MLQD: A package for machine learning-based quantum dissipative dynamics.

Machine learning has emerged as a promising paradigm to study the quantum dissipative dynamics of open quantum systems. To facilitate the use of our recently published ML-based approaches for quantum dissipative dynamics, here we present an open-source Python package MLQD (https://github.com/Arif-PhyChem/MLQD), which currently supports the three ML-based quantum dynamics approaches: (1) the recursive dynamics with kernel ridge regression (KRR) method, (2) the non-recursive artificial-intelligence-based quantum dynamics (AIQD) approach and (3) the blazingly fast one-shot trajectory learning (OSTL) approach, where both AIQD and OSTL use the convolutional neural networks (CNN). This paper describes the features of the MLQD package, the technical details, optimization of hyperparameters, visualization of results, and the demonstration of the MLQD 's applicability for two widely studied systems, namely the spin-boson model and the Fenna–Matthews–Olson (FMO) complex. To make MLQD more user-friendly and accessible, we have made it available on the Python Package Index (PyPi) platform and it can be installed via ▪. In addition, it is also available on the XACS cloud computing platform (https://XACScloud.com) via the interface to the MLatom package (http://MLatom.com). Program Title: MLQD CPC Library link to program files: https://doi.org/10.17632/yxp37csy5x.1 Developer's repository link: https://github.com/Arif-PhyChem/MLQD Code Ocean capsule: https://codeocean.com/capsule/5563143/tree Licensing provisions: Apache Software License 2.0 Programming language: Python 3.0 Supplementary material: Jupyter Notebook-based tutorials External routines/libraries: Tensorflow, Scikit-learn, Hyperopt, Matplotlib, MLatom Nature of problem: Fast propagation of quantum dissipative dynamics with machine learning approaches. Solution method: We have developed MLQD as a comprehensive framework that streamlines and supports the implementation of our recently published machine learning-based approaches for efficient propagation of quantum dissipative dynamics. This framework encompasses: (1) the recursive dynamics with kernel ridge regression (KRR) method, as well as the non-recursive approaches utilizing convolutional neural networks (CNN), namely (2) artificial intelligence-based quantum dynamics (AIQD), and (3) one-shot trajectory learning (OSTL). Additional comments including restrictions and unusual features: 1. Users can train a machine learning (ML) model following one of the ML-based approaches: KRR, AIQD and OSTL. 2. Users have the option to propagate dynamics with the existing trained ML models. 3. MLQD also provides the transformation of trajectories into the training data. 4. MLQD also supports hyperparameter optimization using MLATOM's grid search functionality for KRR and Bayesian methods with Tree-structured Parzen Estimator (TPE) for CNN models via the HYPEROPT package. 5. MLQD also facilitates the visualization of results via auto-plotting. 6. MLQD is designed to be user-friendly and easily accessible, with availability on the XACS cloud computing platform (https://XACScloud.com) via the interface to the MLATOM package (http://MLatom.com). In addition, it is also available as a pip package which makes it easy to install. Future outlook: MLQD will be extended to more realistic systems along with the incorporation of other machine learning-based approaches as well as the traditional quantum dynamics methods. [ABSTRACT FROM AUTHOR]