This tutorial bridges the gap in existing VAE literature by integrating modern PyTorch functionalities like torch.distributions and dataclasses for more efficient, cleaner code. Aimed at advancing understanding and application of VAEs with the latest PyTorch features.
In this post, we’ll use the Coulomb matrix eigenvalues to classify alkane constitutional isomers using supervised learning, replicating methods used in the paper ‘Can One Hear the Shape of a Molecule (from its Coulomb Matrix Eigenvalues)?’
This post replicates the unsupervised learning methods used in the paper ‘Can One Hear the Shape of a Molecule (from its Coulomb Matrix Eigenvalues)?’ to classify the constitutional isomers of alkanes.
Replicating the paper “Can One Hear the Shape of a Molecule (from its Coulomb Matrix Eigenvalues)?” by generating alkane constitutional isomers, calculating their Coulomb matrix eigenvalues, and using machine learning to classify the isomers.
Explore the pivotal role of Coulomb matrices in representing molecular structures for machine learning. Learn about their calculation and application as a molecular descriptor in advancing computational chemistry.