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At least 145 records · Page 8

Reagent-Triggered Isomerization of Fluxional Cluster Catalyst via Dynamic Coupling

Metallic cluster catalysts have many thermodynamically accessible isomers with diverse active sites and low reaction barriers, and lately a strong hypothesis emerged that the many catalyst states collectively drive the catalysis. Furthermore, it remained a hypothesis that catalyst isomerization is actually kinetically feasible under the current reaction conditions. Using high-temperature dynamics simulations and sampling, a range of orientations, and vibrational energy distributions, we probe how thermal effects and molecular events affect cluster catalyst dynamics. We show that even such a delicate affair as the dissociation or scattering of a methane molecule on the heavy and thus slow Pt 13 cluster triggers substantial isomerization of the catalyst, far beyond thermal at 700 K. A kinetic coupling between the methane activity and cluster catalyst dynamics is observed. In return, the thermal dynamics of the cluster affects the methane reaction and scattering probabilities. Hence, molecular events at the surfaces of fluxional cluster catalysts should facilitate the population of an ensemble of catalyst states under the current reaction conditions, with implications for available active sites, reaction mechanisms, and apparent rates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Treasuring trash: Pt/SrTiO 3 catalysts process plastic waste into high-value materials

Single-use plastics are ubiquitous throughout modern society because they have properties that make them desirable in a wide variety of applications, including low cost of production, high thermal and chemical stability, and tunable mechanical properties. Several of the recycling methods currently used to process single-use plastic waste, such as mechanical recycling and pyrolysis, do not selectively process plastic materials into uniform products, as would be advantageous for repurposing these materials in pursuit of a circular economy. Catalytic hydrogenolysis, whereby C–C bonds in polymers are broken over a supported catalyst in the presence of H 2 , is a promising approach to converting plastics into value-added products that can be sold commercially without further purification. Here, in this account, we present some recent advances in polyolefin hydrogenolysis, with a focus on Pt nanoparticles on SrTiO 3 nanocuboid supports (Pt/STO). Current developments in this field, general challenges, and future directions are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Explaining the structure sensitivity of Pt and Rh for aqueous-phase hydrogenation of phenol

Phenol is an important model compound to understand the thermocatalytic (TCH) and electrocatalytic hydrogenation (ECH) of biomass to biofuels. Although Pt and Rh are among the most studied catalysts for aqueous-phase phenol hydrogenation, the reason why certain facets are active for ECH and TCH is not fully understood. Herein, we identify the active facet of Pt and Rh catalysts for aqueous-phase hydrogenation of phenol and explain the origin of the size-dependent activity trends of Pt and Rh nanoparticles. Phenol adsorption energies extracted on the active sites of Pt and Rh nanoparticles on carbon by fitting kinetic data show that the active sites adsorb phenol weakly. We predict that the turnover frequencies (TOFs) for the hydrogenation of phenol to cyclohexanone on Pt(111) and Rh(111) terraces are higher than those on (221) stepped facets based on density functional theory modeling and mean-field microkinetic simulations. The higher activities of the (111) terraces are due to lower activation energies and weaker phenol adsorption, preventing high coverages of phenol from inhibiting hydrogen adsorption. We measure that the TOF for ECH of phenol increases as the Rh nanoparticle diameter increases from 2 to 10 nm at 298 K and -0.1 V vs the reversible hydrogen electrode, qualitatively matching prior reports for Pt nanoparticles. The increase in experimental TOFs as Pt and Rh nanoparticle diameters increase is due to a larger fraction of terraces on larger particles. These findings clarify the structure sensitivity and active site of Pt and Rh for the hydrogenation of phenol and will inform the catalyst design for the hydrogenation of bio-oils.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Hydrogen Adsorption over Transition Metals in Water

The adsorption free energy of atomic hydrogen on Pt(111), Pd(111), Ni(111), Ru(0001), Cu(111), and Rh(111) in liquid water was computed using a quantum mechanical/molecular mechanical free-energy perturbation scheme. The Pt(111) computations indicate that the solvent effect on H adsorption on atop sites (+0.20 eV) is almost twice that on fcc (+0.12 eV), showing it is less likely to find adsorbed hydrogen in atop position in the presence of water than in the gas phase. The solvent effect for the fcc site, which is the most favorable site for adsorbed H on Pt(111), agrees qualitatively with experimental work by Lercher et al., who reported an effect of +0.2 eV. Overall, an endergonic solvent effect for hydrogen adsorption is observed for all metals, indicating a lower hydrogen coverage relative to free site coverage at metal-water interfaces compared to metal-gas interfaces, even when hydrogen transport effects through the fluid phase are negligible; a result with important implications for (de)hydrogenation catalysis.

