Propane Dehydrogenation on Platinum Catalysts: Identifying the Active Sites through Bayesian Analysis

Model-Based Design of Experiments for Temporal Analysis of Products (TAP): A Simulated Case Study in Oxidative Propane Dehydrogenation

Temporal analysis of products (TAP) reactors enable experiments that probe numerous kinetic processes within a single set of experimental data through variations in pulse intensity, delay, or temperature. Selecting additional TAP experiments often involves an arbitrary selection of reaction conditions or the use of chemical intuition. To make experiment selection in TAP more robust, we explore the efficacy of model-based design of experiments (MBDoE) for precision in TAP reactor kinetic modeling. We successfully applied this approach to a case study of synthetic oxidative propane dehydrogenation (OPDH) that involves pulses of propane and oxygen. We found that experiments identified as optimal through the MBDoE for precision generally reduce parameter uncertainties to a higher degree than alternative experiments. The performance of MBDoE for model divergence was also explored for OPDH, with the relevant active sites (catalyst structure) being unknown. An experiment that maximized the divergence between the three proposed mechanisms was identified and provided evidence that improved the mechanism discrimination. However, reoptimization of kinetic parameters eliminated the ability to discriminate between models. The findings yield insight into the prospects and limitations of MBDoE for TAP and transient kinetic experiments.

Reactive Force Field Development for Propane Dehydrogenation on Platinum Surfaces

Propane dehydrogenation (PDH) is an on-purpose catalytic technology to produce propylene from propane that operates at high temperatures, 773-973 K. Several key industry players have been active in developing new catalysts and processes with improved carbon footprint and economics, where Pt-based catalysts have played a central role. The optimization of these catalytic systems through computational and atomistic simulations requires large-scale models that account for their reactivity and dynamic properties. To address this challenge, we developed a new reactive ReaxFF force field (2023-Pt/C/H) that enables large-scale simulations of PDH reactions catalyzed on Pt surfaces. The optimization of force-field parameters relies on a large training set of density functional theory (DFT) calculations of Pt-catalyzed PDH mechanism, including geometries, adsorption and relative energies of reaction intermediates, and key C-H and C-C bond-breaking/forming reaction steps on the Pt(111) surface. The internal validation supports the accuracy of the developed 2023-Pt/C/H force-field parameters, resulting in mean absolute errors (MAE) against DFT data of 14 and 12 kJ mol-1 for relative energies of intermediates and energy barriers, respectively. We demonstrated the applicability of the 2023-Pt/C/H force field with reactive molecular dynamics simulations of propane on different Pt surface topologies and temperatures. The simulations successfully model the formation of propene in the gas phase as well as competitive, unproductive reactions such as deep dehydrogenation and C-C bond cleavage that produce H, C1 and C2 adsorbed species responsible of catalytic deactivation of Pt surface. Results show the following reactivity order: Pt(111) < Pt(100) < Pt(211), and that for the stepped Pt(211) surface, propane activation occurs on low-coordinated Pt atoms at the steps. The measured selectivity as a function of surface topology follows the same trend as activity, the Pt(211) facet being the most selective. The 2023-Pt/C/H reactive force field can also describe the increase of reactivity with the temperature. From these simulations, we were able to estimate the Arrhenius activation energy, 73 kJ mol-1, whose value is close to those reported experimentally for PDH catalyzed by large, supported Pt nanoparticles . The newly developed 2023-Pt/C/H reactive force field can be used in subsequent investigations of different Pt topologies and of collective effects such as temperature, propane pressure, or H surface coverage.

Invariant Molecular Representations for Heterogeneous Catalysis

Catalyst screening is a critical step in the discovery and development of heterogeneous catalysts, which are vital for a wide range of chemical processes. In recent years, computational catalyst screening, primarily through density functional theory (DFT), has gained significant attention as a method for identifying promising catalysts. However, the computation of adsorption energies for all likely chemical intermediates present in complex surface chemistries is computationally intensive and costly due to the expensive nature of these calculations and the intrinsic idiosyncrasies of the methods or data sets used. This study introduces a novel machine learning (ML) method to learn adsorption energies from multiple DFT functionals by using invariant molecular representations (IMRs). To do this, we first extract molecular fingerprints for the reaction intermediates and later use a Siamese-neural-network-based training strategy to learn invariant molecular representations or the IMR across all available functionals. Our Siamese network-based representations demonstrate superior performance in predicting adsorption energies compared with other molecular representations. Notably, when considering mean absolute values of adsorption energies as 0.43 eV (PBE-D3), 0.46 eV (BEEF-vdW), 0.81 eV (RPBE), and 0.37 eV (scan+rVV10), our IMR method has achieved the lowest mean absolute errors (MAEs) of 0.18 0.10, 0.16, and 0.18 eV, respectively. These results emphasize the superior predictive capacity of our Siamese network-based representations. The empirical findings in this study illuminate the efficacy, robustness, and dependability of our proposed ML paradigm in predicting adsorption energies, specifically for propane dehydrogenation on a platinum catalyst surface.

Benchmarking the Accuracy of Density Functional Theory against the Random Phase Approximation for the Ethane Dehydrogenation Network on Pt(111)

The Random Phase Approximation (RPA) is conceptually the most accurate Density Functional Approximation method, able to simultaneously predict both adsorbate and surface energies accurately; however, this work questions its superiority over DFT for catalytic application on hydrocarbon systems. This work uses microkinetic modeling to benchmark the accuracy of DFT functionals against that of RPA for the ethane dehydrogenation reaction on Pt(111). Eight different functionals, with and without dispersion corrections, across the GGA, meta-GGA and hybrid classes are evaluated: PBE, PBE-D3, RPBE, RPBE-D3, BEEF-vdW, SCAN, SCAN-rVV10, and HSE06. We show that PBE and RPBE, without dispersion correction, closely model RPA energies for adsorption, transition states, reaction, and activation energies. Next, RPA fails to describe the gas phase energy as unsaturation and chain-length increases in the hydrocarbon. Finally, we show that RPBE has the best accuracy-to-cost ratio, and RPA is likely not superior to RPBE or BEEF-vdW, which also gives a measure of uncertainty.

Automated Generation of Microkinetics for Heterogeneously Catalyzed Reactions Considering Correlated Uncertainties

The study presents an ab-initio based framework for the automated construction of microkinetic mechanisms considering correlated uncertainties in all energetic parameters and estimation routines. 2000 unique microkinetic models were generated within the uncertainty space of the BEEF-vdW functional for the oxidation reactions of representative exhaust gas emissions from stoichiometric combustion engines over Pt(111) and compared to experiments through multiscale modeling. The ensemble of simulations stresses the importance of considering uncertainties. Within this set of first-principles-based models, it is possible to identify a microkinetic mechanism that agrees with experimental data. This mechanism can be traced back to a single exchange-correlation functional, and it suggests that Pt(111) could be the active site for the oxidation of light hydrocarbons. The study provides a universal framework for the automated construction of reaction mechanisms with correlated uncertainty quantification, enabling a DFT-constrained microkinetic model optimization for other heterogeneously catalyzed systems.

Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective

Autonomous catalysis research requires elaborate integration of operando experiments into automated workflows. Suitable experimental data for analysis by artificial intelligence can be measured more readily according to standard operating procedures.