superlouis / GATGNN
Pytorch Repository for our work: Graph convolutional neural networks with global attention for improved materials property prediction
☆78Updated 4 months ago
Alternatives and similar repositories for GATGNN:
Users that are interested in GATGNN are comparing it to the libraries listed below
- Scalable graph neural networks for materials property prediction☆58Updated last year
- NeurIPS 2018 MLMM Workshop: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Predicti…☆64Updated last year
- ☆33Updated 9 months ago
- Crystal Graph Neural Networks☆109Updated last year
- Crystal graph convolutional neural networks for predicting material properties.☆32Updated 2 years ago
- Crystal Edge Graph Attention Neural Network☆21Updated 10 months ago
- Composition-Conditioned Crystal GAN pytorch code☆43Updated 3 years ago
- Representation Learning from Stoichiometry☆59Updated 2 years ago
- ☆29Updated 3 years ago
- Predict materials properties using only the composition information!☆100Updated 2 years ago
- MatDeepLearn, package for graph neural networks in materials chemistry☆189Updated 2 years ago
- A DGL implementation of "Directional Message Passing for Molecular Graphs" (ICLR 2020).☆21Updated last year
- DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules☆20Updated 4 months ago
- image-based generative model for inverse design of solid state materials☆39Updated 3 years ago
- ☆28Updated 3 years ago
- Unsupervised learning of atomic scale dynamics from molecular dynamics.☆81Updated 3 years ago
- FTCP code☆34Updated last year
- Reference implementation of "SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects"☆72Updated 3 years ago
- Workflow for creating and analyzing the Open Catalyst Dataset☆107Updated 3 months ago
- Zeolite GAN☆22Updated 4 years ago
- Learning to Discover Crystallographic Structures with Generative Adversarial Networks☆38Updated 5 years ago
- We developed a novel method, MOF-CGCNN, to efficiently and accurately predict the methane the volumetric uptakes at 65 bar for MOFs. Two …☆18Updated 3 years ago
- ☆19Updated last year
- BAMBOO (Bytedance AI Molecular BOOster) is an AI-driven machine learning force field designed for precise and efficient electrolyte simu…☆81Updated 4 months ago
- 3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning (JCIM 2020)☆41Updated 2 years ago
- Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ https://www.youtube.com/@dr_k_choudhary☆268Updated 3 weeks ago
- This repository contains a collection of resources and papers on GNN Models on Crystal Solid State Materials☆92Updated 2 months ago
- GemNet model in PyTorch, as proposed in "GemNet: Universal Directional Graph Neural Networks for Molecules" (NeurIPS 2021)☆200Updated 2 years ago
- Implementation of "TransPolymer: a Transformer-based language model for polymer property predictions" in PyTorch☆67Updated last year
- ☆36Updated 3 years ago