Materials Informatics– category –
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arXiv: Framework Proposed to Convert Unstructured Scientific Literature into AI-Ready Knowledge Base for Accelerated Discovery
arXiv Global Overview An arXiv preprint introduces an autonomous framework to transform unstructured scientific literature into a unified, AI-ready scientific knowledge base, accelerating AI-driven scientific discovery. The framework inc... -
CatDT Unveils Autonomous Heterogeneous Catalyst Discovery Digital Twin with High Accuracy Against Experimental Data
Academic Journal Global Overview CatDT (Catalysis Digital Twin) has been presented as a high-fidelity, condition-aware catalyst simulator for autonomous heterogeneous catalyst discovery. It integrates gas-solid and liquid-solid modeling,... -
CASUS Discovers Noble-Metal-Free Solar Fuel Catalysts via AI-Driven Computational Approach, Boosting H2 Generation and CO2 Reduction
TechTrek Germany Overview The Center for Advanced Systems Understanding (CASUS) at Helmholtz-Zentrum Dresden-Rossendorf (HZDR) has developed an AI-driven computational approach, combining machine learning, quantum chemical calculations, ... -
arXiv: Self-Organizing NCA Enables One-Shot Generative Design of Disordered Metamaterials for Biomedical and Robotics Applications
arXiv Global Overview An arXiv preprint introduces a novel generative design framework for disordered metamaterials using self-organizing Neural Cellular Automata (NCA) to overcome data-intensive generative AI limitations. The NCA framew... -
arXiv: New ‘ATR’ Framework Enhances Dynamic Stability of MLIPs by Actively Rejecting Pseudo-Labels
arXiv Global Overview A new arXiv preprint introduces the 'Adaptive Multi-Teacher Routing (ATR)' framework, which significantly improves the reliability and generalization capability of universal machine-learning interatomic potentials (... -
IBM and ORNL Pioneer Hybrid Quantum-AI Simulation for Fusion Reactor Tritium Generation Material
Live Science USA Overview Scientists from IBM and Oak Ridge National Laboratory (ORNL) have successfully conducted the world's first simulation of tritium generation, a rare hydrogen isotope critical for fusion reactors, using a hybrid q... -
Osaka University Develops AI-Designed Super-Adhesive Hydrogel with Self-Healing and High Mechanical Strength Underwater
The Times of India Japan Overview Researchers at Osaka University and collaborating Japanese institutions have developed a novel AI-designed super-adhesive hydrogel that exhibits superior underwater adhesion, self-healing capabilities, a... -
Tokyo University of Science Develops AI-Assisted Method for Catalyst Design Balancing Activity and Stability
Science Tokyo / Mirage News Japan Overview Researchers at Tokyo University of Science have developed a new AI-assisted method for designing catalysts that achieve both high activity and high stability, a long-standing challenge in the fi... -
Self-Driving Labs, Integrating AI and Automated Experimentation, Accelerate Chemical and Materials Discovery
ResearchGate (Review) Global Overview Autonomous self-driving labs (SDLs), which integrate machine learning with automated experimental platforms, are dramatically accelerating the pace of molecular and materials discovery in chemistry a... -
Machine Learning Transforms Materials Science: From Property Prediction to Structural Design, Emphasizing LLM Validation
Academic Review / Journal Global Overview A new academic review highlights the transformative role of machine learning (ML) in materials science, detailing its impact on property prediction, novel material discovery, and process optimiza...
