Simulation– tag –
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OMol25-Trained MLIPs Predict Na-Ion Battery Electrolyte Solvation Structures with DFT-Level Accuracy, Outperforming Inorganic-Only Models
ACS Publications (Journal of Physical Chemistry Letters) Overview This study demonstrates that machine learning interatomic potentials (MLIPs) trained with the Open Molecules 2025 (OMol25) dataset accurately predict and experimentally ve... -
Generative AI Accelerates Custom Combustion Profile Design for 3D High-Energy Materials by Up to 95%
AZoM (Communications Engineering) Overview Researchers developed a hybrid generative AI framework for inverse-designing 3D high-energy material granular structures matching user-defined pressure-time combustion profiles. This framework i... -
Iterative Fine-Tuning Strategy for Universal Machine Learning Interatomic Potentials (uMLIPs) Yields Stable, High-Accuracy Models for Out-of-Domain Tasks
PubMed Overview Research on universal machine learning interatomic potentials (uMLIPs) demonstrates that "iterative fine-tuning" effectively generates stable molecular dynamics simulations and high-accuracy models for out-of-domain tasks... -
Penn State Scientists Lead Three Genesis Mission Projects with AI to Accelerate 2D Material Manufacturing & Catalytic Performance Prediction
Penn State USA Overview Penn State scientists have received Phase 1 funding from the U.S. Department of Energy's (DOE) Genesis Mission to lead three AI-powered projects. These include accelerating 2D material manufacturing for future ele... -
Unlocking Precision: Iterative Fine-Tuning Overcomes Bias in Universal MLIPs for Enhanced Material Simulations
Unknown Source Unknown Overview Universal Machine Learning Interatomic Potentials (uMLIPs) promise broad applicability across the periodic table, yet accurate out-of-domain predictions demand specialized fine-tuning. This study reveals t... -
AI’s Three Pillars: Accelerating Materials Discovery with Predictive Models, Generative Design, and Machine Learning Interatomic Potentials
Source Unknown USA Overview Artificial intelligence is fundamentally transforming materials science through three key advancements: highly accurate property prediction, autonomous generative design, and revolutionary machine learning int... -
Transpire Insight Forecasts $13.4B Metal Bonding Adhesives Market by 2035; Henkel Accelerates EV Battery Design with AI-Powered Loctite Solve
Transpire Insight USA Overview Transpire Insight projects the metal bonding adhesives market to grow from $8.8 billion in 2026 to $13.4 billion by 2035, driven by lightweighting in automotive and aerospace, and rapid expansion in EV batt... -
SUTD and NUS Expand Light-Matter Interaction Simulation Tool, Accelerating Next-Gen Photonic and Quantum Device Design
EurekAlert! Singapore Overview Researchers from Singapore University of Technology and Design (SUTD) and National University of Singapore (NUS) developed a new computational approach to design photonic and semiconductor devices that cont... -
German Researchers Develop Quantum Computer Framework for Quantum Technology Design, Simulating Many-Body Electron Spin Resonance Hamiltonians
arXiv Germany Overview German researchers have developed a framework for designing quantum technologies using a quantum computer. This framework is engineered to simulate general many-body electron spin resonance (ESR) Hamiltonians, inco... -
Flatiron Institute Researchers Solve Quantum-Computer-Level Quantum Physics Problem on Laptop with Tensor Networks and Classical Computing
ScienceDaily USA Overview Researchers at the Simons Foundation's Flatiron Institute have solved a complex quantum physics problem, previously thought to require a quantum computer, using an ordinary laptop, advanced mathematics, and spec...
