Materials Informatics– category –
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University of Toronto’s Acceleration Consortium Drives Material Development with Autonomous Labs (SDLs), Dramatically Cutting Time and Cost
Harrowings - Substack Canada Overview The University of Toronto's Acceleration Consortium (AC) is demonstrating how Self-Driving Labs (SDLs) are maturing into a transformative infrastructural layer for physical sciences, aiming to signif... -
DOE-University Alliance Accelerates Custom Polymer Development via Autonomous AI Inverse Design Workflow and Polybot
Tech Briefs USA Overview Researchers from Argonne National Laboratory (DOE), the University of Chicago, and Purdue University have demonstrated a faster route from target properties to polymer recipes using an autonomous AI inverse desig... -
Argonne National Laboratory Leverages AI and ML for Atomic-Level Design of 2D MXene Materials, Opening Diverse Applications
Argonne National Laboratory USA Overview Scientists at Argonne National Laboratory have unveiled new insights into the design and application of MXene, a rapidly growing class of 2D materials. By utilizing AI and machine learning, resear... -
IBS Develops Crossbreeding Neural Network Enabling AI to Discover Catalysts from Disparate Material Families
Lab Manager South Korea Overview Researchers at the Institute for Basic Science have developed the "Crossbreeding Neural Network (CBNN)" deep learning model to overcome limitations in traditional machine learning for materials. This mode... -
UChicago’s “ElectrolyteGPT” Unleashes AI-Powered Autonomous Generation of Battery Electrolyte Formulations
UChicago News USA Overview Researchers at the University of Chicago Pritzker School of Molecular Engineering have developed "ElectrolyteGPT," an AI model capable of generating entire battery electrolyte compositions autonomously. This AI... -
Argonne National Laboratory Unveils Roadmap for AI-Driven Autonomous Labs to Revolutionize Battery Research with Large Language Models
Argonne National Laboratory USA Overview Researchers at Argonne National Laboratory have outlined a comprehensive technical roadmap for applying Large Language Models (LLMs) to battery research. Integrated into AI-driven autonomous labs ... -
World Economic Forum: AI-Driven Materials Discovery Boosts Industrial Experiment Throughput by 5500%, Cuts R&D to Weeks
The World Economic Forum Switzerland Overview The World Economic Forum announced the third cohort results of its MINDS initiative, reporting that closed-loop autonomous platforms have boosted industrial experiment throughput by up to 5,5... -
Generative AI Breakthrough: Kemira and CuspAI Engineer 5,000+ PFAS-Fighting MOFs in Just Six Months
Water Technology フィンランド/UK Overview Finnish chemical company Kemira and UK-based CuspAI have employed generative AI to rapidly design over 5,000 novel Metal-Organic Frameworks (MOFs) for PFAS removal in just six months. This unprec... -
Finland’s VTT Unveils ‘RADIANT’ Project: AI to Shrink Materials Development from Years to Months
VTT フィンランド Overview Finland's VTT Technical Research Centre and the University of Helsinki have launched the AI-driven "RADIANT" project, aiming to slash new materials development time from years to months. This platform integrates... -
Google Research Introduces “Matter to Mechanism” Benchmark to Accelerate Battery Research with AI Co-Scientists
Google Research USA Overview Google Research has introduced "Matter to Mechanism," a benchmark to evaluate AI co-scientists' ability to derive plausible, mechanism-based solution hypotheses from specific scientific and technical problems...
