Deep Learning– tag –
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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... -
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 ... -
arXiv Releases High-Information Dataset ‘MAD-1.6’ for Universal Atomistic Machine Learning, Boosting Predictive Accuracy
arXiv USA Overview The high-quality, high-information dataset 'MAD-1.6' has been released on arXiv to accelerate universal atomistic machine learning. Comprising 362,646 atomic structures across 102 chemical elements, it covers diverse m... -
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... -
GRACE-OFF Achieves High-Precision Machine-Learned Interatomic Potentials for Organic Liquids via GRACE Architecture
Journal of Chemical Theory and Computation | ACS Publications USA Overview This study introduces GRACE-OFF, a machine-learned interatomic potential (MLIP) built on the Graph Atomic Cluster Expansion (GRACE) neural network architecture, d... -
Royal Society of Chemistry Review: AI Accelerates Nanomaterial Design, Synthesis, and Characterization, Boosting Synthesis Efficiency with Closed-Loop Experimentation
The Royal Society of Chemistry UK Overview A Royal Society of Chemistry review highlights how AI integration is revolutionizing nanomaterial science, accelerating discovery, design, synthesis, and characterization. Machine learning, deep... -
Unveiling Physical Meaning in Materials AI: Beyond Prediction to Mechanism Identification and Design Guidance
ACS Materials Au USA Overview This perspective paper explores how materials AI can move beyond mere property prediction to contribute to physical interpretation, mechanism identification, and design guidance. It advocates for evaluating ... -
Machine Learning Revolutionizes Magnetic Functional Alloy Design: Generative GNNs and High-Throughput DFT Discover 2D Magnets
AIP Advances USA Overview This article details recent advancements in the design of magnetic functional alloys (MFAs) leveraging machine learning (ML), highlighting how ML has contributed to the design of magnetocaloric, magnetostrictive... -
Ansys: Inverse Design Revolutionizes Materials R&D, Automating Design from Desired Performance to Drastically Cut Time and Cost
Ansys USA Overview Ansys details how inverse design, a computational approach, reverses traditional trial-and-error, automating material design from desired specifications. Powered by machine learning and deep learning optimization, this... -
Cypris AI: Generative Models, GNNs, and Autonomous Labs Slash Materials R&D to 1-2 Years; Google DeepMind’s GNoME Predicts 2.4 Million New Materials
Cypris AI USA Overview Cypris AI reports that the integration of generative models, Graph Neural Networks (GNNs), and autonomous labs is drastically cutting materials R&D timelines from 10-20 years to just 1-2 years. Enhanced GNN arc...
