我们发现了一种新的三元化合物$\textrm{Mo}_{20}\textrm{Re}_{6}\textrm{Si}_{4}$，其在5.4 K以下呈现超导性。我们进一步讨论了使用AI进行预测所存在的局限性和挑战，以及潜在的未来研究方向。