Samuel Taylor

QSE PhD Student at UChicago | NSF GRFP Fellow

Chicago, Illinois, United States

About

I am a PhD student in Quantum Science and Engineering at the Pritzker School of Molecular Engineering, University of Chicago, and an NSF Graduate Research Fellow. My research focuses on developing first-principles computational methods to model excited-state and non-adiabatic electron–nuclear dynamics in materials.

Experience

  • Graduate Research Student at Pritzker School of Molecular Engineering at the University of Chicago
    Sep 2025 - Present · 10 mos

    • PhD student researcher in the Galli Group • Developing first-principles computational methods to model excited-state and non-adiabatic electron–nuclear dynamics in materials

  • Research Assistant: Theory and Simulation Group at ELI ALPS Research Institute
    May 2025 - Aug 2025 · 4 mos

    • Computed the polarization potential in high-harmonic generation (HHG) of argon, incorporating it as a correction to improve the single-active-electron approximation • Investigated the effects of proton-hydrocarbon collisions and ion-induced Coulomb explosion imaging using real-time TDDFT with Ehrenfest dynamics.

  • Vanderbilt University (Nashville, Tennessee, United States · On-site)
    • Research Assistant: Computational Nanoscience
      Aug 2023 - Aug 2025 · 2 yrs 1 mo

      • Investigated light-matter interactions, molecular collisions, and graphene hydrogenation using time-dependent density-functional theory (TDDFT) under Dr. Kalman Varga • Generated over 2 TB of simulation data on the Texas A&M ACES supercomputer and the research group’s remote Linux cluster • Developed over 20,000 lines of Fortran90, Make, Bash, C++, and Python code across projects, including simulations and visualizations for classical Coulomb explosions and implementing Boltzmann-distributed molecular velocities to enhance TDDFT capabilities • Trained and mentored 10 students on TDDFT, simulation setup and execution, and result analysis, including creating over 75 minutes of instructional YouTube tutorials

    • Research Assistant: Computer Graphics, Numerical Methods, and Machine Learning
      Jan 2025 - May 2025 · 5 mos

      • Undergraduate researcher in Dr. David Hyde's Simulation, Optimization, and Learning (SOL) laboratory • Designed and implemented a deep learning based convolutional neural network to enhance the MINRES, GMRES, and BiCGSTAB algorithms for solving linear systems, replacing traditional iterative search direction calculations with ML predictions to significantly improve computational efficiency • Numerically solved the incompressible Euler equations and rendered results in Houdini and Blender

  • Research Assistant at ELI ALPS Research Institute
    May 2024 - Jul 2024 · 3 mos

    • Applied TDDFT to model Coulomb explosion experiments in collaboration with the Ultrafast Chemical Dynamics Group under the supervision of Dr. Karoly Mogyorosi • Executed more than 350 simulations on Coulomb explosion of the first four alkanes, utilizing experimental pulse input data to predict fragmentation patterns • Delivered an hour-long presentation to 25 attendees, including research group leaders at ELI-ALPS and professors from the University of Szeged. Explained TDDFT principles and its application to ELI-ALPS experiments, paving the way for future collaborations between Vanderbilt and ELI-ALPS

  • Research Assistant at University of Tsukuba
    May 2023 - Jul 2023 · 3 mos

    • Performed advanced density-functional theory (DFT) simulations using SALMON (Scalable Ab-initio Light-Matter Simulator for Optics and Nanoscience) software with Dr. Kazuhiro Yabana’s research group • Conducted quantum electrodynamic DFT (QED-DFT) simulations to investigate energy shifts and electronic structure differences between left- and right-handed enantiomers of H4 and H2O2 molecules within chiral cavities exposed to circularly polarized light fields • Visualized intricate simulation results and presented findings in weekly meetings, utilizing Python, JMol, and VisIt to effectively communicate insights to the research team