New Technology– category –
-
Dongfeng Targets 2026 Solid-State Battery Mass Production, Promising 1,000 km Range and Robust Safety
CarsGuide / SMM / Gasgoo (複数情報源統合) China Overview Chinese automaker Dongfeng Motor plans to commence mass production of its proprietary oxide-polymer composite solid-state batteries by late 2026, promising New Energy Vehicles (NEV... -
Novel Material Overcomes Low-Energy Optical Modulation Barriers, Enhancing AI Scaling and Optical Networks
The Brighter Side News USA Overview Researchers have developed a novel material that enables low-energy optical signal modulation, overcoming a major barrier to AI scaling. This innovative material functions without requiring electric fi... -
HyperLight Corp. Develops Ultra-Low-Power Thin-Film Lithium Niobate Optical Modulators Operating at 0.5V with Sub-1fJ/bit Energy Consumption
PatSnap (Eureka) USA Overview HyperLight Corp. has developed groundbreaking optical modulators using thin-film lithium niobate (LNOI) technology, achieving ultra-low power consumption. These modulators operate at an exceptionally low vol... -
U.S. DOE Pioneers AI-Driven Closed-Loop Systems to Slash Material Development Time from Decades to Months
Department of Energy USA Overview The U.S. Department of Energy (DOE) announced a transformative approach integrating AI into materials design workflows, aiming to reduce time-to-market for new materials from decades to months. This init... -
Matforge Leverages AI Scientists to Break Semiconductor Material Bottlenecks, Aided by Google GNoME and Microsoft MatterGen for Novel Crystal Discovery
Founderland USA Overview San Francisco startup Matforge is deploying AI scientists to discover new semiconductor materials, aiming to alleviate material bottlenecks in the $1 trillion chip demand. This effort is bolstered by Google DeepM... -
Machine Learning Unveils Hidden Nanophotonic Resonances in Silicon-Gold Nanopillars, Accelerating Complex Material Analysis
npj Computational Materials Global Overview Machine learning (ML) has revealed previously undetectable hidden nanophotonic resonances in silicon-gold nanopillars. A new ML workflow decodes low-loss EELS data, transforming noisy nanoscale... -
Formal Theory for Crystal Structure Prediction Revolutionizes Inverse Design and Material Property Prediction
arXiv USA Overview A groundbreaking formal theory for crystal structure prediction has been introduced, detailing the inverse design of crystal structures using Constrained Crystal Deep Convolutional Generative Adversarial Networks (CG-D... -
Urgency for Biopolymer Data Standardization to Enhance Machine Learning in Materials Discovery
Biomacromolecules - ACS Publications USA Overview The integration of machine learning with biopolymer datasets critically requires robust data standardization to maximize its potential. Researchers, journals, and data providers share a r... -
Fermilab Builds Large-Scale Data Infrastructure for AI-Driven Scientific Discovery, Supporting DOE’s Genesis Mission
Fermilab USA Overview Fermilab provides the Fermi Data Platform, a secure and large-scale data infrastructure, to support the U.S. Department of Energy's (DOE) Genesis Mission, aimed at fostering AI-driven scientific discovery. This plat... -
Google DeepMind’s GNoME Discovers 2.2 Million New Crystal Structures, Propelling Clean Energy Material Design into a New Era
DEV Community 多国籍 Overview Google DeepMind's GNoME AI has identified an unprecedented 2.2 million new stable crystal structures, including 380,000 deemed practical, surpassing all previously known inorganic materials. This monumental ...
