Machine Learning– tag –
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MARVEL Project Marks 12 Years: From Quantum Mechanics to AI-Driven Materials Discovery, Reshaping Computational Science
EurekAlert! USA Overview The MARVEL project, celebrating its 12th anniversary, has fundamentally transformed materials discovery by integrating quantum mechanical simulations, advanced computing, and machine learning. Since its 2014 ince... -
Materials Informatics and ML Clustering Accelerate Data-Driven Discovery of Calcium Phosphate Biomaterial Scaffolds
Indonesian Applied Physics Letters (e-Journal UNAIR) Indiaネシア Overview A recent study published in Indonesian Applied Physics Letters demonstrates a novel data-driven approach for discovering calcium phosphate biomaterial scaffolds, i... -
Interpretable 2D Material Design: Dynamic Collision Fingerprint Outperforms Traditional Descriptors
ACS Omega USA Overview A recent comparative study published in ACS Omega investigates various structural representations for 2D materials, highlighting the promising potential of the Dynamic Collision Fingerprint (DCF). The research addr... -
AI-Driven Resilience: Citrine Informatics Fortifies Specialty Chemical Supply Chains Against Disruption
AZoM (Citrine Informatics) USA Overview Citrine Informatics is deploying AI to significantly enhance resilience in specialty chemical manufacturing, offering innovative solutions to mitigate supply chain disruption risks. This AI technol... -
Explainable AI Unlocks Transparent Catalyst Design for Sustainable Technologies
ACS Applied Materials & Interfaces (ACS Publications) USA Overview A new review in ACS Applied Materials & Interfaces highlights the transformative potential of Explainable AI (XAI) in electrocatalysis and photocatalysis. The paper e... -
Notre Dame Leverages Data-Centric AI and Foundation Models to Accelerate Gas Separation Polymer Membrane Discovery
University of Notre Dame (Curate ND) USA Overview A Ph.D. dissertation from the University of Notre Dame introduces a novel framework leveraging data-centric machine learning and foundation models to significantly accelerate molecular di... -
AI to Accelerate Energy Materials Discovery: NUS Announces 2026 Workshop
National University of Singapore (NUS) Singapore Overview The National University of Singapore (NUS) will host an "AI for Energy Materials" workshop on July 10, 2026, co-located with the Solid State Ionics Conference 2026. Bringing toget... -
Nested Graph Neural Network Revolutionizes Property Prediction for Complex Solid Solutions
Edinburgh Research Explorer UK Overview Researchers at the University of Edinburgh have developed the Solid Solution Nested Graph Neural Network (SSNGNN), a novel AI framework that significantly boosts the predictive accuracy for chemica... -
Tohoku University Launches AI-Driven ‘DigCat 4.0’ Platform to Revolutionize Catalyst Discovery
東北大学 Japan Overview Tohoku University researchers have unveiled 'DigCat 4.0,' an AI-powered digital catalysis platform designed to significantly accelerate catalyst discovery and development. By integrating experimental data, theoret... -
Accelerating Materials Discovery: Ensemble Uncertainty Quantification Enhances Neural Network Interatomic Potentials
arXiv USA Overview A recent comparative study rigorously evaluates ensemble-based uncertainty quantification (UQ) methods for Neural Network Interatomic Potentials (NNIPs), aiming to develop robust machine learning interatomic potentials...
