Machine Learning– tag –
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MDPI: ML-Based Structure-Property Relationship Modeling Boosts Polymer Property Prediction Accuracy
MDPI Switzerland Overview Research published in MDPI proposes a comprehensive machine learning (ML)-based framework for predicting polymer physical properties and identifying structure-property relationships (SPR), improving prediction a... -
arXiv: PolyGraphPy Unifies Atomistic Simulation and ML-Driven Polymer Design in a Python Framework
arXiv Unknown Overview A new paper on arXiv introduces "PolyGraphPy," a unified Python framework for atomistic simulation and machine learning (ML)-driven polymer design. This open-source framework seamlessly integrates atomistic simulat... -
Topsoe Declares AI the “Fifth Paradigm” in Materials Science, Poised to Revolutionize Catalysis, Electrolysis, and Battery Design
Topsoe デンマーク Overview Topsoe, a global leader in catalyst technology, has announced that AI is emerging as the "fifth paradigm" in materials science, fundamentally transforming material discovery and design. Their AI systems combine... -
AI Inverse-Designs “Super-Material” Stronger Than Steel and Lighter Than Foam
Medium - Dark Energy Articles USA Overview Artificial intelligence has reportedly developed a revolutionary carbon-based nanolattice "super-material" that is stronger than steel yet significantly lighter than foam. This achievement demon... -
NC State’s PoLARIS Autonomous Lab Discovers Lead-Free Nanoplatelets in 12 Hours, Accelerating Scientific Discovery by 100x
NC State News USA Overview NC State University's Self-Driving Lab (SDL) technology intelligently plans and executes experiments to discover optimal "recipes" for new molecules and materials, accelerating discovery up to 100 times faster ... -
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... -
ASM International Highlights Accelerated Computational Materials Design Integrating CALPHAD, DFT, MLIPs, and AI Agents
ASM International USA Overview An ASM International webinar showcased recent advancements in accelerating computational materials design by integrating CALPHAD, Density Functional Theory (DFT), Machine Learning Interatomic Potentials (ML... -
Citrine Platform and AI Significantly Reduce Graphite Anode Failure Rate via Iterative Experimental Feedback
arXiv Unknown Overview This research demonstrates that the Citrine Platform, utilizing an AI-guided iterative closed-loop workflow, successfully achieved the design and optimization of graphite-based anode formulations, leading to a sign...
