Title: 

Structuring Electroactive Materials Across Decades of Length Scales: Optimal Pathways Through Data-Driven Design

Abstract:

The Davidson group will aim to precisely and predictively design, create, and measure materials across length scales in order to obtain systems that allow for precise spatiotemporal control of the flow of mass, charge, and energy. Machine-learning and artificial-intelligence-guided approaches will be leveraged to accelerate exploration of synthetic landscapes and to define deterministic workflows for materials design. Specific efforts will involve blending concepts in crystal growth and nanoparticle synthesis with the idea of electrochemical additive manufacturing using scanning electrochemical probes to spatially confine electrodeposition and deterministically control precursor delivery. We will aim to design materials with precise control over the compositional and crystallographic structure, which will be elaborated to three dimensions to enable the design of functional materials such as battery electrodes with precisely defined diffusional pathways, surfaces with gradients in wettability to passively control flow of fluids, and light metal nanoplasmonic arrays. Efforts will further involve investigating the stability and evolution of local catalytic sites for CO₂ electrocatalytic reduction by measuring local site activity using scanning electrochemical cell microscopy and deconvoluting the relative catalytic site contributions of various crystallographic features and electronic structure signatures through correlative transmission electron microscopy and electron backscatter diffraction mapping of crystallographic structure and scanning transmission X-ray microscopy as well as X-ray ptychography maps of electronic structure. In each of these efforts the group will utilize data-enabled approaches to explore synthetic spaces by combining design of experiment sampling techniques with high-throughput synthesis and analysis methods to produce initial datasets, which will be modeled using machine learning algorithms to draw relations between parameters of synthesis and the resulting product composition and properties. Models will then be leveraged to enable strategic sampling of the synthetic landscape to target areas where desired products are most likely to be isolated or areas that would improve model performance and expand understanding of chemical design principles. Across these efforts, the group will aim to bridge from tailoring of single sites to structuring of mesoscale geometries with a focus on using accelerated sampling and AI/ML models to elucidate chemical design principles for electroactive systems in a manner that provides reliable control of macroscopic function.