Developing Efficient and Accurate Machine-Learning Methods for Understanding and Predicting Molecular and Material Properties.

Abstract

Machine learning has been widely applied to accelerate molecular simulations and predict molecular/material properties. Machine learning accomplishes this by leveraging the patterns and relationships between a system's features and the desired property. To further the application of machine learning in chemistry, developing new algorithms and featurization techniques is vital. This dissertation presents innovative machine-learning frameworks and featurization techniques to predict a variety of molecular/material properties and accelerate molecular simulations. In Chapter 2, we investigate training neural networks on features built from information obtained from cheap computational electronic structure (e.g., Hartree-Fock) calculations to predict more expensive ab initio calculations. Chapter 3 presents the development of a machine learning framework that combines neural networks with the many-body expanded Full Configuration Interaction method. In Chapter 4, we apply featurization techniques inspired by natural language processing to leverage nominal categorical data for predicting adsorption energies on metallic surfaces at the Density Functional Theory level. Finally, Chapter 5 introduces a novel hybrid Neural Network Potential/Molecular Mechanics algorithm. Overall, this work provides significant insight into developing more efficient and accurate machine-learning methods for understanding and predicting molecular and material properties.