A collection of my projects showcasing my work in computational chemistry, molecular dynamics, and machine learning for materials science.

ML-CPP: Machine Learning Library with C++ Backend

ML-CPP: Machine Learning Library with C++ Backend

C++ Python

ML-CPP is a high-performance machine learning library that combines the computational efficiency of C++ with the user-friendly interface of Python. The project demonstrates how to bridge these two worlds effectively, creating a machine learning framework that’s both powerful and easy to use.

Molecular Dynamics Simulation with Lennard-Jones Potential

Molecular Dynamics Simulation with Lennard-Jones Potential

C++

This project implements a molecular dynamics simulation framework using the Lennard-Jones potential to model particle interactions. Molecular Dynamics (MD) simulations are a powerful computational technique used in physics, chemistry, and materials science to study the physical movements of atoms and molecules.