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applied-deep-learning

Applied Deep Learning

atomicgraphnets.jl

Atomic graph models for molecules and crystals in Julia

cgdms

Differentiable molecular simulation of proteins with a coarse-grained potential

cgmap

Simple fine-grained to coarse-grained molecular dynamics mapping code

cgnet

learning coarse-grained force fields

cookiecutter-cms

Python-centric Cookiecutter for Molecular Computational Chemistry Packages

gelstack

learning-dl

Python implementations to learn DL

meso

USER-MESO package for LAMMPS

neuralforcefield

Neural Network Force Field based on PyTorch

openpathsampling

An open source Python framework for transition interface and path sampling calculations.

papers-for-molecular-design-using-dl

List of molecular design using Generative AI and Deep Learning

physics-based-deep-learning

Links to works on deep learning algorithms for physics problems, TUM-I15 and beyond

polychrom

Polymer simulations of chromosomes and generating "in silico" Hi-C maps

pysages

Python Suite for Advanced General Ensemble Simulations

schnax

An implementation of SchNet in JAX and JAX-MD.

torchmd-cg

Example to fit parameters and run CG simulations using TorchMD and Schnet

torchmd-net

Neural network potentials based on graph neural networks and equivariant transformers

user-3spn2

Coarse-grained molecular model of DNA (LAMMPS plugin)

user-wlc

Lammps package for WLC

verysimplemd

A very simple molecular dynamics code

warp

A Python framework for high performance GPU simulation and graphics