Superhydrides—materials capable of storing much more hydrogen than conventional hydrides—offer exciting possibilities for technologies like hydrogen storage, maglev transportation, and quantum computing. Despite their potential, synthesizing these compounds has proven extremely difficult, requiring pressures of tens of gigapascals. This has made the reactions difficult to control and replicate, slowing progress in the field.

A recent breakthrough, published on May 29, 2025, in Proceedings of the National Academy of Sciences, presents a solution. A research team has successfully modeled the high-pressure synthesis of superhydrides using a machine learning approach, setting a precedent for applying artificial intelligence to predict previously unknown chemical reaction pathways.

Professor Shin-ichi Orimo of the Advanced Institute for Materials Research (WPI-AIMR) explains how challenging this field has been: "The synthesis of calcium superhydride (CaH₆), which features a hydrogen-to-metal ratio of 6:1, took ten years to achieve after its structure was initially predicted."

Because conventional methods such as thermal analysis are limited at high pressures, researchers have struggled to fully understand how superhydrides form. This lack of insight has slowed development and presented a major obstacle in using these materials to support a carbon-neutral future.

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In this new study, a team led by Assistant Professor Ryuhei Sato at the University of Tokyo, working alongside Professor Orimo, Professor Hao Li from Tohoku University, and Professor Chris Pickard from the University of Cambridge, created a machine learning potential trained on data from known calcium hydrides and hydrogen. This model was then used to simulate the synthesis process.

The simulation revealed a key reaction mechanism: under extreme pressure and high temperature, the surface of calcium hydride (CaHâ‚‚) melts and absorbs hydrogen molecules. This melted surface then solidifies into calcium superhydride (CaHâ‚„), completing the transformation.

The identified mechanism—pressure-induced surface melting followed by hydrogen absorption and resolidification—may be a general pathway in high-pressure hydrogen reactions. By highlighting the importance of material properties like melting points in determining reaction conditions, this research offers new directions for designing superhydride synthesis strategies.

Professor Orimo emphasizes the broader significance of the findings: "This work shows how machine learning can break new ground in materials science by uncovering reaction pathways we couldn’t predict before."

By opening up a new frontier in reaction modeling, this study not only advances the development of superhydrides but also marks a major step forward in integrating machine learning into experimental design across chemistry and physics.