atomistic-machine-learning / SchNet
SchNet - a deep learning architecture for quantum chemistry
☆226Updated 6 years ago
Related projects ⓘ
Alternatives and complementary repositories for SchNet
- Neural Network Force Field based on PyTorch☆237Updated this week
- Workflow for creating and analyzing the Open Catalyst Dataset☆93Updated 4 months ago
- DScribe is a python package for creating machine learning descriptors for atomistic systems.☆400Updated 2 months ago
- ANI-1 neural net potential with python interface (ASE)☆219Updated 7 months ago
- GemNet model in PyTorch, as proposed in "GemNet: Universal Directional Graph Neural Networks for Molecules" (NeurIPS 2021)☆182Updated last year
- G-SchNet - a generative model for 3d molecular structures☆130Updated last year
- Graph deep learning library for materials☆274Updated this week
- Accurate Neural Network Potential on PyTorch☆464Updated last week
- An SE(3)-invariant autoencoder for generating the periodic structure of materials [ICLR 2022]☆242Updated 2 months ago
- MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.☆536Updated this week
- Converts an xyz file to an RDKit mol object☆247Updated 7 months ago
- DimeNet and DimeNet++ models, as proposed in "Directional Message Passing for Molecular Graphs" (ICLR 2020) and "Fast and Uncertainty-Awa…☆293Updated last year
- Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ&hl=en https://www.youtube.com/watch?v=WYePj…☆229Updated this week
- MatDeepLearn, package for graph neural networks in materials chemistry☆175Updated last year
- NequIP is a code for building E(3)-equivariant interatomic potentials☆629Updated 2 weeks ago
- sGDML - Reference implementation of the Symmetric Gradient Domain Machine Learning model☆141Updated last year
- Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals☆507Updated last year
- Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.☆367Updated this week
- Matbench: Benchmarks for materials science property prediction☆120Updated 2 months ago
- SchNetPack - Deep Neural Networks for Atomistic Systems☆788Updated 2 weeks ago
- End-To-End Molecular Dynamics (MD) Engine using PyTorch☆566Updated last month
- OpenMM plugin to define forces with neural networks☆183Updated this week
- Training neural network potentials☆333Updated 2 months ago
- Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov☆248Updated last week
- A repository of update in molecular dynamics field by recent progress in machine learning and deep learning.☆294Updated 3 years ago
- Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials☆340Updated 2 weeks ago
- An open-source Python package for creating fast and accurate interatomic potentials.☆291Updated last week
- a package for developing machine learning-based chemically accurate energy and density functional models☆102Updated last month
- ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures.☆144Updated 4 months ago
- molSimplify code☆172Updated this week