Materials Informatics– category –
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UAE Powers Strategic Industries with AI-Driven Materials Discovery Acceleration
The National News UAE Overview The United Arab Emirates is making significant strategic investments in AI infrastructure to revolutionize materials discovery, shifting from laborious trial-and-error to rapid, prediction-based approaches.... -
University of Washington Develops Self-Healing Polymer Library ‘DPAL’ with Machine Learning, Accelerating Materials Design
EurekAlert! USA Overview University of Washington researchers have developed a novel machine learning approach to accelerate polymer material design and discovery. They created the "Dynamic Polymer Annotated Library (DPAL)" with self-hea... -
QIAGEN Launches QIAsymphony Connect for Automated Clinical Nucleic Acid Extraction, Securing FDA/EUDAMED Approvals
QIAGEN N.V. Germany Overview QIAGEN N.V. announced the commercial release of QIAsymphony Connect, an automated clinical nucleic acid extraction system. This platform integrates liquid handling, prefilled IVD reagent cartridges, automated... -
Closing the Loop in AI-Driven Biomedical Discovery: LLM Agents Generate Scientific Reasoning and Interpret Experimental Results
Preprints.org Switzerland Overview This article explores how to close the 'loop' from hypothesis to experiment and revised hypothesis in AI-driven scientific discovery. Large Language Model (LLM) agents are highlighted for their role in ... -
Citrine Informatics Partners with Forum Rathenau to Accelerate Germany’s Green Deep Tech Ecosystem with AI Platform
AZoM Germany Overview Forum Rathenau and Citrine Informatics announced a strategic partnership to accelerate Germany's green deep tech ecosystem. This collaboration supports the 'Scale New Chemistry' bootcamp, aiming to transform challen... -
Google DeepMind’s GNoME Discovers 2.2 Million New Crystals: Accelerating Materials Science by 800 Years, Offering Novel Superconductor and Battery Candidates
Facebook (DeepMind) UK Overview Google DeepMind's AI tool, GNoME, has reportedly discovered 2.2 million new inorganic crystals, accelerating materials science progress by 800 years. GNoME leveraged AI models trained on existing materials... -
Self-Driving Labs: Exploring the Feasibility of Closed-Loop Experimentation in Pharma R&D, Addressing XRD Data Challenges
Sakara Digital USA Overview This article discusses the feasibility of autonomous closed-loop experimentation in pharmaceutical R&D, identifying criteria for suitability and practical bottlenecks. The A-Lab team emphasizes demonstrati... -
X2DB: Unifying Experimental and Computational 2D Materials Data to Accelerate AI-Driven Discovery
DTU Research Database Denmark Overview A new open infrastructure, X2DB, has been established to integrate experimental and computational data for 2D materials on a massive scale, centralizing previously dispersed knowledge. This robust d... -
Argonne National Laboratory Accelerates AI-Driven Materials Discovery: 18 Promising Battery Candidates Identified from 500,000 Materials in 80 Hours
Facebook (Argonne National Laboratory) USA Overview Argonne National Laboratory dramatically accelerated materials discovery using an AI-driven method, identifying 18 promising battery development candidates from 500,000 stable materials... -
Autonomous Labs Accelerate Materials Discovery: Key US Labs Invent New Materials with AI-Driven Robots
Architect Magazine USA Overview AI-driven autonomous laboratories are rapidly advancing, exemplified by Lawrence Berkeley National Laboratory's A-Lab, Carnegie Mellon University's Materials Innovation Cloud Lab (MICL), and Texas A&M ...
