Materials Informatics– category –
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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).... -
South Korea’s Pohang City to Establish AI-Driven ‘K-Graphene Foundry’ with Government Support, Accelerating 2D Material R&D
The Investor South Korea Overview South Korea's Pohang City has announced plans to establish the 'K-Graphene Foundry,' an AI-driven manufacturing and validation platform for graphene and other 2D materials, with government backing. This ... -
South Korea’s Hanwha Essential Advances Localization of AI Chip Substrate Build-up Film, Challenging Market Monopoly
The Elec Inc. South Korea Overview South Korea's Hanwha Essential has announced accelerated efforts towards localizing build-up film (BuF), a critical material for AI semiconductor substrates, now entering customer sample evaluation and ... -
IBM Revolutionizes AI Chip Design with Sub-1 nm NanoStack, Achieving 50% Performance Boost or 70% Power Reduction
Data Center Knowledge USA Overview IBM has announced "NanoStack," an innovative semiconductor technology extending chip scaling to sub-1 nm to meet the growing demands of AI workloads. This 3D architecture, which vertically stacks transi... -
Applied Materials Unveils New Manufacturing Systems to Accelerate DRAM and Advanced Packaging for AI Chips
GLOBE NEWSWIRE USA Overview Applied Materials has announced a suite of innovative chip manufacturing systems to accelerate the production of advanced 3D chip architectures (DRAM and advanced packaging) critical for next-generation AI wor... -
LLM and MLIP Break Solid Electrolyte Discovery Bottlenecks, AI Closed-Loop Architecture Accelerates Development
arXiv Unknown Overview An innovative approach integrating large language models (LLMs) and machine learning potentials (MLIPs) is proposed to address bottlenecks in solid electrolyte discovery. A closed-loop architecture, combining AI-dr... -
AI-Physics Collaboration Achieves Breakthrough in Hydrogen Storage Material Design, Pinpointing Optimal Lattice Architectures
Phys.org USA Overview An innovative collaboration between AI and physics has yielded a breakthrough in hydrogen storage material design. Research revealed a strong correlation between hydrogen storage capacity and the atomic-scale geomet... -
NUS and University of Toronto Launch $10M ‘Materials Data Foundry’ to Accelerate AI Materials Discovery
AcademicJobs.com Singapore Overview National University of Singapore (NUS) and the University of Toronto Acceleration Consortium have launched a $10 million 'Materials Data Foundry,' an open autonomous platform combining AI and robotics.... -
NC State Develops ‘Flex-Cat’ Self-Driving Lab, Autonomously Discovering Catalysts for On-Demand Product Switching
North Carolina State University USA Overview Researchers at North Carolina State University have developed 'Flex-Cat,' a self-driving chemistry lab integrating robotics, high-pressure chemical reactors, automated analysis, and AI. This s...
