Deep Learning– tag –
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AI Accelerates Material Discovery: Novel GNN Model Generates Electronic Fingerprints for High-Throughput Catalyst & Battery Research
Facebook USA Overview A novel Graph Neural Network (GNN) model has been developed to rapidly generate electronic fingerprints, significantly accelerating the discovery of new materials for catalysts and batteries at a fraction of previou... -
DeepH-pack Unites Ab Initio Calculations and Deep Learning to Accelerate AI-Driven Electronic Structure Modeling
Facebook (reposting about DeepH-pack) USA Overview DeepH-pack has been introduced as a general-purpose neural network package that combines ab initio calculations with deep learning to accelerate electronic structure modeling. This tool ... -
Periodic Labs, an AI-Driven Materials Science Startup, Founded by Former OpenAI and Google DeepMind Researchers
Facebook (reposting about Periodic Labs) USA Overview Periodic Labs, a venture-backed startup, was founded in 2025 by former senior researchers from OpenAI and Google DeepMind. The company aims to accelerate scientific discovery, particu... -
Meta AI Releases OMat24, a Massive Inorganic Materials Dataset with Over 110 Million DFT Calculations, Alongside High-Performance EquiformerV2 GNN Model
Meta Fundamental AI Research (FAIR) USA Overview Meta Fundamental AI Research (FAIR) has unveiled Open Materials 2024 (OMat24), a monumental inorganic materials dataset comprising over 110 million DFT calculations, positioning it as one ... -
Graph Neural Network Accelerates Novel Material Discovery with Reduced Computational Cost for Catalysts and Batteries
PRX Intelligence USA Overview A new Graph Neural Network (GNN) model published in PRX Intelligence significantly accelerates novel material prediction and discovery while drastically lowering computational costs. The model leverages proj... -
Physics-Informed Pairwise Charge Transfer GGNN Framework Achieves Fast, Universal Prediction of Dynamic Properties Under Electric Fields
ACS Publications USA Overview An ACS Publications paper reports a novel framework combining physics-informed Pairwise Charge Transfer (PQT) theory with a Generalized Global Neural Network (GGNN) for rapid and accurate prediction of dynam... -
AI Accelerates Novel Material Discovery: Machine Learning and Graph Neural Networks Drive Solar Energy and Catalyst Development
Future Science AI USA Overview AI, particularly machine learning models and graph neural networks (GNNs), is dramatically accelerating novel material discovery by identifying complex relationships between material composition, structure,... -
U.S. DOE Accelerates Materials Inverse Design with Physics-Aware AI Framework, Dramatically Reducing Time-to-Market
Department of Energy USA Overview The U.S. Department of Energy (DOE) announced a strategic initiative to accelerate materials inverse design by integrating a physics-aware AI framework encompassing foundation models, deep learning, gene... -
ACS Publications: Deep Learning Foundation Models from Classical Molecular Descriptors Accelerate Molecule Discovery in Low-Data Regimes
ACS Publications USA Overview A research paper in ACS Publications explores deep learning foundation models for low-data regimes derived from classical molecular descriptors. By pre-training models to predict these descriptors, they inte... -
EE Times: AI Adoption in Materials R&D Hinges More on People Than Technology, Toyota Example Highlights Accelerated Development
EE Times USA Overview EE Times argues that AI adoption in materials R&D increasingly depends on people and organizational factors rather than technological capabilities, as neural network potentials now offer DFT-comparable accuracy ...
