Finding better materials to store hydrogen safely and efficiently is key to clean energy, but AI models trained on incomplete data can recommend candidates that don't actually work in practice. An international team led by Tohoku University, drawing on insight from 69 researchers, has proposed a "physics-aware" framework that combines thermodynamic constraints, reproducible data, and real experimental feedback to make AI-guided materials discovery more reliable. The roadmap could help accelerate the search for practical hydrogen storage solutions to support future clean-energy systems.

Nanographenes can be considered as molecular fragments of graphene, a 2D conductive material in which carbon atoms are connected in a honeycomb pattern. Because their electronic and photophysical properties vary depending on the size and shape of the molecule, nanographenes are expected to find applications in OLEDs, organic solar cells, organic field-effect transistors, and more. For this, simple methods to synthesizing nanographenes that avoid complex multi-step processes are desired. Now, a team of scientists from WPI-ITbM at Nagoya University and from RIKEN have established a new two-step annulative p-extension (APEX) method that generates structurally diverse nanographenes. This research was published online in the scientific journal Angewandte Chemie International Edition on August 25.

Researchers at Tohoku University have combined multiple tools to create an automated, self-improving AI workflow that may improve our search for polymer material candidates so that it is less expensive, less costly, and more environmentally friendly.

An international team of researchers led by Kavli IPMU have used the JWST to uncover growing black holes that existed approximately 12.5 to 12.8 billion years ago, which are heading toward a merger.

Instead of producing graphene at temperatures as high as 900°C, researchers at Tohoku University found a way to lower this to 300 °C. Even better, the material used to achieve this can be easily obtained from industrial waste.

The manipulation of a ferromagnet’s magnetization by circularly polarized light is traditionally explained by an effective magnetic field aligned with the light wavevector. Researchers at AIMR studying cobalt-platinum alloy films found a substantial, previously unreported torque that this picture cannot account for—one driven instead by electrons’ orbital angular momentum, coupled to magnetization through spin-orbit interaction.

As solid-state electrolyte research grows, so does the amount of data. The problem is that much of it remains scattered across the literature. That is where researchers at WPI-AIMR, Tohoku University, come in — with DigBat, a new platform designed to bring this information together and help map the growing landscape of solid-state electrolyte research. By connecting data from across the field, DigBat can help researchers see relationships more clearly and point toward promising directions for future battery materials research.

For decades, scientists have understood proteins primarily through two defining features: their amino acid sequence and their three-dimensional structure. This framework has driven major advances in biology, biotechnology, and medicine, culminating in recent artificial intelligence tools capable of predicting protein structures with remarkable accuracy. Yet a fundamental challenge remains: even when a protein's sequence and structure are known, predicting its function, interactions, and behavior often remains difficult. An international team of researchers from Japan, Finland, Italy, and the United States now reports evidence that part of this missing information may lie in an often-overlooked component of proteins: the highly organized water surrounding them. In a study accepted for publication in Nature Communications, the team provides the first direct three-dimensional visualization of sequence-dependent hydration architectures surrounding peptide assemblies at sub-nanometer resolution using advanced three-dimensional atomic force microscopy (3D-AFM). The findings reveal that water near peptide and protein surfaces is not simply a passive solvent. Instead, it forms highly organized, multilayered structures whose organization is dictated by the underlying amino acid sequence and surface chemistry. These hydration architectures extend multiple layers from the peptide surface and contain distinct structural signatures associated with hydrophobic, polar, aromatic, and charged amino acid residues.