atomistic-machine-learning / schnetpackLinks
SchNetPack - Deep Neural Networks for Atomistic Systems
☆876Updated this week
Alternatives and similar repositories for schnetpack
Users that are interested in schnetpack are comparing it to the libraries listed below
Sorting:
- NequIP is a code for building E(3)-equivariant interatomic potentials☆792Updated 2 weeks ago
- End-To-End Molecular Dynamics (MD) Engine using PyTorch☆661Updated 8 months ago
- Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals☆541Updated 2 years ago
- DScribe is a python package for creating machine learning descriptors for atomistic systems.☆444Updated 2 weeks ago
- SchNet - a deep learning architecture for quantum chemistry☆265Updated 7 years ago
- MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.☆894Updated this week
- Training neural network potentials☆439Updated 3 weeks ago
- 🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.☆555Updated last week
- TorchANI 2.0 is an open-source library that supports training, development, and research of ANI-style neural network interatomic potentia…☆517Updated this week
- Graph deep learning library for materials☆432Updated this week
- A repository of update in molecular dynamics field by recent progress in machine learning and deep learning.☆327Updated 4 years ago
- Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials☆432Updated last month
- Crystal graph convolutional neural networks for predicting material properties.☆779Updated 4 years ago
- Robust representation of semantically constrained graphs, in particular for molecules in chemistry☆802Updated 4 months ago
- DimeNet and DimeNet++ models, as proposed in "Directional Message Passing for Molecular Graphs" (ICLR 2020) and "Fast and Uncertainty-Awa…☆339Updated 2 years ago
- An open-source Python package for creating fast and accurate interatomic potentials.☆335Updated last month
- Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.☆430Updated this week
- Code for 10.1021/acscentsci.7b00572, now running on Keras 2.0 and Tensorflow☆534Updated 2 years ago
- a molecular descriptor calculator☆436Updated last year
- Neural Network Force Field based on PyTorch☆281Updated last month
- An SE(3)-invariant autoencoder for generating the periodic structure of materials [ICLR 2022]