torchani

Neural simulator

A PyTorch implementation of a neural network potential for molecular simulations

Accurate Neural Network Potential on PyTorch

GitHub

471 stars
30 watching
130 forks
Language: Python
last commit: almost 2 years ago
deep-learningforce-fieldmolecular-simulationneural-networkquantum-chemistryquantum-mechanics

Related projects:

RepositoryDescriptionStars
pasqal-io/pyqtorchA PyTorch-based simulator for quantum machine learning45
ahmedfgad/torchgaTrains PyTorch models using a genetic algorithm96
kaihsin/tor10A PyTorch-based tensor network library for quantum simulation60
ypxie/pytorch-neucomAn implementation of the Differentiable Neural Computer architecture in PyTorch94
project-dc/pygenesesA PyTorch-based framework for training and studying artificial species in bio-inspired environments72
achaiah/pywickA PyTorch-based neural network training framework with advanced features and utilities398
higgsfield/interaction_network_pytorchAn implementation of Interaction Networks for learning physical simulations and generalizing to novel systems131
jingweiz/pytorch-dncAn implementation of Neural Turing Machine and Differentiable Neural Computer architectures using PyTorch and Visdom for deep learning tasks.278
ixaxaar/pytorch-dncAn implementation of Differentiable Neural Computers and family for PyTorch, enabling scalable memory-augmented neural networks.338
norse/norseDeep learning framework for spiking neural networks685
dyhan0920/pyramidnet-pytorchAn implementation of a deep neural network architecture for image classification tasks273
ne7ermore/torch-lightA comprehensive collection of deep learning examples using PyTorch, covering various applications and models.537
graal-research/poutyneA PyTorch framework simplifying neural network training with automated boilerplate code and callback utilities572
sinkingsugar/nimtorchA Nim frontend for PyTorch, generating native ATen code for machine learning and artificial neural networks464
akanimax/pro_gan_pytorchImplementation of a deep learning model for generating high-quality images with improved stability and variation.538