An international research collaboration has successfully identified two new superconducting materials, YRu3B2 and LuRu3B2, leveraging machine learning to accelerate the discovery of quantum materials. Led by Aalto University Professor Päivi Törmä, the SuperC consortium demonstrated that algorithmic prescreening can efficiently filter vast elemental combinations, bypassing traditional computational bottlenecks that have historically limited superconductor discovery to fewer than 7,000 known materials. The newly identified compounds derive their zero-resistance electrical properties from electrons forming flat energy bands within a kagome lattice, a hexagonal atomic arrangement inspired by traditional Japanese basket-weaving patterns. After machine learning models narrowed down candidate structures, theoretical validations were conducted before the consortium turned to Rice University, where Professor Emilia Morosan synthesized the samples and experimentally confirmed their superconducting behavior. The findings were recently published in Physical Review Research. Superconductivity, which typically requires extreme cooling to near-absolute zero temperatures, underpins critical technologies ranging from quantum computing and magnetic levitation trains to medical neuroimaging and fusion reactors. However, the quest for a scalable, room-temperature superconductor has remained one of condensed matter physics most persistent challenges. Conventional discovery methods rely heavily on serendipity and exhaustive computational modeling, making the synthesis of viable materials both time-consuming and economically prohibitive. The SuperC approach replaces this trial-and-error paradigm with a structured pipeline: machine learning identifies promising candidates, targeted quantum geometry calculations refine the selections, and experimental validation confirms performance. By integrating algorithmic screening with precise theoretical and experimental workflows, the consortium estimates the capability to evaluate billions of material combinations, drastically compressing development timelines. Törmä emphasized that achieving room-temperature superconductivity would fundamentally reshape global energy infrastructure. Replacing conventional conductors in data centers and computing systems with room-temperature alternatives would substantially reduce electricity demand and eliminate heat management costs, offering a transformative solution for the information and communications technology sector. Formed in 2023 as a coordinated global initiative, the SuperC consortium aims to secure a practical room-temperature superconductor by 2033. The research will be featured in Aalto University Designs for a Cooler Planet exhibition in Greater Helsinki from September 1 to October 30, 2026. This breakthrough establishes a reproducible framework for next-generation quantum material discovery, marking a definitive shift from serendipitous discovery to engineered material design.