Discovering excitonic superconductors

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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.