Nathan Nichols is a graduate student of materials science at the University of Vermont. His research focuses on development of high performance computational algorithms to explore the field of condensed matter physics. Special interests include continuous space quantum Monte Carlo algorithms, analytic continuation of imaginary-time correlation functions, exotic phases in low dimensional systems (especially strained graphene and nanoporous environments), and using evolutionary computation and machine learning approaches for the quantum many-body problem. Check out his GitHub and code.delmaestro.org for publicly available samples of his code.
PhD in Materials Science, 2021 (prospective)
University of Vermont
Certificate of Graduate Study in Complex Systems, 2021 (prospective)
University of Vermont
BSc in Chemistry, Physics, and Mathematics, 2014
Hartwick College
Use parallelization, deep learning, and evolutionary computation for algorithmic improvements to continuous space quantum Monte Carlo methods.
Direct comparison of experimental spectra with imaginary time density-density correlation functions
Realize a Tomanaga-Luttinger liquid of helium.
Extension of DLP theory using quasi-two-dimensional materials.
Mechanical tuning of interaction potentials.