Simulation– tag –
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Data-Driven Atomistic Modeling Unravels Surface Passivation Mechanism of Hybrid Halide Perovskites
arXiv Unknown Overview Molecular passivation of surface defects is critical for enhancing the optoelectronic performance of hybrid halide perovskite materials, yet its atomistic mechanism has remained incompletely understood. This study ... -
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... -
U.S. DOE Propels AI Innovation Ecosystem, Empowering Research with Autonomous Laboratories
Department of Energy USA Overview The U.S. Department of Energy (DOE) is fostering a world-leading AI innovation ecosystem by integrating AI across high-performance computing, environmental modeling, and materials research. This includes... -
AI Workflow Unites Foundation Models and Bayesian Optimization for Cost-Efficient Materials Discovery
arXiv USA Overview A new preprint introduces a novel Bayesian materials design workflow that integrates surrogate-gated generation with foundation model embeddings. This approach, leveraging pre-trained ORB embeddings and Gaussian proces... -
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... -
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... -
Chalmers Pushes Automated Machine Learning for Breakthroughs in Computational Chemistry and Materials Science
Chalmers University of Technology (AIMLeNS) スウェーデン Overview A recent AI4Science seminar at Chalmers University of Technology underscored the transformative impact of machine learning (ML) on computational chemistry and materials re... -
IBM Quantum Promotes Next-Gen Quantum Algorithm Development for Complex Materials via Quantum Credits Program
IBM Quantum USA Overview IBM Quantum is actively driving advancements in quantum simulations for complex materials, especially those with many-body interactions, through its Quantum Credits program. This initiative supports researchers d... -
Frontiers Launches Research Topic on “High-Throughput AI-Driven Materials Discovery for High-Rate Batteries,” Accelerating Next-Gen Energy Storage
Frontiers Switzerland Overview Frontiers has initiated a new research topic, "High-Throughput AI-Driven Materials Discovery and Design for High-Rate Batteries," aiming to revolutionize battery material development through machine learnin... -
Digital Twin Technology Transforms Urban Development and Manufacturing, Demonstrated by Performance Monitoring and Prediction at NASA MAF
Axiom USA Overview Digital twin technology is revolutionizing diverse sectors from urban development to manufacturing, with dynamic digital replicas of physical assets linked to real-time data for performance monitoring, behavior predict...
