Deep Learning– tag –
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arXiv Paper: Model-Agnostic Graph Prompt Learning Significantly Enhances GNN Crystal Property Prediction Accuracy
arXiv International Overview This paper proposes a novel model-agnostic soft prompt learning framework to improve the crystal property prediction performance of Graph Neural Networks (GNNs). By incorporating node-level and graph-level so... -
arXiv Paper: Equivariant Graph Neural Networks Revolutionize Atomic Modeling and Advance Molecular Coarse-Graining
arXiv International Overview This paper discusses how machine-learning interatomic potentials (MLIPs), including equivariant graph neural networks (EGNNs), have transformed atomic modeling by learning potential energy surfaces from quant... -
Springer Nature Launches Call for Papers on ML Methods for Crystalline Defects, Emphasizing Integration with Atomic Simulations
Research Communities (Springer Nature) International Overview Research Communities by Springer Nature has initiated a call for papers focusing on machine learning (ML) methods for modeling and predicting crystalline defects. The call enc... -
Oxford Academic Paper: DeepMeso (DeepFerro) Enables Rational Multi-Scale Design for Ferroelectrics
Oxford Academic International Overview The DeepMeso framework, specifically DeepFerro for ferroelectric materials, has been introduced to address existing challenges in the rational design of mesoscopic heterogeneous materials. This inno... -
LAMMPS Integrates Diverse Machine Learning Potentials Like MACE and CHGNet, Enhancing Atomic Simulation Versatility
LAMMPS Molecular Dynamics Simulator Unknown Overview The LAMMPS molecular dynamics simulator has significantly enhanced its interoperability with leading machine learning potential (MLP) frameworks, including MACE, CHGNet, DeePMD-kit, Ne... -
Comprehensive Review Details Machine Learning’s Role in Materials Science, From GNNs to LLMs for Data-Driven Discovery
Nature Computational Materials International Overview A new review paper offers a comprehensive analysis of machine learning's advancements in data-driven discovery and functional applications within materials science. Key technologies l... -
U.S. DOE Accelerates Inverse Materials Design with Physics-Informed AI, Dramatically Shortening Development Time
Department of Energy USA Overview The U.S. Department of Energy (DOE) has launched the 'Genesis Mission,' a physics-informed AI framework aimed at dramatically accelerating materials innovation and reducing time-to-market. This closed-lo... -
Argonne National Laboratory Unveils AI-Driven ‘ChemGraph’ Framework, Accelerating Materials Research with LLMs
Argonne National Laboratory USA Overview Argonne National Laboratory has announced 'ChemGraph,' an open-source, AI-driven framework that automates the setup, execution, and analysis of computational chemistry and materials science simula... -
Machine Learning Penetrates Materials Science, Accelerating Autonomous Discovery with LLMs
Google Cloud Vertex AI Search USA Overview A comprehensive review highlights the transformative impact of machine learning, especially Graph Neural Networks, ML Interatomic Potentials, and Large Language Models (LLMs), on materials scien... -
Bosch Escalates AI Ambitions, Forges Decentralized Web3/AI Ecosystem with Fetch.ai
Bosch Germany Overview The Bosch Center for Artificial Intelligence (BCAI) is significantly escalating its AI initiatives across manufacturing, autonomous driving, and engineering, aiming to bridge the gap between scientific research and...
