Author

Tyler Sours

Observed names: Sours, Tyler, Tyler G. Sours

Institution: SandboxAQ

Observed institutions: SandboxAQ, University of California, Davis, Colorado State University

ORCID: 0000-0003-2893-6276

Observed ORCID: 0000-0003-2893-6276

OpenAlex ID: A5034455896

A Theory-Guided X-ray Absorption Spectroscopy Approach for Identifying Active Sites in Atomically Dispersed Transition-Metal Catalysts

2021 · Yizhen Chen, Rachita Rana, et al. · Journal of the American Chemical Society · 58

Circumventing Scaling Relations in Oxygen Electrochemistry Using Metal–Organic Frameworks

2020 · Tyler Sours, Anjli Patel, et al. · The Journal of Physical Chemistry Letters · 58

Predicting Structural Properties of Pure Silica Zeolites Using Deep Neural Network Potentials

2023 · Tyler G. Sours, Ambarish R. Kulkarni · The Journal of Physical Chemistry C · 40

Atomically Dispersed Platinum in Surface and Subsurface Sites on MgO Have Contrasting Catalytic Properties for CO Oxidation

2022 · Yizhen Chen, Rachita Rana, et al. · The Journal of Physical Chemistry Letters · 22

Structural determinants in a glucose-containing lipopolysaccharide from Mycobacterium tuberculosis critical for inducing a subset of protective T cells

2018 · Prithwiraj De, Michael McNeil, et al. · Journal of Biological Chemistry · 16

Screening Cu-Zeolites for Methane Activation Using Curriculum-Based Training

2024 · Jiawei Guo, Tyler Sours, et al. · ACS Catalysis · 14

AQCat25: unlocking spin-aware, high-fidelity machine learning potentials for heterogeneous catalysis

2026 · Omar Allam, Brook Wander, et al. · npj Computational Materials · 11

Elucidating correlated defects in metal organic frameworks using theory-guided inelastic neutron scattering spectroscopy

2022 · Lucas S. R. Cavalcante, Makena A. Dettmann, et al. · Materials Horizons · 10

Early-Cycle Internal Impedance Enables ML-Based Battery Cycle Life Predictions Across Manufacturers

2024 · Sours, Tyler, Agarwal, Shivang, et al. · arXiv (Cornell University) · 5

Quantifying the Limits of Methane Activation in Cu-exchanged Zeolites using Reactive and Interpretable Machine Learning based Potentials

2023 · Jiawei Guo, Tyler Sours, et al. · ChemRxiv · 2

Quantifying the Limits of Methane Activation in Cu-exchanged Zeolites using Reactive and Interpretable Machine Learning based Potentials

2023 · Jiawei Guo, Tyler Sours, et al. · ChemRxiv · 1

Aqvolt: High-Fidelity Machine-Learned Force Fields for Halogenated Solid-State Electrolyte Materials

2026 · Jiyoon Kim, Chuhong Wang, et al. · ECS Meeting Abstracts

AQVolt26: High-Temperature r$^2$SCAN Halide Dataset for Universal ML Potentials and Solid-State Batteries

2026 · Kim, Jiyoon, Wang, Chuhong, et al. · arXiv (Cornell University)

Works count: 14

Citation count: 237

H-index: 8

I10-index: 8