August 2026– date –
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New Ontology Proposed to Standardize Machine Learning Interatomic Potentials (MLIPs), Boosting Reproducibility and Comparability
arXiv Overview An arXiv preprint introduces the 'MLIPs ontology,' an OWL 2 DL framework designed to systematically describe Machine Learning Interatomic Potentials (MLIPs), their hyperparameters, and training/benchmarking data. By standa... -
South China University of Technology and Xi’an Jiaotong University Introduce ‘HULU’ to Accelerate Clean Energy Material Discovery
EurekAlert! China Overview Researchers from South China University of Technology and Xi'an Jiaotong University have developed 'HULU,' a flexible Python framework integrating advanced Machine Learning Potentials (MLPs) with Monte Carlo ad... -
University at Buffalo Secures DOE Genesis Grants to Accelerate AI and Quantum Technology Research in Carbon-Based Fuel Catalysts and Quantum Materials
University at Buffalo (UB) RENEW (Department of Energy Genesis Mission grants) USA Overview University at Buffalo (UB) researchers secured three DOE Genesis mission grants focused on AI and quantum technology. One project, "CLEAR-AI," is... -
Integrated Modeling Framework Proves Effective for Liquid Electrolyte Design, Bioengineer.org Publishes Reusability Report
Bioengineer.org Overview A reusability report by Bioengineer.org evaluates a unified machine learning framework for liquid electrolyte formulation design. This framework integrates molecular structure representation and composition-level... -
Forbes Reports LAM Research and Applied Materials Enhance Semiconductor Manufacturing Productivity by up to 35x with AI Integration; Synopsys, Siemens, and NVIDIA Strengthen Collaboration
Forbes USA Overview Forbes highlights how leading semiconductor firms like LAM Research and Applied Materials integrate AI to boost design and manufacturing productivity. LAM's "Semiverse" and Applied Materials' "Ai^x" leverage AI and di... -
AI-Powered Simulations Unlock Next-Gen Battery Design: How MLIPs Bridge the Gap Between DFT and Classical MD
ResearchGate Overview A recent review highlights how Machine Learning Interatomic Potentials (MLIPs) are effectively addressing persistent challenges in computational materials science for battery development. By bridging the gap between... -
National University of Singapore Develops AI Method to Learn Macroscopic Material Behavior from Microscopic Data, Accelerating Discovery
National University of Singapore (NUS) Faculty of Science Singapore Overview Researchers at the National University of Singapore developed an innovative AI method that learns complex macroscopic material behaviors from microscopic data. ... -
Tokyo University of Science Unveils AI-Driven Inverse Design for High-Speed Spin Wave Computing
EurekAlert! Japan Overview Researchers at Tokyo University of Science have developed an AI-driven inverse design framework, integrating genetic algorithms with micromagnetic simulations, to optimize magnonic crystal (MC) structures. This... -
Northwestern University Accelerates Electronic and Energy Material Design with Property-Predicting Machine Learning Model ‘LVGP’
Northwestern University (Paula M. Trienens Institute For Sustainability And Energy) USA Overview Northwestern University's Wei Chen research group developed the Latent Variable Gaussian Process (LVGP) machine learning model, which conver... -
Computational Powerhouse: AI and DFT Drive Next-Gen Sodium-Ion Battery Innovation
MDPI Overview A new MDPI review highlights the critical role of theoretical calculations, including Density Functional Theory (DFT) and Molecular Dynamics (MD), alongside Machine Learning (ML), in accelerating the discovery and optimizat...
