Grant: Discovery Grants Program - Individual

Computational models of molecules, materials, and their chemistry are an important part of the research and development of new technology. The emergence of machine-learning based research tools has increased the importance of computational models even further. This is due to the ability of computational models to efficiently generate the data needed for machine learning. Despite the great advantages that machine learning gives to scientific research, these methods are only as good as the data on which they are trained. Generating large amounts of physical and chemical data with a computational model requires a compromise between accuracy (how well a model agrees with experiment) and calculation time. Currently, one-electron density functional models are the common choice for such calculations. However, these models can often be unreliable. This is because one-electron density functionals fail to properly describe the correlated motion of electrons. Therefore, there is a need for more reliable and efficient models. The goal of this research plan is to use our discoveries about the relative motion of electrons to design an efficient and universal quantum chemistry model. We recently defined a new property of quantum particles, the momentum balance, which is a measurement of correlated motion. Using momentum balance, we discovered a natural way to classify correlated motion of electrons into two types: potential dependent and universal. This research involves using a different model for each type of correlated motion. Potential dependent correlation will be modelled with our Delta-NO method. Universal correlation will be modelled with our two-electron density functional. A two-electron density functional offers improved reliability and accuracy over a one-electron density functional. Research will be focused on accelerating the calculation of the two-electron density functional, including the use of machine learning. To complete the overall model of electron correlation, we will add multiple models of dispersion and evaluate each. Finally, the model will be extended to excited states to make it more useful. This research will result in a more cost effective tool for the analysis and development of new molecules and materials. It will greatly reduce the number of physical experiments required for research and development, which will reduce the need for expensive equipment, harsh chemicals, and accelerate the overall process. Accelerated research and development has wide reaching benefits. These benefits include improved treatments for disease and the creation of new industries based on new technology. Additionally, over 85% of the budget is dedicated to wages and stipends of undergraduate and graduate students. They will conduct this research and learn valuable problem-solving, communication, and technical skills that are transferable to in-demand careers in academia, government and industry.