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Mathematics

Updates every hour. Last Updated: 6-May-2026 15:16 ET (6-May-2026 19:16 GMT/UTC)

Large language models unleash AI’s potential for autonomous and explainable materials discovery

Institute of Industrial Science, The University of Tokyo
Peer-Reviewed Publication

A team from the Institute of Industrial Science, The University of Tokyo has developed MatAgent, an AI system powered by a large language model. By reasoning in natural language, using memory, and learning from feedback, MatAgent explores new inorganic materials with a human-like process while explaining each step it takes.

Journal: Cell Reports Physical Science

Gottfried Wilhelm Leibniz Prizes 2026

Deutsche Forschungsgemeinschaft
Grant and Award Announcement

Germany’s most important research funding prize is presented to ten researchers / Prize money of €2.5 million each / Award ceremony to be held in Berlin on 18 March

Demonstration of altermagnetism in RuO₂ thin films -- A new magnetic material for the AI era

National Institute for Materials Science, Japan
Peer-Reviewed Publication

A joint research team from NIMS, The University of Tokyo, Kyoto Institute of Technology and Tohoku University has demonstrated that thin films of ruthenium dioxide (RuO₂) exhibit altermagnetism—the defining property of what is now recognized as the third fundamental class of magnetic materials. Altermagnets have the potential to overcome limitations associated with current magnetic random access memory using conventional ferromagnets and are attracting attention as promising materials for next-generation high-speed, high-density memory devices. In addition to identifying RuO₂ as a strong candidate for such applications, the study also highlights the possibility of enhancing its functionality through control of crystallographic orientation. The research findings were published in Nature Communications on September 24, 2025.

Journal: Nature Communications
Funder: Japan Society for the Promotion of Science, Japan Society for the Promotion of Science, Ministry of Education, Culture, Sports, Science and Technology, the GIMRT Program of the Institute for Materials Research, Tohoku University, the Cooperative Research Projects of the Research Institute of Electrical Communication, Tohoku University

Frontiers in Science Deep Dive series: How breaking the ‘memory wall’ using brain-inspired algorithms could help overcome AI energy costs

Frontiers
Meeting Announcement

Discover how next generation AI hardware—powered by brain inspired algorithms—can break the memory wall and enable faster, more efficient, and adaptable AI.

Advanced microrobots driven by acoustic and magnetic fields for biomedical applications

Beijing Institute of Technology Press Co., Ltd
Peer-Reviewed Publication

A review paper by scientists at Beijing Institute of Technology reviews the current progress of hybrid magneto-acoustic actuation to address the limitations of single magnetic or acoustic actuation.

The review paper, published on Nov. 10, 2025 in the journal Cyborg and Bionic Systems.

New research finds Zillow’s Zestimate reduces uncertainty and improves outcomes for both buyers and sellers

Institute for Operations Research and the Management Sciences
Peer-Reviewed Publication

New research published in the INFORMS journal Marketing Science finds that while Zillow’s widely used “Zestimate” home-valuation algorithm boosts efficiency in the residential real estate sector, it also significantly benefits lower-income neighborhoods even in situations where the algorithms are sometimes less accurate in those areas.

Journal: Marketing Science

Digital cognitive behavioral treatment for generalized anxiety disorder

JAMA Network
Peer-Reviewed Publication

About The Study: In this randomized clinical trial, digital cognitive behavioral therapy provided significant and sustained benefits to adults with generalized anxiety disorder. Given the limitations in access to empirically supported cognitive behavioral therapy, an efficacious digital cognitive behavioral therapy program has clear potential for public health benefit.

Journal: JAMA Network Open

Neuroanatomy-informed brain–machine hybrid intelligence for robust acoustic target detection

Beijing Institute of Technology Press Co., Ltd
Peer-Reviewed Publication

A research paper by scientists at the Beijing Institute of Technology proposed a brain–machine hybrid intelligent system for sound target detection (STD), featuring a neuroanatomy-informed EEG decoding network and an adaptive confidence-based fusion strategy to address poor robustness and limited generalization of existing methods under low signal-to-noise ratio (SNR) conditions or with unseen target classes.

The new research paper, publishede journal Cyborg and Bionic Systems, presented the development, validation, and optimization of the hybrid system, demonstrating that integrating neuroanatomical priors into EEG decoding and fusing brain-machine decisions can significantly enhance STD performance in complex acoustic environments.

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