AI Website GeneratorWelcome to my Computational Chemistry Scripts Hub, your go-to resource for time-saving Python and Bash scripts tailored specifically for researchers and students in quantum chemistry, molecular simulations, and materials modelling. Here, you’ll find practical tools to automate repetitive tasks, parse output files, submit and manage jobs, and analyze simulation data with ease. Each script is lightweight, customizable, and crafted to solve common problems faced in computational chemistry workflows. Whether you’re working with Gaussian, ORCA, or other software, these ready-to-use scripts will help you streamline your work, boost productivity, and focus on meaningful scientific insights.
This script can read a Gaussian log file, extract the converged energies from SCF cycles, and plot them as a graph. This is good if you don't have access to a fancy software tool like GaussView and want to view the progress of your calculation straight in terminal.
This Python script quickly extracts geometry optimization convergence data from any Gaussian or ORCA output file and presents it in a clean, easy-to-read table directly in your terminal. It captures Energies, Maximum Force, RMS Force, Maximum Displacement, and RMS Displacement, saving you from manually searching through long output logs. Ideal for quantum chemistry and computational chemistry workflows, this lightweight and user-friendly tool helps you monitor convergence progress, troubleshoot failed optimizations, and focus on meaningful scientific results. Works seamlessly for small molecules and large systems alike.
This Python 3 script generates SCF convergence plots from Gaussian or ORCA output files, helping you visualize optimization progress step-by-step. It can plot single point energy, energy change, RMS gradient, and maximum gradient against the step number, giving a clear picture of how your calculation is converging. Simply provide the output file as the first argument, and the script will parse the data and produce professional-quality plots for presentations, publications, or troubleshooting convergence issues. Perfect for computational chemists and quantum chemistry researchers looking for quick, automated visualization of SCF behavior.
This Python script automates Boltzmann-averaged NMR shielding tensor calculations from multiple conformers generated in Gaussian NMR simulations. It scans all log files in the current directory, extracts NMR shielding tensors, retrieves SCF and Gibbs energies, sorts conformers by their relative energies (kJ/mol), and calculates Boltzmann population percentages. Results are saved in a CSV file listing each atom alongside its Boltzmann-averaged shielding tensor. With a single command, you can process large datasets, streamline conformational analysis, and obtain accurate, thermally weighted NMR data for publications or reports.
This Python 3 script reads Gaussian or ORCA output files and extracts the optimized molecular structure with ease. It can export the Cartesian coordinates of the final optimized geometry to an XYZ file, or directly generate a new Gaussian input file or ORCA input file for further calculations. Simply provide the output file as the first argument (e.g., python3 Extract_Optimized_Molecule_Gaussian+ORCA.py c60.out), and the script will handle the rest. Ideal for computational chemistry workflows, this tool eliminates the need to manually copy coordinates, streamlining the process of moving from optimization to further analysis or simulations.
This Python 3 script separates individual conformers from a CREST conformer scan file (typically named crest_conformers.xyz) into separate XYZ files for easy viewing and analysis. Each conformer is saved with a clear, sequential name (conformer01.xyz, conformer02.xyz, …), and a CSV file is generated listing all conformers along with their corresponding energies. Simply run: python3 crest_conf_separation.py crest_conformers.xyz
This tool is perfect for computational chemists working with CREST-generated conformers, making it quick and effortless to manage, visualize, and document conformational ensembles.
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