Materials Informatics– category –
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Atinary Technologies Demonstrates Catalyst Optimization and Enhanced Process Safety with AI-Driven Closed-Loop Experiments
Atinary Technologies USA Overview Atinary Technologies has demonstrated a fundamental transformation in R&D processes through closed-loop experiments in self-driving labs, integrating AI, robotics, and human scientific expertise. By ... -
DTU Unveils ‘Self-Driving Lab’: AI and Robotics Slash Materials Discovery from Decades to Days
Mirage News デンマーク Overview The Technical University of Denmark (DTU) has unveiled an innovative 'self-driving lab' where AI and robotic arms autonomously conduct chemical experiments, promising to compress new materials development ... -
Bias Identified in Universal Machine-Learned Interatomic Potentials; Iterative Fine-Tuning Improves Accuracy
Journal of Chemical Theory and Computation (ACS Publications) USA Overview This study thoroughly investigated intrinsic biases in universal machine-learned interatomic potentials (uMLIPs), such as MACE, and their impact on fine-tuning qu... -
Meta FAIR’s Universal MLIP ‘UMA’ Precisely Models Oxygen Plasma Interactions with 2D Materials, Advancing Semiconductor Manufacturing
arXiv Unknown Overview Meta FAIR's universal machine-learned interatomic potential (MLIP) model, UMA, has demonstrated highly accurate modeling of oxygen plasma interactions with tungsten disulfide (WS2), a 2D material, with performance ... -
Machine Learning Potentials Accelerate Quantum Chemistry by Up to 1 Million-Fold, Revolutionizing Materials Science
ACS Central Science USA Overview Rapid advancements in machine learning interatomic potentials (MLIPs) are poised to accelerate quantum chemistry calculations by up to a million times, fundamentally transforming chemical and materials sc... -
Physics-Informed Foundation Model “CLOUD,” Pre-trained on Over 6 Million Crystals, Revolutionizes Materials Property Prediction
springermedizin.de Germany Overview The "CLOUD" model, a scalable, physics-informed, Transformer-based foundation model for crystal representation learning, has been introduced. Pre-trained on over 6 million crystals, CLOUD encodes cryst... -
Causal-Aware Framework “ARIA” Enhances LLM Reliability in 2D Material Design by Integrating Causal Reasoning
arXiv Unknown Overview Addressing the challenge of generative models failing to satisfy physical causality in materials discovery, the ARIA framework extends large language models (LLMs) with causal reasoning. Utilizing knowledge graphs,... -
DeepMind’s GNoME and Microsoft’s MatterGen Drastically Accelerate AI-Driven Materials Discovery, Rapidly Screening Millions of Inorganic Crystals
AI CERTs News USA Overview Advanced AI pipelines like DeepMind's GNoME and Microsoft's MatterGen are leveraging graphene neural networks and machine learning potentials to screen millions of inorganic crystals at unprecedented speeds. Th... -
University of Washington Develops Self-Improving Design Loop for New Materials via AI-Quantum Computing Fusion
richardmitnick (blog) USA Overview University of Washington research has developed a self-improving design loop for new materials through the fusion of AI and quantum computing. AI simulates complex quantum behaviors in stacked atomic sh... -
MDPI Buildings Features Mechanically Constrained GNN for Enhanced Linear Static Analysis of Planar Frame Structures
MDPI Buildings Switzerland Overview This study developed a mechanically constrained Graph Neural Network (GNN) method for 2D linear elastic static analysis of planar truss and building frame structures. The method represents structural s...