Zare, Mehdi↗

Double-Atom Catalysts Featuring Inverse Sandwich Structure for CO 2 Reduction Reaction: A Synergetic First-Principles and Machine Learning Investigation

Electrocatalytic CO 2 reduction reactions (CO 2 RR) based on scalable and highly efficient catalysis provide an attractive strategy for reducing CO 2 emissions. Here in this work, we combined first-principles density functional theory (DFT) and machine learning (ML) to comprehensively explore the potential of double-atom catalysts (DACs) featuring an inverse sandwich structure anchored on defective graphene (gra) to catalyze CO 2 RR to generate C 1 products. We started with five homonuclear M 2 ⊥gra (M = Co, Ni, Rh, Ir, and Pt), followed by 127 heteronuclear MM'⊥gra (M = Co, Ni, Rh, Ir, and Pt, M' = Sc–Au). Stable DACs were screened by evaluating their binding energy, formation energy, and dissolution potential of metal atoms, as well as conducting first-principles molecular dynamics simulations with and without solvent water molecules. Based on DFT calculations, Rh 2 ⊥gra DAC was found to outperform the other four homonuclear DACs and the Rh-based single- and double-atom catalysts of noninverse sandwich structures. Out of the 127 heteronuclear DACs, 14 were found to be stable and have good catalytic performance. An ML approach was adopted to correlate key factors with the activity and stability of the DACs, including the sum of radii of metal and ligand atoms (d M–M' , d M–C , and d M'–C ), the sum and difference of electronegativity of two metal atoms (P M + P M' , P M – P M '), the sum and difference of first ionization energy of two metal atoms (I M + I M' , I M – I M '), the sum and difference of electron affinity of two metal atoms (A M + A M' , A M – A M '), and the number of d-electrons of the two metal atoms (Nd). The obtained ML models were further used to predict 154 potential electrocatalysts out of 784 possible DACs featuring the same inverse sandwich configuration. Overall, this work not only identified promising CO 2 RR DACs featuring the reported inverse sandwich structure but also provided insights into key atomic characteristics associated with high CO 2 RR activity.

30 DIRECT ENERGY CONVERSION↗

Strain and ligand effects in Pt-Ni alloys studied by valence-to-core X-ray emission spectroscopy

Experimental detection of the Pt 5d densities of states in the valence band is conducted on a series of Pt-Ni alloys by high energy resolution valence-to-core X-ray emission spectroscopy (VTC-XES) at the Pt L 3 -edge. VTC-XES measurements reveal that the Pt d-band centroid shifts away from the Fermi level upon dilution, accompanied by concentration-dependent Pt d-band width. The competition between the strain effect and ligand effect is observed experimentally for the first time. It is found that the d-band widths in Pt 3 Ni and PtNi are broader than that of Pt metal due to compressive strain which overcompensates the effect of dilution, while it is narrower in PtNi 3 where the ligand effect dominates. VTC-XES is demonstrated to be a powerful tool to study the Pt d-band contribution to the valence band of Pt-based bimetallic. The implication for the enhanced activity of Pt-Ni catalysts in oxygen reduction reaction is discussed.

36 MATERIALS SCIENCE↗

A measure of active interfaces in supported catalysts for high-temperature reactions

Formulating knowledge of structure-function relationships in heterogeneous catalysis is central to the design of efficient catalysts; yet, the elucidation of dominant reaction sites has remained as a challenge. Here, in this paper, we present a methodology that can be used to visualize metal-gas and metal-oxide-gas interfaces in three dimensions and to quantify their catalytic activity levels. As a case study, CH 4 oxidation occurring in a Pt/CeO 2 system is chosen. By employing thermally robust Pt@CeO 2 model catalysts with size-tunable and monodisperse cores, and gas-permeable shells, we reconstruct a series of structures in 3D via electron tomography and match the information to activity data and theoretical calculations. This strategy reveals that two different interfaces catalyze the CH 4 oxidation and that their contribution to the overall rate changes with the Pt size, temperature, and gas atmosphere. Our results provide an analytic platform on which to explore reaction pathways and mechanisms applicable to multiple reactions and materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Application of machine learning to discover new intermetallic catalysts for the hydrogen evolution and the oxygen reduction reactions

The adsorption energies for hydrogen, oxygen, and hydroxyl were calculated by means of density functional theory on the lowest energy surface of 24 pure metals and 332 binary intermetallic compounds with stoichiometries AB, A 2 B, and A 3 B taking into account the effect of biaxial elastic strains. This information was used to train two random forest regression models, one for the hydrogen adsorption and another for the oxygen and hydroxyl adsorption, based on 9 descriptors that characterized the geometrical and chemical features of the adsorption site as well as the applied strain. All the descriptors for each compound in the models could be obtained from physico-chemical databases. The random forest models were used to predict the adsorption energy for hydrogen, oxygen, and hydroxyl of ