Simulation– tag –
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Quantum Machine Learning Interatomic Potential Achieves Enhanced Molecular Energy Prediction with Variational Quantum Algorithm
arXiv USA Overview This study applied quantum circuit learning to machine learning interatomic potentials (MLIPs), improving molecular dataset energy predictions. Using a quantum transfer learning architecture, the ANI model was retraine... -
SCAPS-1D Simulation Analyzes Lead-Free Double Perovskite Solar Cells, Highlighting Efficiency Improvement Challenges
arXiv (preprint) Unknown Overview An arXiv preprint outlines performance analysis of lead-free double perovskite solar cells using SCAPS-1D simulation. While lead-free perovskites offer low toxicity, stability, and high photoelectric pot... -
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... -
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... -
Atomicrex: Open-Source Platform Unlocks Large-Scale Atomic Interaction Models for Faster Materials Discovery
atomicrex Overview A new open-source code, 'atomicrex,' is poised to accelerate computational materials science by dramatically simplifying the construction of interatomic potentials for simulations involving thousands of atoms or more. ... -
Machine Learning Interatomic Potentials: Foundation Models Reshape Materials Discovery
Review of Peer-Reviewed Articles Overview A new comprehensive review critically examines Machine Learning Interatomic Potentials (MLIPs), a transformative technology bridging the accuracy of quantum mechanics with the efficiency of class... -
NVIDIA and Applied Materials Propel Semiconductor Innovation with GPU-Accelerated AI, Achieving Up to 55x Speedup
NVIDIA Technical Blog USA Overview NVIDIA and Applied Materials have announced a strategic partnership to revolutionize semiconductor innovation through the integration of AI and GPU-accelerated simulations. This collaboration leverages ...
