Richmond has earned degrees from the University of Washington and Michigan. As a graduate student at the University of Michigan he wrote software to analyze statistical mechanical simulations of nanoparticles in order to study the nucleation rates in systems of self-assembling nanopolyhedra. His interests in science are generally broader than this specific work, and he is enthusiastic about applying scientific and numerical analysis methods to solve real-world problems.
Various data consulting work for clients
Designed and built software to evaluate clinical treatment pathways and determine whether provided patient care was consistent with internal standards.
Performed research in the field of computational statistical mechanics by simulating the self-assembly of polyhedral nanoparticles.
Computed Gibbs free energy as a function of cluster size in a family of related polyhedra to better understand how polyhedral faceting and emergent directional entropic forces promote or hinder crystal nucleation.
Contributed simulations of the self-assembly of gold nanopolyhedra into large-scale superlattices to better understand the homogeneous and heterogenous nucleation behavior observed in the experiments and factors required to create large single crystalline domains.
Provided Monte Carlo simulations of bipyramidal nanoparticles discovering that two separate mechanisms, either slight edge truncation or particle attractions, can stabilize the novel striped phase.
Performed molecular dynamics simulations to quantify the role of rigid clusters on gel fluid rheology.
Contributed software tools and supervised simulations studying colloidal solid-solid phase transitions as a function of polyhedral geometry.
Assisted primary author in performing the Monte Carlo simulations, interpreting data, and in automatically detecting observed crystalline phases.