2025-07-25 –, Main Room 2
The SmoQySuite organization maintains a growing suite of Julia packages for solving low-energy effective models of strongly-correlated quantum materials. Our organization specifically focuses on developing quantum Monte Carlo related tools for solving model Hamiltonian systems. SmoQySuite pursues a modular development philosophy, enabling users to interface with its suite of packages at various levels of complexity. This allows us to provide useful tools to a large community of researchers.
The SmoQySuite organization is committed to developing and maintaining Julia packages to solve low-energy effective Hamiltonian models for strongly-correlated quantum materials, with particular focus given to quantum Monte Carlo (QMC) related tools. In this talk, we will review the current Julia packages maintained by the SmoQySuite organization and discuss the planned development roadmap.
Adopting a modular design philosophy, the SmoQySuite organization maintains high-level, low- level, and utility packages that serve various research needs. High-level packages implement a specific algorithm for a certain parameterized class of systems. For example, this talk will review the functionality of the SmoQyDQMC.jl package, which exports a user-friendly implementation of the determinant QMC (DQMC) method for simulating Hubbard and flexibly parameterized electron-phonon Hamiltonians. We will also discuss the SmoQyDEAC.jl package, an implementation of the Differential Evolution Analytic Continuation algorithm for obtaining spectral densities based on results generated by QMC simulations.
While high-level packages like the ones mentioned above are useful, researchers frequently encounter specific research questions requiring specialized functionality that fall outside the scope of existing packages. The low-level and utility packages maintained by this SmoQySuite organization are intended to address this issue by exporting functionality to expedite the implementation of other numerical techniques. For instance, the SmoQyDQMC.jl package is built on top of the low-level package JDQMCFrameworks.jl, which exports functionality empowering researchers to implement their own DQMC codes while avoiding many common pitfalls that typically hinder such efforts.
Lastly, a development roadmap for the SmoQySuite organization will be discussed. In particular, development efforts are underway for two new high-level packages focused on modeling strongly- correlated systems near the thermodynamic limit. Specifically, SmoQyElPhQMC.jl will export a QMC algorithm for simulating electron-phonon Hamiltonians with a computational cost that scales linearly with systems size, while SmoQyDCA.jl will implement the dynamical cluster approximation using SmoQyDQMC.jl as the cluster solver.
The development of the SmoQyDQMC.jl and SmoQyDEAC.jl packages was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Award Number DE-SC0022311. The current development of the SmoQyElPhQMC.jl and SmoQyDCA.jl package is supported by the National Science Foundation under Grant No. OAC-2410280. Lastly, I would like to thank the Simons Foundations for supporting my open-source software development efforts with the Scientific Software Research Faculty award.
I am a Research Assistant Professor in the Department of Physics and Astronomy at the University of Tennessee, Knoxville (UTK), and am based out of the Institute for Advanced Materials & Manufacturing (IAMM). My position at UTK was created after receiving the Scientific Software Research Faculty (SSRF) award from the Simons Foundation, and my work focuses on the development of open-source software tools for the condensed matter physics (CMP) research community. Prior to switching to my current position, I worked as a postdoctoral researcher in Professor Steve Johnston’s research group at UTK after receiving my PhD from the Department of Physics and Astronomy at the University of California, Davis.
More broadly, my research interests focus on developing and applying novel numerical methods to address CMP problems and bridge the gap between theory and experiment. I am also interested in investigating opportunities to supplement standard numerical methods in CMP with machine learning approaches to accelerate existing computations and infer additional information from the high-dimensional data sets generated in studies of quantum materials.
My recent development efforts have focused on applying quantum Monte Carlo (QMC) methods to study model Hamiltonians meant to describe various quantum materials. This includes developing well-documented and user-friendly open-source QMC software packages to make these algorithms more accessible to the broader CMP community.