/tools

PythonCompphys is a collection of Jupyter notebooks that cover various topics in computational physics, with a strong emphasis on computational chemistry methods like Hartree-Fock and Density Functional Theory. It serves as a tutorial resource for understanding molecular simulations and quantum mechanics.

This repository contains input files and Python scripts for LAMMPS tutorials, focusing on molecular dynamics simulations. It provides resources for generating molecular structures and analyzing simulation data, making it a valuable tool for computational chemistry and molecular biology applications.

PyQInt is an educational Python implementation of the Hartree-Fock method, designed to provide transparency in electronic structure calculations. It supports various molecular integrals and geometry optimization, making it a useful tool for learning and prototyping in computational chemistry.

Psi4Education offers a suite of computational chemistry lab activities designed for educational purposes. It includes both WebMO labs for quantum chemistry computations and Python labs for data collection and analysis within a Jupyter notebook environment.

The OpenMM Cookbook offers a collection of tutorials and notebooks for users to learn how to utilize OpenMM for molecular dynamics simulations. It serves as a resource for both beginners and advanced users in the field of computational chemistry.

The 'openmm-tutorials' repository offers a collection of Jupyter notebooks designed to help researchers learn how to utilize OpenMM for molecular dynamics simulations. It covers topics such as creating molecular systems and integrators, making it a valuable resource for practitioners in the field.

This repository provides a course on protein language modeling, covering topics such as sequence analysis, model training, and protein design. It includes hands-on notebooks for predicting features and classifying protein sequences using embeddings generated by language models.

This repository provides Jupyter notebooks for tutorials on quantum computing and its applications in chemistry, including quantum algorithms for simulating molecular systems. It serves as an educational resource for understanding quantum machine learning and chemistry simulation techniques.

The UGM 2016 repository provides educational materials from the RDKit User Group Meeting, featuring notebooks and presentations on various topics in cheminformatics, including molecular simulations and property prediction techniques. It serves as a resource for learning and applying RDKit tools in computational chemistry.

The hoomd-examples repository provides Jupyter Notebook tutorials for using HOOMD-blue, a molecular dynamics simulation software. It covers topics such as organizing simulations, modeling rigid bodies, and parallel simulations, making it a valuable resource for those interested in molecular simulations.

This repository provides a collection of programming exercises in Python that emphasize quantum chemistry concepts, utilizing the PySCF package for quantum calculations. It serves as a tutorial for those interested in learning about quantum chemistry programming.

The ccpbiosim-2023 repository provides educational materials for a workshop on molecular simulations and force fields, including notebooks for working with protein-ligand systems and other molecular types. It serves as a tutorial resource for users interested in molecular modeling and simulation techniques.

This repository provides a tutorial on how to analyze molecular dynamics simulations of membrane proteins using VMD and MDAnalysis. It includes examples of simple analysis tasks and encourages users to write their own analysis code.

The GGM_LOG_Tutorial provides a tutorial on geometric generative models, which can be applied in the context of molecular design and optimization. It is aimed at researchers interested in generative modeling techniques for molecular applications.

NAMD-FEP is a tool designed for calculating the binding free energy difference between two small molecules interacting with the same protein target using free energy perturbation (FEP) methods. It provides a Jupyter Notebook tutorial for users to perform FEP simulations efficiently, leveraging cloud GPU resources.

The Chemical Graph Series is an educational resource that teaches users how to represent molecules as graphs and apply deep learning techniques, specifically graph neural networks, to predict chemical properties. It covers foundational concepts in cheminformatics and progresses to advanced modeling techniques, including property prediction using real datasets.

This repository contains files and resources for a workshop series on quantum chemistry calculations using Python. It covers topics such as molecule input generation and the basics of DFT, along with methods to derive molecular properties from quantum mechanical calculations.

This repository provides Jupyter notebooks and simulation inputs for a mini course on enhanced sampling methods using GROMACS. It covers techniques such as umbrella sampling and metadynamics, which are crucial for molecular simulations and dynamics.

The 'tutorial-lmp' repository offers step-by-step instructions for setting up and running molecular dynamics simulations with polarizable force fields in LAMMPS. It covers the creation of simulation boxes, equilibration, and the addition of explicit polarization terms, making it a valuable resource for those interested in molecular simulations.

This repository provides an introductory workshop on protein language models, exploring the theoretical and practical aspects of protein embeddings and their applications in protein-related machine learning tasks.

This repository implements a method for training neural network potentials for coarse-grained proteins using unsupervised learning. It allows for the simulation of proteins and the prediction of native-like conformations through molecular dynamics, making it a valuable tool in computational chemistry.

This repository hosts tutorials for using GROMACS, a software package for molecular dynamics simulations. It includes various input files and step-by-step guides for simulating molecular systems, such as proteins and polymers, which are essential for understanding molecular behavior.

LSQT-Jupyter is a tutorial that demonstrates linear scaling quantum transport methods using Jupyter notebooks. It serves as an educational resource for understanding quantum transport in molecular systems, although it is not intended for high-performance computing applications.

MDAnalysisMolSSIWorkshop-Intermediate2Day provides materials for a workshop on using MDAnalysis, a Python library for analyzing molecular dynamics simulations. The repository includes tutorials on loading, manipulating, and analyzing molecular simulation data, making it a valuable resource for those working in computational chemistry.