Jacob I. Monroe, Ph.D.
Jacob joined the Ralph E. Martin Department of Chemical Engineering at the University of Arkansas as an assistant professor in January of 2023. He is originally from Virginia and obtained a B.S. in Chemical Engineering from the University of Virginia in 2014. He went on to study water-mediated interactions via atomistic simulations at the University of California, Santa Barbara, obtaining his PhD in 2019. During his three years as a postdoc at the National Institute of Standards and Technology, he explored ways in which machine learning techniques can be leveraged to improve the efficiency of molecular simulations. Jacob is an avid runner and enjoys playing violin in chamber ensembles whenever he gets the chance.
Positions Available!
We are currently seeking undergraduate and graduate researchers to contribute to our research efforts. Desirable qualifications include experiences with statistical mechanics, thermodynamics, applied mathematics, molecular simulations, and/or code development, though enthusiasm is the only requirement. Please send inquires with an attached CV or resume to jacob.monroe@uark.edu.
Graduate Researchers
Zobaer Rahman
Zobaer’s research focuses on developing probabilistic backmappings from coarse-grained models to atomistic representations. An emphasis is placed on solvent, especially water, which is important yet computationally expensive to model explicitly.
Nazanin Abbasi
Nazanin uses molecular simulations to explore the process of scaling, or the deposition of salt aggregates, at membrane interfaces and how this may be controlled by tuning the nanoscale geometric patterning of an interface. Of particular interest is how this process occurs during membrane distillation, which is typically utilized for purifying high mineral content water sources.
Mahsa Khalili
In collaboration with the Harris group at the University of Arkansas, Mahsa applies VAEs to understand and accelerate Monte Carlo procedures that determine rate coefficients for models of biological signaling and metabolism.
Muhi Muntaka
Muhi performs molecular dynamics simulations of self-assembling peptides, specifically diphenylalanine. Using developed backmappings from CG models of this peptide, she plans to use VAE-based MC to examine its aggregation behavior, specifically the free energies of different self-assembled structures, under fully atomistic models.
Postdoctoral Researchers
Abhinaw Kumar
Abhinaw develops probabilistic backmappings for a variety of systems, currently focusing on diphenylalanine. Of primary interest is the establishment of theory and foundational knowledge around capabilities, limitations, and best-practices with regards to machine-learned backmappings that produce configurations amenable to reweighting.
Undergraduate Researchers
Austen Lee
Austen fits equations of state through Gaussian Process Regression techniques, which can simultaneously incorporate various thermodynamic derivatives and their uncertainties to produce a model for the free energy with its own uncertainty estimates.
Sage Paschall
Sage trains machine-learned backmapping models of diphenylaline, working to identify atomistic coordinate systems that improve model performance.
Previous Group Members
Cole Donnelly – Undergraduate Researcher
Cole conducted molecular dynamics simulations to determine the propensity for salt aggregates to nucleate and grow in both bulk solution and at model membrane interfaces.