schnetpack

Neural property predictor

A toolbox for training and applying deep neural networks to predict atomistic properties of molecules and materials

SchNetPack - Deep Neural Networks for Atomistic Systems

GitHub

790 stars
32 watching
215 forks
Language: Python
last commit: 10 days ago
Linked from 1 awesome list

condensed-mattermachine-learningmolecular-dynamicsneural-networkquantum-chemistry

Backlinks from these awesome lists:

Related projects:

Repository Description Stars
atomistic-machine-learning/dtnn An open-source Python framework for developing machine learning models to predict quantum-mechanical observables of molecular systems. 77
anthony-wang/crabnet A deep learning framework for predicting material properties from composition information. 94
cedergrouphub/chgnet A neural network potential for atomistic modeling 252
shen-lab/deepaffinity A deep learning framework for predicting protein-compound affinity from molecular sequences and structures 137
txie-93/cgcnn An implementation of a deep learning framework to predict material properties from crystal structures. 657
ppdebreuck/modnet A Python package implementing a machine learning framework for predicting material properties from composition or crystal structure data. 80
molcik/python-neuron A Python library for implementing and training various neural network architectures 40
netket/netket Delivers methods for studying many-body quantum systems with machine learning and neural networks 548
alan-turing-institute/deepsensor Provides an interface for building and evaluating neural process models for environmental prediction tasks using Python libraries. 92
abdulk084/smiles2vec An implementation of a deep learning model for predicting chemical properties from molecular structure data 30
drorlab/atom3d Enables machine learning on three-dimensional molecular structure by providing tools and datasets for working with 3D molecular data 303
chemprop/chemprop A software framework for machine learning of chemical property prediction using message passing neural networks 1,786
priba/nmp_qc An implementation of neural networks on graph structures for learning molecular properties 339
yangnianzu0515/moleood An implementation of a molecular representation learning method with substructure invariance for out-of-distribution generalization. 60
namisan/mt-dnn A PyTorch package implementing multi-task deep neural networks for natural language understanding 2,238