pedrojuanbj/MLTSA
Machine Learning Transition State Analysis (MLTSA) suite with Analytical models to create data on demand and test the approach on different types of data and ML models.
This project helps scientists in molecular dynamics identify which molecular features are most important for specific transition states. You input molecular dynamics (MD) trajectory data and a topology file, and it outputs an analysis of relevant features. Researchers working with molecular simulations would use this to understand complex molecular behaviors.
No commits in the last 6 months.
Use this if you are a molecular dynamics researcher needing to pinpoint which specific atomic distances or other features drive a particular molecular transition.
Not ideal if you need to analyze molecular dynamics data without identifying key features related to transition states, or if you are not working with MD simulations.
Stars
7
Forks
5
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jul 19, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/pedrojuanbj/MLTSA"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
arogozhnikov/hep_ml
Machine Learning for High Energy Physics.
CompPhysics/ComputationalPhysics2
Advanced course in Computational Physics, see texbook at...
FNALLPC/machine-learning-hats
FNAL LPC Machine Learning HATS
DeepLearningForPhysicsResearchBook/deep-learning-physics
This project contains additional material for the textbook Deep Learning for Physics Research by...
desy-ml/cheetah
Fast and differentiable particle accelerator optics simulation for reinforcement learning and...