Researchers developed a high-entropy perovskite to protect iridium nanoparticles, improving the stability of catalysts used in water electrolysis. The new design allowed the catalyst to last over 3,800 hours with minimal loss in performance.
Researchers from Tohoku University developed a high-frequency method to read quantum states at ultra-fast speeds in 2D-material-based quantum devices. This breakthrough enables near-perfect impedance matching, unlocking sensitive radio-frequency reflectometry and accelerating qubit development.
Researchers developed an AI-assisted method to optimize woven textile patterns for sound absorption, leveraging symmetry and physics-based simulations. The approach identified promising designs in under a minute, with predicted performance matching detailed simulations within 5%.
A collaborative group of researchers has demonstrated charge sensing, high-frequency reflectometry, and the formation of a few-electron double quantum dot in a ZnO device. This breakthrough bridges a critical experimental gap for zinc oxide quantum devices, enabling the investigation of fundamental spin properties.
A new framework, PhysMat AI, integrates physical knowledge into AI for materials discovery, enabling more interpretable and testable predictions. This approach helps move materials discovery beyond correlation-based prediction toward reasoning based on physical principles.
Researchers at Tohoku University's AIMR developed carbon-supported blue-pigment catalysts for anion-exchange membrane fuel cells, achieving high power density without platinum. The team optimized the pigments' molecular structures to strengthen the interaction at the active site, translating to real gains in fuel cell performance.
An international team proposes a physics-aware framework to make AI-guided hydrogen storage materials discovery more reliable. The framework connects reproducibility-aware data, thermodynamics-constrained models, AI-driven inverse design, and experimental validation to create a learning cycle.
A new AI-powered system is being developed to discover new polymeric materials, reducing waste and accelerating innovation. The system integrates multiple tools, including polymer databases, predictive models, and automated laboratories, to create a self-automated workflow that refines itself.
Researchers from Tohoku University and Queen Mary University of London have developed a low-temperature method for graphene production, utilizing acetylene gas and cerium oxide. This breakthrough enables precise control over the material's final form and paves the way for sustainable resource recycling.
DigBat brings together solid-state electrolyte data, simulations, machine learning, and AI to support battery materials research, providing a clearer view of the solid-state electrolyte landscape. Researchers can compare experimental and computational data, build machine-learning models, and gain insight from the data.
Researchers have developed an AI-powered framework that combines multiple AI technologies with automated experiments to accelerate the discovery of advanced energy materials. The '4th+ paradigm' approach enables near-atomic-level accuracy in predicting material properties and rapidly analyzing experimental data.
Researchers from Tohoku University have created a stable version of boron graphene on the surface of a three-dimensional crystal, revealing a new quantum state. The discovery could lead to more energy-efficient electronic devices and unlock entirely new quantum phenomena.
Researchers developed an AI assistant called ChatHEA to guide the discovery of new catalysts for clean energy technologies. The team screened and evaluated 100 five-element high-entropy alloy catalysts, finding that FeCoCuPtIr showed excellent oxygen reduction activity and durability.
A new electrochemical system converts biomass-derived compounds into valuable chemicals while reducing energy consumption, producing essential products such as glutaric acid and ammonia. The nickel-vanadium layered double hydroxide catalyst accelerates both chemical reactions efficiently.
Researchers at Tohoku University have created a clearer map for searching for hydrogen storage materials, identifying key physical factors that control their performance. The study suggests adjusting geometry and lattice flexibility to raise capacity while tuning stiffness to keep equilibrium pressure near everyday conditions.
Researchers from Tohoku University and East China University of Science and Technology developed a data-driven approach to quickly screen for durable and efficient catalysts. By analyzing experimental data and scientific theories, they identified promising candidates that outperformed commercial RuO2 catalysts.
Researchers developed DigMethpy, an AI-empowered digital catalysis platform to speed up methane pyrolysis catalyst discovery. The platform uses machine learning and large language models to predict promising catalyst candidates, reducing trial-and-error experimentation.
