superlouis / GATGNNLinks
Pytorch Repository for our work: Graph convolutional neural networks with global attention for improved materials property prediction
☆82Updated 8 months ago
Alternatives and similar repositories for GATGNN
Users that are interested in GATGNN are comparing it to the libraries listed below
Sorting:
- Scalable graph neural networks for materials property prediction☆62Updated last year
- NeurIPS 2018 MLMM Workshop: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Predicti…☆66Updated last year
- Crystal graph convolutional neural networks for predicting material properties.☆34Updated 2 years ago
- Crystal Graph Neural Networks☆108Updated last year
- Composition-Conditioned Crystal GAN pytorch code☆43Updated 3 years ago
- ☆33Updated last year
- MatDeepLearn, package for graph neural networks in materials chemistry☆197Updated 2 years ago
- Predict materials properties using only the composition information!☆108Updated 2 years ago
- ☆28Updated 3 years ago
- Unsupervised learning of atomic scale dynamics from molecular dynamics.☆81Updated 3 years ago
- Representation Learning from Stoichiometry☆58Updated 2 years ago
- 3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning (JCIM 2020)☆41Updated 2 years ago
- Learning to Discover Crystallographic Structures with Generative Adversarial Networks☆38Updated 5 years ago
- Reference implementation of "SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects"☆78Updated 3 years ago
- data and code to reduplicate paper: Topological representations of crystalline compounds for the machine-learning prediction of materials…☆21Updated 4 years ago
- image-based generative model for inverse design of solid state materials☆40Updated 3 years ago
- ☆32Updated 3 years ago
- A graph neural network for the prediction of bond dissociation energies for molecules of any charge.☆63Updated 2 years ago
- Crystal Edge Graph Attention Neural Network☆22Updated last year
- A DGL implementation of "Directional Message Passing for Molecular Graphs" (ICLR 2020).☆21Updated last year
- This is a simple but efficient implementation of PaiNN-model for constructing machine learning interatomic potentials☆21Updated 2 years ago
- Zeolite GAN☆22Updated 5 years ago
- Molecular graph deep sets learning for mixture property modeling.☆23Updated 7 months ago
- Generate and predict molecular electron densities with Euclidean Neural Networks☆48Updated last year
- DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules☆23Updated 7 months ago
- ☆26Updated last year
- Workflow for creating and analyzing the Open Catalyst Dataset☆111Updated 6 months ago
- Source code for generating materials with 20 space groups using PGCGM☆33Updated 2 years ago
- FTCP code☆35Updated last year
- Heterogeneous relational message passing networks (HermNet)☆15Updated 2 years ago