Discovering excitonic superconductors

常压下室温超导的发现有可能给众多行业带来革命性的变化，并推动能源技术的重大进步。已提出的实现高温超导的机制之一是通过光诱导过程形成激子，这可以促进电子配对，并使超导电流在比传统声子介导的超导体高得多的温度下运行。虽然先前的研究发现碳层上的过渡金属二卤代化合物(TMD)是观察激子介导的超导电性的有希望的候选者，但实验证实还没有观察到。在这项工作中，我们使用一种物理信息的机器学习方法来识别、合成和测试碳化层上的TMD作为潜在的激子超导体候选。我们建议的框架包括使用通过自动第一原理计算获得的带隙和激子结合能等计算性质来构建机器学习模型。此外，我们将实验获得的数据和材料数据库(如材料项目)中现有的信息进行整合，以增强机器学习模型的预测能力。在TMDS性能预测的指导下，我们进行了合成实验，主要是利用热化学方法来合成TMDS纳米颗粒，这些纳米颗粒随后被用于覆盖碳纤维，或者被覆上碳本身。然后使用光学低温恒温器对合成的材料进行表征和超导电性测试，使光能够穿透，同时将样品冷却到接近绝对零度的温度。随后，获得的数据被用于完善机器学习模型的预测，合成和表征的循环继续，导致揭示新型TMD材料的特性。

The discovery of room temperature superconductivity at ambient pressure has the potential to revolutionize numerous industries and drive significant advancements in energy technologies. One of the proposed mechanisms for achieving high-temperature superconductivity involves the formation of excitons through light-induced processes, which can facilitate the pairing of electrons and enable superconducting currents at significantly higher temperatures than traditional phonon-mediated superconductors. While previous research identified transition metal dichalcogenides (TMDs) on carbonaceous layer as promising candidates for observing exciton-mediated superconductivity, experimental confirmation has yet to be observed. In this work, we employ a physics-informed machine learning approach to identify, synthesise and test TMDs on a carbonaceous layer as potential excitonic superconductor candidates. Our proposed framework involves construction of a machine learning model using computed properties such as band gap and exciton binding energies obtained through automated first-principles calculations. Additionally, we integrate experimentally acquired data and existing information available in materials databases, such as the Materials Project, to enhance the predictive capabilities of the machine learning model. Guided by the TMDs property predictions, we perform synthesis experiments mainly employing thermochemical methods to synthesise TMDs nanoparticles that are subsequently used to coat carbon fibres, or that are coated with carbon themselves. The synthesised materials are then characterized and tested for superconductivity using an optical cryostat, enabling light penetration while cooling the samples to near absolute zero temperatures. The acquired data are subsequently used to refine the machine learning model's predictions, and the cycle of synthesis and characterization continues, leading to uncovering properties of novel TMD materials.