标题： 基于深度学习的超导性：预测与实验测试 显示英文标题

标题： Deep Learning Based Superconductivity: Prediction and Experimental Tests

摘要： 发现新型超导材料是材料科学中的一个长期挑战，具有在能源、交通和计算领域的广泛应用潜力。 人工智能（AI）的最新进展使得通过高效利用大量材料数据库来加速新材料的搜索成为可能。 在本研究中，我们开发了一种基于深度学习（DL）的方法来预测新的超导材料。 我们合成了一种来自我们的DL网络的化合物，并确认其超导特性与我们的预测一致。 我们的方法还与基于随机森林（RFs）的先前工作进行了比较。 特别是，RFs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组成。 在我们的网络提供的提示帮助下，我们发现了一种新的三元化合物$\textrm{Mo}_{20}\textrm{Re}_{6}\textrm{Si}_{4}$，它在5.4 K以下表现出超导性。 我们进一步讨论了使用AI进行预测所面临的现有局限性和挑战，以及潜在的未来研究方向。 显示英文摘要

摘要： 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.