Machine Learning– tag –
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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... -
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... -
EurekAlert! Review: AI Reshapes Chemical Engineering, Accelerating R&D from Reaction Design to Smart Manufacturing
EurekAlert! USA Overview A review published on EurekAlert! highlights the fundamental transformation artificial intelligence (AI) is bringing to chemical engineering. AI is significantly accelerating R&D cycles through diverse applic... -
GPUMD 4.0 from Chalmers University Unleashes Machine Learning Power for Advanced Materials Simulations
Computational Materials Group @ Chalmers Sweden Overview Chalmers University of Technology's Computational Materials Group has released GPUMD 4.0, a high-performance molecular dynamics (MD) software that integrates cutting-edge machine l... -
Springer Nature Launches Call for Papers on ML Methods for Crystalline Defects, Emphasizing Integration with Atomic Simulations
Research Communities (Springer Nature) International Overview Research Communities by Springer Nature has initiated a call for papers focusing on machine learning (ML) methods for modeling and predicting crystalline defects. The call enc... -
LAMMPS Integrates Diverse Machine Learning Potentials Like MACE and CHGNet, Enhancing Atomic Simulation Versatility
LAMMPS Molecular Dynamics Simulator Unknown Overview The LAMMPS molecular dynamics simulator has significantly enhanced its interoperability with leading machine learning potential (MLP) frameworks, including MACE, CHGNet, DeePMD-kit, Ne... -
Comprehensive Review Details Machine Learning’s Role in Materials Science, From GNNs to LLMs for Data-Driven Discovery
Nature Computational Materials International Overview A new review paper offers a comprehensive analysis of machine learning's advancements in data-driven discovery and functional applications within materials science. Key technologies l... -
Advances and Challenges in Wearable Sensors: Non-Invasive Monitoring and AI Integration Revolutionize Health Management
ACS Publications USA Overview This mega-review explores the state-of-the-art in wearable (bio)sensors for health monitoring, emphasizing the shift towards continuous, non- or minimally invasive measurements of bodily fluids like sweat, s... -
Hanbat National University Study Advances Machine Learning Calibration of Biosensors for Microcystin Toxin Monitoring in Freshwater, Enhancing On-Site Accuracy
PR Newswire (Hanbat National University) South Korea Overview Researchers from Hanbat National University and the University of Central Florida developed a machine learning framework to improve the calibration accuracy of portable screen...
