RAPID Reaction Software Ecosystem - VLab

• • The Python Multiscale Thermochemistry Toolbox (pMuTT) is a Python library that implements statistical thermodynamics for thermochemistry calculations and allows a one-stop-shop calculator. Conversion between quantum mechanically computed properties and thermodynamic properties is ubiquitous in multiscale modeling. pMuTT is an open-source software that converts data from a) experimental observations or b) ab-initio data or DFT calculations to thermodynamic properties of species and reactions and kinetic parameters of reactions. Several input and output formats are enabled to make the tool useful. Energy profiles and graphs are also enabled. pMuTT offers extensive functionality right out of the box to automate these routine, and repetitive tasks. pMuTT is implemented in python and can be easily installed using “pip install pmutt”. pMuTT also has extensive documentation page with many clear examples. As an open-source object-oriented Python library, it can be easily accessed and adapted in your own Python code to provide this capability. For more information please visit: pMuTT Documentation.
  • - Documentation for the Python Multiscale Thermochemistry Toolbox (pMutTT)
•     - GitHub Documentation: https://github.com/VlachosGroup/pMuTT
•     - Corresponding Author(s): Jonathan Lym, Gerhard R. Wittreich, Dionisios G. Vlachos (vlachos@udel.edu)

Python Group Additivity (pGrAdd) is a Python package and database that implements the First-Principles Semi-Empirical (FPSE) Group Additivity (GA) method for estimating thermodynamic properties of molecules in the gas phase, liquid phase, and on catalysts. pGrAdd allows researchers to rapidly estimate the thermodynamic properties of thousands of molecules/adsorbates and of large molecules from thermochemistry of a smaller dataset of small molecules, building and deploying models in a fraction of the time normally required. pGrAdd contains databases of gas species and adsorbates on Pt(111) surfaces so new models can be built immediately. In addition, a user can easily build a new database from their own DFT data for any adsorbates and surfaces they require. The method identifies the groups, estimate the group additivity values, and uses these to predict the thermochemistry of new molecules. The method can be applied to other catalysts by supplying new DFT data or by simply using the extended linear scaling relations and the Pt data. pGrAdd can be easily installed using “python PIP”. For more information please visit: pGrAdd Documentation.

- Documentation for the Python Group Additivity (pGrAdd) package

- GitHub Documentation: 

• • https://github.com/VlachosGroup/PythonGroupAdditivity

- Corresponding Author(s): Gerhard R. Wittreich, Dionisios G. Vlachos (vlachos@udel.edu)

The Virtual Kinetic Laboratory Units (VUnits) is a Python library for unit conversion and constants developed by the Vlachos Research Group at the University of Delaware. This code supports Python-based Virtual Kinetic Laboratory software and aims to be lightweight. The list of supported units, constants, and other documentation can be found at VUnits’s documentation.

• • Documentation for the Virtual Kinetic Laboratory Units (VUnits) library.
  • GitHub Documentation: https://github.com/VlachosGroup/vunits
  • Corresponding Author(s): Jonathan Lym, Dionisios G. Vlachos (vlachos@udel.edu)

The Python Reaction Stencil (pReSt) is a first principles-based reaction mechanism generation framework. It learns the reaction rules from DFT data of published reaction mechanisms by generating molecular graphs of reactants and products, extracting the common subgraph, and defining all bonds that change during a reaction. It can generate reaction networks not studied before, “flag” reactions not seen before for further DFT convergence tests, and easily reconcile differences between catalysts and reactants that may introduce new pathways never seen before. The pReSt framework can also be a diagnostic tool for data (mechanism) quality assessment and novel pathway discovery to new molecules.

• • Documentation for the Python Reaction Stencil (pReSt)
  • GitHub Documentation: https://github.com/VlachosGroup/prest
  • Corresponding Author(s): Udit Gupta, Dionisios G. Vlachos (vlachos@udel.edu)

Reaction Network Viewer (ReNView) quickly generates a graphical representation of the reaction fluxes within the system essential for identifying dominant reaction pathways and reducing a mechanism without undergoing manual data processing. ReNView helps users analyze reaction mechanisms and identify key species and reactions by showing the flux for each path (via line thickness), partially equilibrated reactions (via line color), and surface coverage (via species background colors). The code is updated regularly with upcoming features that include lumping species based on the characteristics, such as molecular weight and number of monomers. For more information please visit: ReNView documentation.

