Graph Neural Network– tag –
-
Formal Theory for Crystal Structure Prediction Revolutionizes Inverse Design and Material Property Prediction
arXiv USA Overview A groundbreaking formal theory for crystal structure prediction has been introduced, detailing the inverse design of crystal structures using Constrained Crystal Deep Convolutional Generative Adversarial Networks (CG-D... -
Google DeepMind’s GNoME Discovers 2.2 Million New Crystal Structures, Propelling Clean Energy Material Design into a New Era
DEV Community 多国籍 Overview Google DeepMind's GNoME AI has identified an unprecedented 2.2 million new stable crystal structures, including 380,000 deemed practical, surpassing all previously known inorganic materials. This monumental ... -
bioRxiv: ORIGAMI, an Orientation-Aware GNN, Developed for Assessing Multimeric Interfaces of Protein Complex Structures
bioRxiv Unknown Overview A study published on bioRxiv introduces "ORIGAMI," an orientation-aware graph neural network (GNN) for evaluating multimeric interfaces of protein complex structures. ORIGAMI innovatively utilizes both scalar and... -
arXiv: PolyGraphPy Unifies Atomistic Simulation and ML-Driven Polymer Design in a Python Framework
arXiv Unknown Overview A new paper on arXiv introduces "PolyGraphPy," a unified Python framework for atomistic simulation and machine learning (ML)-driven polymer design. This open-source framework seamlessly integrates atomistic simulat... -
ResearchGate: NequIP GNN Predicts Amorphous Material Many-Body Interactions at 10,000x Lower Cost than DFT
ResearchGate Unknown Overview Recent research applied NequIP, an equivariant message passing graph neural network (GNN), to predict many-body interactions in model soft glasses of solvent-free polymer-grafted nanoparticles (PGNs). NequIP... -
arXiv: BiMat-ML Advances Stacked 2D Material Property Prediction via Multimodal Learning and GNNs
arXiv Unknown Overview A new research paper on arXiv proposes "BiMat-ML," a multimodal learning approach for property prediction in stacked two-dimensional (2D) materials. This method utilizes graph neural networks (GNNs) to process mole... -
AI and Graph Neural Networks Drive a Materials Revolution: Gulf University on Property Prediction
Gulf University バーレーン Overview Gulf University research highlights AI's profound impact on materials science, particularly through Graph Neural Networks (GNNs). These models predict material properties directly from atomic structure... -
Google DeepMind’s GNoME Predicts Over 2 Million New Crystal Structures, Revolutionizing Chemical Engineering with AI and Autonomous Labs
Medium USA Overview Google DeepMind's GNoME project, utilizing graph neural networks (GNNs), has predicted over 2 million new stable crystal structures, surpassing the total known material catalog accumulated over the past century. This ...
