Joseph Schwan and Pablo Andres Unzueta have received funding from the Department of Energy Office of Science Graduate Student Research, or SCGSR, program to conduct their research at Department of Energy, or DOE, labs.
Schwan, a doctoral candidate in mechanical engineering working on improving lithium-ion battery performance with associate professor Lorenzo Mangolini, will work with the National Renewable Energy Laboratory under the guidance of researcher Michael Carroll. His project involves improving the anode, or negative end, of the lithium-ion battery by adding or replacing the current active material, graphite, with silicon. Silicon can increase capacity tenfold, but its poor electrical conductivity, material degradation, and changing surface chemistries have hindered commercialization.
Mangolini’s lab has come up with a low-cost way to overcome most of these problems, but it requires a better understanding of the complex chemistry between the liquid electrolyte and the anode material.
“The SCGSR grant will allow me to peer into the chemical workings of batteries as they cycle with one-of-a-kind machines,” Schwan said. “This will let us see the different chemistries that develop when we alter the active material’s surface layer, leading us to the next generation of rechargeable batteries.”
Unzueta, a doctoral student in chemistry, will work with Oak Ridge National Laboratory under the guidance of researcher Victor Fung, who also received his doctorate in chemistry at UCR.
Nuclear magnetic resonance, or NMR, chemical shifts play an important role in solving the structures of materials, pharmaceuticals, and biologically relevant molecules. However, disentangling a NMR spectrum for a complex system which contains many chemical shifts can be a major challenge.
With his UCR dissertation advisor, chemistry professor Gregory Beran, Unzueta has already built what he believes to be the most accurate machine learning model for NMR chemical shifts available, but it is currently limited to a relatively narrow class of molecules. His work with Oak Ridge National Laboratory will extend this approach to a much broader and more representative class of species, which contain any of the chemical elements commonly found in organic molecules.
“While popular physics-based methods work well for chemical shift prediction, the computer time and resources to perform those predictions increases rapidly with the size and complexity of the system being studied, and such predictions quickly become intractable,” Unzueta said. “Machine learning models promise to accelerate the process by orders of magnitude, but this requires models that can predict chemical shifts accurately for a wide variety of chemical systems.”
SCGSR awardees are selected from a diverse pool of graduate applicants from institutions around the country. Selection was based on merit peer review by external scientific experts. SCGSR awardees work on research projects of significant importance to the Office of Science mission and that address societal challenges at national and international scale. Projects in this cohort cover topics like fundamental studies for energy sciences, earth systems modeling, atmospheric systems research, advanced accelerator and detector research, nuclear physics, enabling R&D for fusion energy, microelectronics, machine learning, quantum information science, and data science.