From Catalytic Networks to Activity Landscapes: A Hierarchical Nonequilibrium Framework for Catalysis
- 1Research Computing Center
- 2University of North Carolina
- 3Department of Chemistry
Abstract
Catalysis is commonly described in terms of free-energy profiles, elementary steps, barriers, intermediates, adsorption energies, turnover frequency (TOF), and selectivity. These quantities are essential, yet they do not fully answer a deeper question: what makes a system catalytic? Here we retain the waterwheel picture for catalysts as an organizing model and develop it into a hierarchical framework connecting microscopic kinetics to macroscopic turnover. In this framework, the established correspondence between productive steady-state flux and TOF is used to recast catalytic performance as a nonequilibrium activity landscape, linking rate constants and catalyst-state populations to observable performance. A catalyst is viewed as a hierarchical system with accessible multistate capacity, regenerative closure, kinetic and thermodynamic compensation, and productive nonequilibrium steadystate (NESS) current. The catalyst provides the structured wheel, while reaction conditions provide the thermodynamic driving force. The framework recovers classical catalytic principles as limiting cases and motivates catalyticity as a condition-dependent capacity defined over an explicitly declared operating or design domain, rather than as a new observable replacing TOF. A literature-inspired Pd(PPh3)4/Pd-PEPPSI Suzuki–Miyaura model illustrates how the framework converts barrier data into NESS currents, ratecontrol coefficients, Hessian modes, frustration indices, and domain-level activity capacities. A Gaussianprocess surrogate and an active-learning workflow further show how sparse evaluations of the activity landscape can reconstruct and navigate such landscapes, suggesting a practical route toward DFT- and experiment-guided catalyst design.
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BibTeX
@misc{liu2026from,
title = {{From Catalytic Networks to Activity Landscapes: A Hierarchical Nonequilibrium Framework for Catalysis}},
author = {Shubin Liu},
howpublished = {ChemRxiv},
year = {2026},
month = oct,
doi = {10.26434/chemrxiv.15010707/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010707/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - From Catalytic Networks to Activity Landscapes: A Hierarchical Nonequilibrium Framework for Catalysis
AU - Shubin Liu
PY - 2026
DA - 2026/10/20
PB - ChemRxiv
DO - 10.26434/chemrxiv.15010707/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010707/v1
AB - Catalysis is commonly described in terms of free-energy profiles, elementary steps, barriers, intermediates, adsorption energies, turnover frequency (TOF), and selectivity. These quantities are essential, yet they do not fully answer a deeper question: what makes a system catalytic? Here we retain the waterwheel picture for catalysts as an organizing model and develop it into a hierarchical framework connecting microscopic kinetics to macroscopic turnover. In this framework, the established correspondence between productive steady-state flux and TOF is used to recast catalytic performance as a nonequilibrium activity landscape, linking rate constants and catalyst-state populations to observable performance. A catalyst is viewed as a hierarchical system with accessible multistate capacity, regenerative closure, kinetic and thermodynamic compensation, and productive nonequilibrium steadystate (NESS) current. The catalyst provides the structured wheel, while reaction conditions provide the thermodynamic driving force. The framework recovers classical catalytic principles as limiting cases and motivates catalyticity as a condition-dependent capacity defined over an explicitly declared operating or design domain, rather than as a new observable replacing TOF. A literature-inspired Pd(PPh3)4/Pd-PEPPSI Suzuki–Miyaura model illustrates how the framework converts barrier data into NESS currents, ratecontrol coefficients, Hessian modes, frustration indices, and domain-level activity capacities. A Gaussianprocess surrogate and an active-learning workflow further show how sparse evaluations of the activity landscape can reconstruct and navigate such landscapes, suggesting a practical route toward DFT- and experiment-guided catalyst design.
ER -