A new strategy enhances oxygen reduction in zinc-air batteries by fine-tuning an efficient catalyst. The Fe2O3/Sm2O3 heterointerface accelerates ORR kinetics by inducing charge redistribution and orbital hybridization.
Researchers at Tohoku University's Advanced Institute for Materials Research have developed a method to summarize decades of scattered literature data into actionable information for catalyst design. By combining human intelligence, regression models, and AI agents, they can uncover new discoveries hidden in the literature data.
Researchers at Tohoku University developed a new magnesium alloy anode that balances interfacial reactions for improved battery efficiency. The optimized Mg-Sn alloy demonstrated significant improvements in electrochemical performance, including stable cycling behavior and enhanced ion transport.
Researchers from Tohoku University's Advanced Institute for Materials Research use AI and data science to extract valuable insights from decades-old experiments and scientific literature. This approach accelerates materials design and screening in catalysis, solid-state electrolytes, and hydrogen storage research.
Scientists at Tohoku University uncovered a hidden rule behind dual-atom catalysts, which follow a previously unknown 'dual-Sabatier optima' pattern. This discovery could accelerate the development of cheaper and more efficient fuel cells.
Researchers developed an AI method to automate charge transition line extraction from charge stability diagrams, enabling high-efficiency single-electron region definition and virtual gate configuration. This breakthrough aims to scale up quantum computing by handling vast numbers of qubits beyond human capability.
Researchers have developed a new approach to unlocking hydrogen from magnesium hydride using catalysts, which can reshape the release process and make it more efficient. This breakthrough has significant implications for the development of hydrogen-based energy systems and could support their broader adoption.
Researchers have discovered a new understanding of skyrmions, highly stable structures that can be moved with minimal electrical current. This breakthrough has significant implications for nanocomputing and the development of ultra-power-saving devices.
Recent progress in advanced energy manufacturing highlights 3D printing's potential to redefine next-generation lithium batteries. The technology enables precise control over three-dimensional structures, improving ion-transport pathways and mechanical robustness.
Researchers at Tohoku University have made significant progress in precise nanoscale construction of g-C₃N₄ catalysts, which enables efficient photocatalytic H₂O₂ evolution. The study highlights the importance of nanoarchitectonics in scaling up industrial production.
Researchers from Tohoku University examined the role of materials databases in supporting modern artificial intelligence tools used in materials science. They found that database architecture can directly affect AI model performance and reliability. The study aims to improve database quality, connectivity, and develop new AI systems th...
Researchers developed a lightweight lattice structure inspired by butterfly wings, exhibiting enhanced mechanical strength, impact resistance, and energy absorption capabilities. The new design outperforms conventional lattice designs under compression and dynamic impact loading.
Scientists at Tohoku University's Advanced Institute for Materials Research have created a more efficient way to turn methane into hydrogen by combining chemical looping with water splitting. This new method achieves low-temperature methane reforming at 500-600°C, significantly reducing energy consumption and carbon emissions.
Researchers used AI to identify key characteristics of catalysts and guide their designs, discovering a universal design principle for copper-based single-atom alloy catalysts. The approach uses machine learning to predict catalyst performance and inspire generalizable design principles.
Researchers at Tohoku University's Advanced Institute for Materials Research developed an unprecedented method to bond lithium metal directly to garnet-type oxide electrolyte using ultrasonic welding. This technique reduces interfacial resistance and establishes direct solid-state contact without melting or thermal activation.
Researchers at Tohoku University have developed a comprehensive digital materials ecosystem that integrates AI tools to streamline materials design, enabling faster and more accurate discovery of new materials. The ecosystem uses databases, AI, and scientific workflows to predict material properties and optimize design processes.
A new Y-doped catalyst has been developed to efficiently transform ammonia into sustainable hydrogen energy, enabling a cleaner energy future. The catalyst, composed of nickel and yttrium, improves the performance of the ammonia decomposition reaction, overcoming issues of intrinsic activity and energy barriers.
