Superhydrides, such as calcium superhydride (CaH4), have the potential to revolutionize hydrogen storage due to their ability to store a higher amount of hydrogen than conventional hydrides. However, their synthesis under high pressures has been a challenging task. In a groundbreaking development, researchers have utilized a machine learning model to reproduce the high-pressure synthesis reactions of superhydrides. This advancement offers a new level of precision in controlling these materials and demonstrates the power of machine learning in predicting complex chemical reactions. The study, led by Assistant Professor Ryuhei Sato and a team of collaborators, unveiled a unique reaction pathway where the surface of calcium hydride melts to absorb hydrogen molecules under high pressures, resulting in the formation of ordered calcium superhydride. By clarifying this reaction mechanism, the research contributes to a deeper understanding of high-pressure physicochemical processes and highlights the importance of easily computable material properties in determining reaction conditions. Professor Shin-ichi Orimo emphasized the significance of this research in overcoming the challenges associated with synthesizing superhydrides and praised the role of the machine learning model in accelerating material discovery. The findings published in the Proceedings of the National Academy of Sciences mark a significant step towards realizing a carbon-neutral society by enabling advancements in hydrogen storage, superconducting materials, and other cutting-edge applications. In essence, this study not only showcases the potential of machine learning in materials science but also sets a precedent for predicting unknown chemical reaction pathways, propelling the field towards new frontiers of innovation and discovery.