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.

Contents:

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.

Indices and tables