Researchers at Tohoku University discovered that subtle variations in coordination symmetry significantly alter reaction energetics and product selectivity in single-atom electrocatalysts. Asymmetric Co-N3 sites exhibited enhanced overall ORR activity, while lower-symmetry Co-N5 centers achieved the highest selectivity toward hydrogen ...
Researchers at Tohoku University's Advanced Institute for Materials Research developed distortion-resistant energy materials for lithium-ion batteries, improving efficacy and cost-effectiveness. The cathode design utilizes 'interfacial orbital engineering' to neutralize Jahn-Teller distortions, achieving near-perfect cycling stability.
Developed at Tohoku University's Advanced Institute for Materials Research, the new catalyst enables smoother hydrogen formation under alkaline conditions. The auxiliary-driving strategy improves both steps of the hydrogen evolution reaction, resulting in higher hydrogen evolution activity and efficient production with low energy loss.
A team of researchers from Tohoku University's WPI-AIMR has developed a new class of hydrogen storage alloys that can store large amounts of hydrogen while remaining thermodynamically stable. By controlling magnetism, the researchers were able to design materials that combine high hydrogen capacity with good stability.
A new catalyst enables efficient and stable electrosynthesis of ethylamine at industrial scale, overcoming long-standing challenges in selectivity loss and instability. The developed method supports continuous energy-efficient production of EA using electricity and water.
Researchers at Tohoku University developed DIVE, an AI multi-agent workflow that extracts information from images to propose new materials within minutes. The system outperforms commercial models, offering 10-15% better accuracy and coverage of data extraction.
Researchers discovered that topological surface states can survive and be optimized by electrochemical reconstruction, leading to near-peak ORR activity. The study reveals the importance of considering quantum topology and electrochemical surface chemistry together for next-generation electrocatalysts.
Researchers are leveraging AI agents to design and evaluate solid electrolytes for safer, more reliable batteries. By integrating data analysis, materials modeling, simulations, and experimental planning, these AI agents enable coordinated research strategies that evolve as new information becomes available.
Researchers at Tohoku University's WPI-AIMR propose Harvested Reservoir Computing framework that harnesses road traffic dynamics for energy-efficient AI systems. The approach yields better prediction accuracy before congestion begins, reducing computational overhead and energy consumption.
Researchers developed a computational framework to identify effective catalysts for producing hydrogen peroxide from water and electricity. The approach successfully predicted key reaction properties across diverse materials, leading to the discovery of promising candidate lithium scandium oxide.
A breakthrough in carbon-based battery materials has improved safety and performance by re designing fullerene molecule connections. This research provides a blueprint for designing next-generation battery materials that support safer fast-charging, higher energy density, and longer lifetimes.
Researchers used causal AI to extract insights from ARPES data of cesium vanadium antimonide, a kagome superconducting material. The technology revealed that the chemical bonding state of cesium atoms strongly influences the electronic state of the V3Sb5 layer, responsible for superconductivity.
Researchers at AIMR discovered that Europium substitution in Cu2O catalysts allows for selective control of electrochemical CO2 reduction products. By leveraging the Eu3+/Eu2+ redox couple, they demonstrated how subtle changes in electronic structure can favor either C-C coupling or deep hydrogenation.
Researchers at AIMR and UC Santa Barbara develop a breakthrough digital p-bit design that eliminates bulky analog components, enabling self-organizing hardware-based probabilistic computing. This advances applications in AI, logistics, scientific discovery, and future computing systems.
Researchers have developed an electrocatalyst that efficiently converts nitrate into ammonia at low concentrations and gentle voltage. The catalyst reduces emissions linked to fertilizer and chemical manufacturing, and enables recycling of nitrate, a common pollutant found in groundwater and agricultural runoff.
A new damage-driven lifetime design methodology has been introduced to predict the lifespan of mechanical equipment used in clean-energy systems. The research offers more consistent lifetime predictions than conventional models and provides a framework for designing reliable and climate-conscious infrastructure.