Workflow Basics¶

A scientific workflow in MISPR provides a complete description of the procedure leading to the final data used to predict the desired property of a given molecule or system. It consists of multiple steps ranging from the initial setup of a molecule or system of molecules to a sequence of calculations with dependencies and optional automated post-processing of parsed data to derive properties of interest.

A workflow in FireWorks is modeled as a Directed Acyclic Graph representing the chain of relationships between computational operations. A workflow consists of one or more Fireworks (jobs) with dependencies. The workflow contains information about the links between Fireworks to execute them in the correct order. Each Firework consists of one or more Firetasks that run sequentially. A Firetask is an atomic computing job that can call shell scripts, transfer files, write/delete files, or execute other Python functions. An example of the structure of a DFT workflow in MISPR is shown below:

Once a Workflow object is created, the user can use the FireWorks package to execute the calculations on various computing resources. The goal of MISPR infrastructure is to provide preset workflows for computing properties relevant to the molecular science community and to simplify the process of creating new workflows by using the implemented FireWorks and Firetasks in MISPR as building blocks for custom workflows.

At the end of each workflow in MISPR, an analysis FireTask is performed to analyze the results and generate a report. The report is in the form of a JSON file and/or MongoDB document. It contains all the input parameters used in the calculations, the output data, general information about the calculation like the software version used (Gaussian, LAMMPS, MISPR, etc.), the wall time the full run took, and chemical metadata about the molecule or system of molecules (e.g. SMILES, InChI, molecular formula, etc.).

In general, each property predicted by MISPR workflows is the result of multiple Gaussian or LAMMPS calculations, and the predicted property is represented by a single file/document summarizing data and “raw” information collected from different calculation steps. The MongoDB document corresponding to a predicted property is stored in a MongoDB collection named after the property. For example, bond dissociation energies are stored in a bde collection in the database while electrostatic partial charges are saved in an esp collection and so on. Some of the analysis FireTasks also include optional plotting of the results. Besides the final summary file/document, MISPR stores data from the intermediate calculation steps into a collection called runs in the database.

The following diagram summarizes the process in MISPR workflows to generate the analysis files/documents: