seongsukim-ml / GPWNOLinks
Gaussain plane-wave neural operator (GPWNO) is a novel approach to predict the electron density of molecule, combining two types of the bases: Gaussian-type orbital and plane-wave.
☆24Updated 10 months ago
Alternatives and similar repositories for GPWNO
Users that are interested in GPWNO are comparing it to the libraries listed below
Sorting:
- Official implementation of the NeurIPS 23 spotlight paper of ♾️InfGCN♾️.☆15Updated last year
- [NeurIPS 2024] source code for "A Recipe for Charge Density Prediction"☆35Updated 7 months ago
- Data and code for graph neural network accelerated molecular dynamics☆42Updated 3 years ago
- [TMLR 2023] Training and simulating MD with ML force fields☆112Updated 8 months ago
- Higher-order equivariant neural networks for charge density prediction in materials☆59Updated 4 months ago
- [ICLR 2024 Spotlight] Official Implementation of "Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Product…☆64Updated 8 months ago
- [NeurIPS 2024] Official implementation of the Efficiently Scaled Attention Interatomic Potential☆51Updated 4 months ago
- Equivariant machine learning interatomic potentials in JAX.☆73Updated 2 months ago
- Code for “From Molecules to Materials Pre-training Large Generalizable Models for Atomic Property Prediction”.☆59Updated 8 months ago
- Implementation of "Denoise Pretraining on Non-equilibrium Molecular Conformations for Accurate and Transferable Neural Potentials" in PyT…☆14Updated last year
- Multi-modal conditioning diffusion model for MOFs generation☆32Updated 5 months ago
- Official implementation of DeepDFT model☆80Updated 2 years ago
- Reference implementation of "Ewald-based Long-Range Message Passing for Molecular Graphs" (ICML 2023)☆50Updated 2 years ago
- Higher order equivariant graph neural networks for 3D point clouds☆40Updated 2 years ago
- Diffusion Probabilistic CDVAE☆24Updated last year
- Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting☆31Updated last year
- A repository for implementing graph network models based on atomic structures.☆85Updated 11 months ago
- This repo contains the datasets used for the paper The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials.☆18Updated 2 years ago
- [TMLR 2024] Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields☆37Updated 5 months ago
- Build neural networks for machine learning force fields with JAX☆122Updated last month
- Generative materials benchmarking metrics, inspired by guacamol and CDVAE.☆40Updated last year
- This repository contains a collection of resources and papers on GNN Models on Crystal Solid State Materials☆98Updated last month
- [ICLR 2025] GotenNet: Rethinking Efficient 3D Equivariant Graph Neural Networks☆39Updated last month
- ☆58Updated 7 months ago
- A library for building equivariant neural networks and a zoo of implementations & examples.☆32Updated 2 years ago
- Reference implementation of "SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects"☆77Updated 3 years ago
- ☆23Updated last year
- An SE(3)-invariant autoencoder for generating the periodic structure of materials [ICLR 2022]☆23Updated 9 months ago
- A text-guided diffusion model for crystal structure generation☆62Updated last month
- Denoising diffusion probabilistic models for replica exchange☆24Updated 3 years ago