Graph Neural Network– tag –
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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... -
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,... -
ChemRxiv Unveils ‘CataCon’: A Contrastive Graph Representation Learning Framework for Catalyst Prediction
ChemRxiv International Overview A paper published on ChemRxiv introduces 'CataCon,' a novel contrastive graph representation learning framework for catalyst prediction. Utilizing GraphSAGE, CataCon generates robust molecular graph embedd... -
Scilight Press Publishes Review on AI-Driven Rational Design of Solid-State Electrolytes: Paving the Way for Next-Gen Batteries
Scilight Press Unknown Overview Scilight Press published a review paper on AI's role in advancing the rational design of solid-state electrolytes (SSEs) for high energy density and safety in next-generation rechargeable batteries. The pa... -
CataCon Introduces Contrastive Graph Representation Learning for Catalyst Prediction, Accelerating Optimal Catalyst Identification via AI
PMC USA Overview This research introduces 'CataCon,' a novel contrastive graph representation learning framework designed to predict optimal catalysts for chemical reactions. CataCon generates rich structural embeddings for all reaction ... -
Energy-Constrained MLIP Embedding Improves Prediction Accuracy for Hydrogen Evolution Electrocatalysts
ACS Publications (Journal of Chemical Information and Modeling) Overview Researchers developed an "energy-constrained" machine learning framework integrating MLIP-derived energy descriptors with pre-trained Crystal Hamiltonian Graph Neur... -
Machine Learning Interatomic Potentials: Foundation Models Reshape Materials Discovery
Review of Peer-Reviewed Articles Overview A new comprehensive review critically examines Machine Learning Interatomic Potentials (MLIPs), a transformative technology bridging the accuracy of quantum mechanics with the efficiency of class... -
Generative AI System (GSDS) Accelerates De Novo Solvent Design for Alkali Metal Batteries
ACS Nano USA Overview A new Generative Solvent Design System (GSDS) has been introduced for de novo solvent design in rechargeable batteries, integrating a graph-based deep molecular generator with machine learning property predictors. T... -
arXiv: New ML Potential ‘TWIN’ Achieves Ab Initio Accuracy and Transferability in Biomolecular Simulations
arXiv Global Overview A new arXiv preprint introduces TWIN (Transferable Water Implicit Network), a highly transferable implicit solvent machine-learning potential (MLP) for biomolecular systems. TWIN, trained solely on ab initio and exp...
