New Technology– category –
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Cen-Online.org Details Breakthroughs and Industrial Applications in Catalyst Discovery Driven by Digital Tools and AI
Cen-Online.org Unknown Overview Cen-Online.org's article details recent advancements in catalysis and their industrial applications, highlighting how digital tools and AI are transforming catalyst discovery. Computational chemistry, mach... -
Lisa Pedrosa Explains the Rise of ‘Self-Driving Labs’ Integrating AI & Robotics, Transforming Scientific Discovery
Lisa Pedrosa USA Overview Lisa Pedrosa's article details how 'self-driving labs,' integrating AI, robotics, and human expertise, are fundamentally transforming scientific discovery. These labs autonomously design, execute, and analyze ex... -
Institute of Science Tokyo Develops ML Framework to Infer Semiconductor Material Parameters with High Accuracy in Under 1 Millisecond from Transistor Measurements
Institute of Science Tokyo / Advanced Intelligent Systems Japan Overview Researchers at the Institute of Science Tokyo have developed a groundbreaking machine learning framework for solving inverse problems in semiconductor materials. Th... -
U.S. Department of Energy (DOE) Collaborates with Microsoft to Advance AI Innovation Ecosystem, Accelerating Battery Material Development
Department of Energy USA Overview The U.S. Department of Energy (DOE) is actively advancing the AI innovation ecosystem, leveraging AI for advanced computing and materials research in particular. Through collaboration with Microsoft, DOE... -
Frontiers Launches Research Topic on “High-Throughput AI-Driven Materials Discovery for High-Rate Batteries,” Accelerating Next-Gen Energy Storage
Frontiers Switzerland Overview Frontiers has initiated a new research topic, "High-Throughput AI-Driven Materials Discovery and Design for High-Rate Batteries," aiming to revolutionize battery material development through machine learnin... -
Institute of Science Tokyo Dramatically Enhances Material Prediction Interpretability with ALIGNN and Clustering AI, Precisely Forecasting Optical Absorption Spectra
Lab Manager Japan Overview Researchers at the Institute of Science Tokyo have developed a novel method combining a graph neural network (ALIGNN) with hierarchical clustering to significantly improve the interpretability of AI-driven mate... -
CVC Analyzes 8 Leading AI Material Development Companies: Investment Strategies and Public Market Proxies Explored
note Japan Overview From a Corporate Venture Capital (CVC) perspective, eight key AI material development companies—Cusp AI, DeepMind, Microsoft, Orbital, Matlantis, Citrine Informatics, Aionics, and Kebotix—have been analyzed. This arti... -
Digital Twin Technology Transforms Urban Development and Manufacturing, Demonstrated by Performance Monitoring and Prediction at NASA MAF
Axiom USA Overview Digital twin technology is revolutionizing diverse sectors from urban development to manufacturing, with dynamic digital replicas of physical assets linked to real-time data for performance monitoring, behavior predict... -
Machine Learning Predicts Thermal Properties of PHB/PHBV-Based Materials with Improved Accuracy Using Integrated Polymer Database
MDPI Switzerland Overview This study reports a novel machine learning (ML) approach for predicting thermal properties (glass transition temperature, melting point, crystallization temperature) of PHB and PHBV-based materials. By building... -
ASML, TSMC, and Imec Announce Breakthrough 300mm Integration for 2D Material Transistors, Advancing Next-Gen Logic
The cleanroom Portal ヨーロッパ Overview Semiconductor industry leaders ASML, TSMC, and imec have unveiled a 300mm wafer integration process for 2D material transistors (MoS2 nFET and WSe2 pFET) featuring a 50nm contact poly pitch (CPP)....
