This is a tutorial website for High school students interested in Machine Learning, specifically computational Chemistry. The tutorials on this website utilize and explain many different machine learning models and APIs that can be applied elsewhere and is a useful skill to learn. The first tutorial goes over Chemoinfographis, Data sets, and different API’s to transfigure them and their properties.

This website was developed under the supervision of Prof. Fang Liu and Xu Chen under the Liu Group at Emory University

Machine Learning in Computational Chemistry

Machine learning (ML) is transforming computational chemistry by enabling faster and more accurate predictions of molecular properties, reaction mechanisms, and material behaviors. By training algorithms on historical datasets, ML models uncover patterns and relationships that are difficult or impossible to derive through traditional computational methods alone. Basics of ML in this field include supervised learning for property prediction, unsupervised learning for clustering molecules, and neural networks for modeling complex systems. Its importance lies in reducing computational costs and accelerating discoveries in drug design, catalysis, and environmental applications, making it a powerful tool for addressing pressing scientific challenges.