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## Overview
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MISPR is a software that executes, manages, and stores computational materials science
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simulations. It contains pre-defined density functional theory (DFT) and molecular dynamics (MD) workflows to calculate and analyze different
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properties of materials. MISPR uses [MDPropTools][mdproptools] to perform MD analysis.
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MISPR is a free, open-source software for executing, managing, and storing computational materials science simulations. Features of MISPR include:
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- Predicts various materials properties through pre-defined workflows
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- Integrates density functional theory (DFT) and molecular dynamics (MD) simulations seamlessly
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- Processes, analyzes, and visualizes simulation results with built-in utilities
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- Handles errors automatically using recipe-like fixes
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- Builds and manages computational databases in MongoDB backend for easy querying, analysis, and sharing
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- Supports multiple queuing systems via FireWorks including SLURM, PBS, SGE, etc.
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- Scales architecture to handle computations from individual materials to thousands of compounds
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- Modifies and chains workflows together flexibly
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- Leverages open-source libraries including [pymatgen][pymatgen], [custodian][custodian], and [FireWorks][fireworks]
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## Workflows
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3. Build initial system configuration
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4. Create LAMMPS data file containing initial atomic coordinations, molecular topology, and force field parameters
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5. Run two step energy minimization, NPT equilibration run, melting and quenching, and an NVT production run
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6. Perform analysis using the generated log and trajectory files (radial distribution function, coordination number, cluster analysis, mean squared displacement and diffusion)
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6. Perform analysis using the generated log and trajectory files (radial distribution function, coordination number, cluster analysis, mean squared displacement and diffusion) using [MDPropTools][mdproptools]
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### Hybrid Workflows
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-**General**: A workflow that combines the ESP DFT workflow with the MD workflow described above
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-**NMR**: A workflow that first runs the general hybrid workflow, then analyzes solvation shells from MD trajectories, and finally runs the NMR DFT workflow on representative solvation structures to predict chemical shifts
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All these workflows are easily customizable and can be modified to suit specific research needs.
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