Welcome to MLTSA’s documentation!
MLTSA is a python-based package which enables users to apply the Machine Learning Transition State Analysis from https://doi.org/10.1021/acs.jctc.1c00924 to any given data with an array of ML models, as well as generating an analytical model on demand for detecting relevant features in a dataset.
MLTSA: Machine Learning Transition State Analysis repository
Introduction
This is a Python package to apply the MLTSA approach for relevant CV identification on Molecular Dynamics data using both Sklearn and TensorFlow modules.It also includes both a suite of 1D Potential Analytical model feature generation module for light testing and a suite of different 2D potential shapes (Spiral, Z-shaped) generation as well as the posterior feature generation by 1D projections of the 2D data. In this package you will find:
Data Generation Module (MLTSA_datasets) : Contains files with the easy to call 1D/2D/MD examples to generate data or play around with it as tests for the approach.
Scikit-Learn-based ML models and Feature Reduction module (MLTSA_sklearn) : Contains the Scikit-Learn integrated functions to apply MLTSA on data.
TensorFlow-based ML models and Feature Reduction module (MLTSA_tensorflow): Contains the set of functions and different models built on TensorFlow to apply MLTSA on data.
Usage
Example OneD
Example TwoD
Example Train
Example MLTSA
Installation
To use MLTSA, first install it using pip:
(.venv) $ pip install MLTSA
Note
This project is under active development, do not expect a stable version, code is provided as is.