Simulation– tag –
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AI Unravels Interphase Design Mechanisms to Boost Lithium-ion Battery Performance in LLNL Study
Mirage News USA Overview A Lawrence Livermore National Laboratory (LLNL) study has leveraged AI to elucidate the design mechanisms of the ultrathin interphase layer critical for lithium-ion battery performance and durability. By training... -
Physics-Informed Graph Neural Network Unlocks Heterogeneous Solid Mechanics: Modeling Deformation and Crack Propagation Without Extensive Data
arXiv Unknown Overview A novel Physics-Informed Graph Neural Network (PI-GNN) has been developed, successfully modeling deformation and crack propagation in heterogeneous solid mechanics with high accuracy. Operating on conforming mesh g... -
Matlantis and NVIDIA ALCHEMI Cut ENEOS Catalyst Discovery from Years to Months, Screening 100 Million Structures
AiThority Unknown Overview Matlantis announced that the combination of NVIDIA ALCHEMI and its Matlantis PFP (universal machine learning interatomic potential) significantly accelerated catalyst materials discovery for ENEOS Holdings. Thi... -
Columbia & Cambridge Establish Quantum AI Benchmark to Combat ‘Hallucination’ in Material Models, Enhancing Prediction Reliability
Quantum Zeitgeist USA Overview Researchers from Columbia and Cambridge Universities have established a new benchmark for evaluating machine learning models that predict material properties. This benchmark addresses concerns about models ... -
AI and 3D Printing Revolutionize Battery Electrode Design, Accelerating Efficiency and Safety Improvements
Unknown source Unknown Overview The integration of AI and 3D printing is revolutionizing battery electrode design, significantly enhancing both efficiency and safety. AI accelerates the discovery of new electrolytes and cathode materials... -
Generative AI Revolutionizes Engineering Design: Autonomously Creating Optimized Novel Solutions and Reducing Development Cycles
Not explicitly stated (appears to be a guide/blog) Overview Generative AI is fundamentally transforming engineering design by autonomously creating optimized designs based on high-level specifications. Unlike traditional CAD, generative ... -
Machine Learning Potentials Grapple with Long-Range Interactions in Atmospheric Modeling: A DTU Deep Dive
DTU Research Database Denmark Overview Researchers at the Technical University of Denmark (DTU) evaluated AIMNet2 and PaiNN, two machine-learned interatomic potentials (MLIPs), for modeling molecular collisions critical to atmospheric cl... -
Hessian-Based Molecular Conformation Augmentation Improves Scalability and Efficiency of Machine Learning Interatomic Potentials
arXiv (Preprint Server) International Overview This preprint proposes two Hessian-derived data augmentation schemes, isotropic Gaussian displacement (UniAug) and normal mode-weighted displacement (ModeAug), for machine-learning interatom... -
Faster Quantum Simulation Via Randomization Boosts Scalability & Precision for Markovian Open Quantum Systems
Quantum (Scientific Journal) International Overview Researchers have introduced novel non-probabilistic algorithms for simulating Markovian open quantum systems using randomization, aiming for faster and more accurate quantum simulations... -
Equivariant Graph Neural Network Interatomic Potentials Accelerate Nanomaterials Simulation by 100x
nano-matter.com International Overview Equivariant Graph Neural Network (GNN) interatomic potentials are accelerating nanomaterials research by predicting atomic forces and energies with DFT-comparable accuracy, while reducing computatio...
