Machine Learning– tag –
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Tokyo University of Science Discovers New Class of Ferromagnetic Quasicrystals with Machine Learning, AI Accelerates Magnetic Material Development
Digital Journal (Tokyo University of Science) Japan Overview A research team at Tokyo University of Science has successfully developed a new class of ferromagnetic quasicrystals by applying machine learning. This AI-guided approach enabl... -
AI-Integrated Advanced Materials Manufacturing Accelerates Innovation in Energy Storage, Catalysis, and Environmental Applications
Frontiers (Journal of Energy Research & Reviews) International Overview AI-integrated manufacturing of advanced materials is reported to be bringing revolutionary changes to energy storage, catalysis, and environmental applications. Mach... -
MLIP Studio Launches on arXiv, Integrating Over 60 MLIPs to Dramatically Reduce Computational Costs for Atomistic Simulations
arXiv International Overview MLIP Studio, an open-source platform, has been announced on arXiv, integrating over 60 universal Machine Learning Interatomic Potentials (MLIPs) to facilitate atomistic simulations and benchmarking. This plat... -
Royal Society of Chemistry Integrates MLIPs and Classical Force Fields for MOF Adsorption Screening, Achieving DFT Accuracy at Low Cost
The Royal Society of Chemistry (Chemical Science) International Overview An efficient hybrid screening workflow integrating classical force fields with universal machine learning interatomic potentials (u-MLIPs) has been introduced for m... -
Explainable ML Predicts Bulk Modulus of High-Entropy Alloys, Accelerating Next-Gen Materials Design
MDPI (Metals) International Overview A novel framework employing an explainable machine learning (ML) system has been introduced to predict the bulk modulus of high-entropy alloys (HEAs) with high accuracy. The Extra Trees algorithm demo... -
Aalto University Unlocks Two New Superconductors with AI-Quantum Fusion, Dramatically Accelerating the Quest for Room-Temperature Materials
ScienceDaily (Aalto University) Finland Overview Researchers at Finland's Aalto University have pioneered a novel methodology, seamlessly integrating machine learning with quantum physics, to rapidly identify new room-temperature superco... -
Machine Learning Penetrates Materials Science, Accelerating Autonomous Discovery with LLMs
Google Cloud Vertex AI Search USA Overview A comprehensive review highlights the transformative impact of machine learning, especially Graph Neural Networks, ML Interatomic Potentials, and Large Language Models (LLMs), on materials scien... -
Hybrid Mechanistic–Machine Learning PK/PD Models Leverage Digital Biomarkers and Remote Monitoring for Deeper Insights from Preclinical to Clinic
Frontiers Switzerland Overview This review highlights the rapid growth of digital biomarkers and remote monitoring technologies, enabling continuous measurement of physiological and behavioral endpoints. It covers both preclinical digita... -
Nanozyme Aptasensors: A Breakthrough for Rapid, High-Sensitivity Detection
AZoNano Australia Overview Nanozyme aptasensors, which ingeniously combine nanozymes and aptamers, represent a highly promising technological advancement for rapid and exceptionally sensitive detection across critical fields like clinica... -
Converging Technologies Drive Digital Biomarker Development in Biotech and Pharma: Wearables, AI, and Telemedicine Integration
Infinix Bio USA Overview The biotechnology and pharmaceutical industries are accelerating digital biomarker development through the integration of wearable devices, mobile health apps, AI/machine learning algorithms, and telehealth platf...