• • Documentation for Reaction Network Viewer (ReNView)
  • GitHub Documentation: https://github.com/VlachosGroup/renview
  • Corresponding Author(s): Udit Gupta, Dionisios G. Vlachos (vlachos@udel.edu)

Open-source Microkinetic Modeling (OpenMKM) is a multiscale microkinetics toolkit for modeling homogeneous reactions, e.g., gas-phase, and/or heterogeneous catalytic reactions. Microkinetic modeling enables coupling of “microscale” atomistic data with “macroscale” reactor observables. OpenMKM is a modular, object-oriented, open-source C++ software toolbox developed at the Delaware Energy Institute and is built upon the popular and robust open-source Cantera software. With OpenMKM, users can quickly set up and start performing microkinetic simulations without the need to write any code. For more information please visit: OpenMKM Documentation.

• • Documentation for the Open-source Microkinetic Modeling (OpenMKM) toolkit
  • GitHub Documentation: https://github.com/VlachosGroup/openmkm
  • Corresponding Author(s): Bharat Medasani, Sashank Kasiraju, Dionisios G. Vlachos (vlachos@udel.edu)

Descriptor-Based Microkinetic Analysis Package (DescMap) is a Python-based software package for automating volcano curve generation. It leverages existing software tools in the Virtual Kinetic Laboratory suite for enhanced synergy. DescMAP provides modules for descriptor selection, descriptor sampling, kinetic performance analysis and volcano curve creation. Inputting data via spreadsheets and controlling program behavior via template files increases flexibility and supported capabilities. Interactive output graphs enhance the interpretability and allow users to zoom in on optimal properties of the catalyst.

- Documentation for the Descriptor-Based Microkinetic Analysis Package (DescMap)

- Under Development

- Corresponding Author(s): Jonathan Lym, Xue Zong, Dionisios G. Vlachos (vlachos@udel.edu)

The Chemical Kinetics Database (CKineticsDB) is a state-of-the art datahub for ab-initio calculations-based microkinetic modeling files. It is extensible and adaptable, with a data model rooted in the inherent relations of the stored data, resulting in efficient data management practices. CKineticsDB retains all the information from simulations at various scales and allows accurate regeneration of publication results. The stored data can be accessed based on software parameters, catalysis parameters, reactions of interest, and publications. The data is curated before uploading to the database through a semi-automated process to ensure high quality standards and uniformity in the stored files.

• • Documentation for the Chemical Kinetics Database (CKineticsDB)
  • Under Development
  • Corresponding Author(s): Siddhant M. Lambor, Sashank Kasiraju, Dionisios G. Vlachos (vlachos@udel.edu)

Artificial Intelligence Molecular Similarity (AIMSim) is an open source, accessible cheminformatics platform for performing similarity operations on collections of molecules (molecular datasets). AIMSim brings together the rich knowledge base of the cheminformatics community in an easily accessible interface. It provides a unified platform to simplify cheminformatics workflows for molecular datasets, such as diversity quantification, outlier and novelty analysis, clustering, and inter-molecular comparisons. AIMSim uses Python to abstract sophisticated similarity operations, molecular fingerprinting, and multiprocessing capabilities from the user. The user gets to decide the granularity of control over the operations ranging from no code GUI use to programmatic usage similar to a Python package. With thousands of available chemical descriptors and almost 50 unique metrics for calculating similarity, AIMSim has a diverse range of applications from GNN verification to novelty analysis. Visit our online documentation for installation details, unit-tests, and detailed descriptions of the class structure. Users will also find this interactive, online tutorial helpful.

- Documentation for the Artificial Intelligence Molecular Similarity (AIMSim) platform.

- GitHub Documentation: 

• • https://github.com/VlachosGroup/AIMSim

• - Corresponding Author(s): Himaghna Bhattacharjee, Jackson Burns, Dionisios G. Vlachos (vlachos@udel.edu)

The Chemical Kinetic Bayesian Inference Toolbox (CKBIT) is an open-source Python library that facilitates Bayesian inference upon kinetic model parameters. Bayesian techniques estimate optimal parameter values (maximum a posteriori) or probability distributions (Markov chain Monte Carlo, variational inference) at rapid speeds. Leveraging Excel data entry to facilitate minimal coding, users may estimate activation energies, pre-exponential terms, and reaction orders from chemical kinetic data from various reactors (batch, continuous stirred tank, plug flow) and reaction networks. Additional capabilities of hierarchical error modeling and prior distribution specification make CKBIT a flexible, accurate tool for the task of kinetic parameter estimation and uncertainty quantification. Multiple examples are available online to facilitate implementation. For more information please visit: CKBIT Documentation.

