(6dl) Atomistic Modeling of Energy Storage Materials

A societal shift towards greater adoption of renewable energy is underway. To accelerate this transition, new approaches for energy storage are needed to address the intermittent nature of these resources. My work employs computational methods to investigate two categories of energy storage: electrochemical and thermal. With regard to electrochemical storage, the lithium-ion battery has revolutionized portable electronics. Nevertheless, batteries that possess energy densities much larger than state-of-the-art technologies are highly desirable for emerging applications such as grid-based storage and electric vehicles. Batteries employing metallic anodes have gained considerable attention, as they can improve safety and theoretically boost energy densities by as much as ten times. However, battery performance is highly susceptible to the interfacial properties at the surface of the metallic anode. Using atomistic techniques such as density functional theory and molecular dynamics, my work probes the chemical origin of these limitations.¹ Thermal energy storage materials, on the other hand, store energy in the form of heat. These systems are able to capture ‘lost’ energy that is typically rejected from a device or process. A central question is: ‘which storage materials are best?’ My research addresses this question by computing the theoretical energy capacities of hundreds of hydration reactions.² The materials database generated by these computations is being examined with machine learning algorithms to more rapidly relate the structure and composition of the materials to their performance. Through collaborations with experimentalists, these relationships can be used to pinpoint specific thermal energy storage materials for synthesis and subsequent characterization.

Research Interests:

I am broadly interested in modeling processes/phenomena related to energy storage materials, and collaborating with experimental colleagues. Some specific examples from my research background and interests include:

• Analyzing electrolyte decomposition reaction pathways at anode surfaces
• Probing structural properties of the solid electrolyte interphase layer in lithium metal batteries
• Analyzing the performance of core-shell nanoparticles as electrocatalysts
• Predicting energy storage capacities of current and new materials
• Applying machine learning to the discovery of new materials

Teaching Interests:

I am interested in teaching a variety of topics including introductory, core, and elective courses. However, based on my interests and expertise I would be best suited to teach: General Engineering, Engineering Thermodynamics, Chemical Reaction Engineering, Scientific Computing, and Battery Fundamentals.

References:

1. J.S. Lowe and D.J. Siegel, “Reaction Pathways for Solvent Decomposition on Magnesium Anodes,” The Journal of Physical Chemistry C 2018, 122, 10714-10724.
2. S. Kiyabu, J.S. Lowe, A. Ahmed, and D.J. Siegel, “Computational Screening of Hydration Reactions for Thermal Energy Storage: New Materials and Design Rules,” Chemistry of Materials 2018, 30, 2006-2017.