Welcome to ULaMDyn’s documentation!

ULaMDyn is a python-based toolkit for data analysis of (nonadiabatic) molecular dynamics (MD) simulations built on top of the Pandas and Scikit-Learn framework. It provides a set of classes and methods to perform the preprocessing, statistical, and unsupervised learning analysis of MD trajectories data generated by the Newton-X program. ULaMDyn was designed to automate the search and discovery of hidden patterns in high-dimensional molecular data sets representing complex potential energy surfaces, thereby enhancing the interpretability and understanding of nonadiabatic dynamics simulations.

Features

• Data curation:

  • Collect and process text outputs from multiple MD trajectories to construct structured data sets.

  • Facilitate data sharing by providing data sets in standard csv format.

• Statistical analysis:

  • Perform a descriptive statistics by computing the median, mean and standard deviation over all available MD trajectories.

  • The bootstrap algorithm can be used to determine the uncertainty of molecular properties within a given confidence level.

• Dimensionality reduction:

  • Principal Component Analysis (PCA).

  • Kernel Principal Component Analysis (KPCA).

  • Isometric Mapping.

  • t-distributed Stochastic Neighbor Embedding (TSNE).

• Patterns search in full dimensional MD data:

  • K-Means clustering.

  • Hierarchical agglomerative clustering.

  • Spectral clustering.

Getting Started:

• Installation
  • Development version
  • Optional dependencies
• Wrapper program
  • Step 1: create geom.xyz file
  • Step 2: run ULaMDyn Wrapper

Tutorial:

• Data curation
  • GetProperties:

Source documentation:

• ulamdyn package
  • Submodules
  • ulamdyn.data_loader module
  • ulamdyn.data_writer module
  • ulamdyn.descriptors module
  • ulamdyn.interface module
  • ulamdyn.kinetics module
  • ulamdyn.run_analysis module
  • ulamdyn.statistics module
  • ulamdyn.unsup_models module
  • ulamdyn.utilities module
  • Module contents

License

This package is freely available for use and distribution under the terms of the GNU Public License (GPL version 3).

Our team:

Light and Molecules group - Aix-Marseille University (AMU), France 🇫🇷

Maintainers

• Max Pinheiro Jr (AMU), max.pinheiro-jr@univ-amu.fr

Contributors

• Mariana Casal (AMU): Jupyter notebook tutorials

Coordinator

• Prof. Mario Barbatti (AMU)

We encourage any contributions and feedback. Feel free to fork and make pull-request to the “development” branch in GitLab.