• • Documentation for the Chemical Kinetic Bayesian Inference Toolbox (CKBIT)
  • • GitHub Documentation: https://github.com/VlachosGroup/ckbit
    • Corresponding Author(s): Maximilian Cohen, Dionisios G. Vlachos (vlachos@udel.edu)

The Next Experiment Toolkit in PyTorch (NEXTorch) is an open-source software package in Python/PyTorch to facilitate experimental design using Bayesian Optimization (BO). It can also be used for learning the theory and implementation of BO. The modular and flexible design of NEXTorch can deal with mixed types of parameters and data-type conversions, support both automated and human-in-the-loop optimization and offer various visualization options. It can be used in chemical synthesis in laboratory experiments, molecular modeling, reaction condition optimization, parameter estimation, and reactor geometry optimization, to mention a few examples. Such tasks can easily be performed without extensive programming effort so that the user can focus on domain-specific questions. Its backend from BoTorch/PyTorch enables GPU acceleration, parallelization, and state-of-the-art Bayesian optimization algorithms. Moreover, NEXTorch enables an interface with the commonly used simulation tools in the reaction engineering field, such as CFD and multiphysics simulations, for automatic optimization. For more information please visit: NEXTorch documentation.

• • Documentation for the Next Experiment Toolkit in PyTorch (NEXTorch)
  • GitHub Documentation: https://github.com/VlachosGroup/nextorch
  • Corresponding Author(s): Yifan Wang, Tai-Ying Chen, Dionisios G. Vlachos (vlachos@udel.edu)

The ‘Python-based Quantification under Uncertainty with Analysis through Deconvolution‘ (pQUAD) is an easily customizable software for product quantification. It uses a principal component regression (PCR) model with error propagation via prediction intervals for multivariable regression. It can analyze experimental measurement errors by including the deconvolution of multi-component spectra. pQUAD has been developed with Agile Software Development Workflow in concurrence with feedback from experimentalist-collaborators. It also provides a user interface via Command Prompt in addition to the Python module. For more information please visit: pQUAD documentation.

• • Documentation for the Python-based Quantification under Uncertainty with Analysis through Deconvolution (pQUAD)
  • GitHub Documentation: https://github.com/VlachosGroup/pQUAD
  • Corresponding Author(s): Joshua Lansford, Dionisios G. Vlachos (vlachos@udel.edu)

Parameter Estimation with Bayesian Optimization (petBOA) is a python-based, open-source tool designed for parameter estimation of mathematical models. It uses gaussian process-based Bayesian optimization to minimize objective functions. The optimizer is extended from the core NEXTorch software package. Example templates for kinetic parameter estimation using experimental data for widely used reactor models such as: batch and plug-flow reactor (PFR) are bundled with the tool. A unique feature of this tool is the ability to optimize and fit parametrized microkinetic models to experimental data using a python interface to OpenMKM. The tool can also run global and local sensitivity analysis including normalized sensitivity coefficients (degree of rate control) for rate kinetics.

• • Documentation for the Parameter Estimation with Bayesian Optimization (petBOA) tool.
  • Under Development
  • Corresponding Author(s): Sashank Kasiraju, Yifan Wang, Dionisios G. Vlachos (vlachos@udel.edu)

Learn Chemical Kinetics (LearnCK) is a Python toolkit designed to leverage machine learning to build surrogate neural network models for highly nonlinear and stiff chemical reaction kinetics represented within microkinetic models (MKM). Currently, the toolkit enables artificial neural network (ANN) representation of intrinsic chemical kinetics for heterogenous catalytic systems. This method is exceptionally powerful for large-scale MKM simulations. The tool provides a simple user interface a) to quickly build neural networks with the TensorFlow framework and train them, and b) to develop reactor models (such as: PFR, CSTR, etc.) using the previously trained ANN-based surrogate black-box models. A simple user-interface exists to implement statistical sampling methods such as: Latin Hypercube Sampling (LHS) to generate training data encompassing the phase-space envelop of relevant reaction conditions.

- Documentation for the Learn Chemical Kinetics (LearnCK) framework

•       - Under Development
•       - Corresponding Author(s): Sashank Kasiraju, Dionisios G. Vlachos (vlachos@udel.edu)