txie-93/gdynet
Unsupervised learning of atomic scale dynamics from molecular dynamics.
This tool helps materials scientists understand how atoms move and interact within materials by analyzing molecular dynamics simulations. You input a molecular dynamics trajectory file, and it outputs insights into the underlying atomic-scale dynamics. Researchers studying material properties and behavior would find this useful.
No commits in the last 6 months.
Use this if you need to automatically learn and visualize complex atomic movements and interactions from molecular dynamics simulation data.
Not ideal if you are not working with molecular dynamics trajectories or require real-time analysis during a simulation.
Stars
85
Forks
23
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Dec 14, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/txie-93/gdynet"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
deepmodeling/deepmd-kit
A deep learning package for many-body potential energy representation and molecular dynamics
chemprop/chemprop
Message Passing Neural Networks for Molecule Property Prediction
mir-group/nequip
NequIP is a code for building E(3)-equivariant interatomic potentials
Acellera/moleculekit
MoleculeKit: Your favorite molecule manipulation kit
CederGroupHub/chgnet
Pretrained universal neural network potential for charge-informed atomistic modeling...