This repository was developed by @JuaoSea, an undergraduate student in Chemistry (Bachelor’s degree) at the Federal University of São Carlos (UFSCar).
This project aims to provide a collection of Python-based solutions for chemistry-related problems, ranging from data analysis and molecular visualization to predictive modeling using machine learning. By leveraging powerful libraries such as NumPy, Pandas, and RDKit, this repository serves as a toolkit for students.
The motivation behind this project is to bridge the gap between computational tools and chemistry, making complex analyses more accessible and efficient. The solutions provided can be used in various fields such as analytical chemistry, organic chemistry, and computational chemistry.
In the future, this project can be expanded with additional features and functionalities, including:
- Integration with Machine Learning: Implementing deep learning models for chemical property prediction and reaction optimization.
- Web-based Applications: Developing interactive web tools using frameworks like Streamlit or Flask to facilitate user-friendly chemical computations.
- Database Expansion: Extending support for larger datasets, including open-access chemical databases for more extensive analysis.
- Quantum Chemistry Simulations: Incorporating quantum mechanical calculations using software like Psi4 or OpenMolcas to model molecular behavior more accurately.
- Automation & Scripting: Enhancing automation capabilities for experimental data processing, making lab workflows more efficient.
These improvements will make the project a more robust and valuable resource for the chemistry community.
- @JuaoSea - Idea & Initial Work
- Departamento de Química da Universidade Federal de São Carlos (UFSCar)