Machine learning for molecular structures

Music City Center 

Machine learning and statistical models in chemistry offer the promise of aiding material identification and discovery from experimental measurements, but developing models that are appropriate for chemistry data (including molecular structures and various experimental signatures) can be challenging. In this work, we investigate machine learning approaches that link molecular structures to properties with the ultimate goal of predicting molecular structure from experimental measurements (e.g., nuclear magnetic resonance spectra). We demonstrate the ability of convolutional autoencoder neural networks to represent spectral data and present results on the utility of molecular structure embeddings for downstream tasks.

machine learning

chemistry 

Section on Statistics in Defense and National Security