Transfer and active learning have the potential to accelerate the development of new chemical
reactions, using prior data and new experiments to inform models that adapt to the target …

Reaction databases provide a great deal of useful information to assist planning of experiments
but do not provide any interpretation or chemical concepts to accompany this information…

This dissertation applies a skeptical but hopeful analytical paradigm and the tools of linear
algebra, numerical methods, and machine learning to a diversity of problems in …

Molecular geometry prediction of flexible molecules, or conformer search, is a long-standing
challenge in computational chemistry. This task is of great importance for predicting structure…

In a departure from conventional chemical approaches, data-driven models of chemical
reactions have recently been shown to be statistically successful using machine learning. These …

Heat-bath configuration interaction (HCI) is a deterministic method that approaches the full CI
limit at greatly reduced computational cost. In this work, computational improvements to the …

Conical intersections (CIs) are important features of photochemistry that determine yields and
selectivity. Traditional CI optimizers require significant human effort and chemical intuition, …

The Py-Conformational-Sampling (PyCoSa) technique is introduced as a systematic
computational means to sample the configurational space of transition-metal-catalyzed …

Polyad is a computer program that constructs sets of strongly interacting vibrational states
from resonant interactions. It utilizes the multi-resonant Hamiltonian model which accounts for …

Single-electron transfer (SET) promotes a wide variety of interesting chemical transformations,
but modeling of SET requires a careful treatment of electronic and solvent effects to give …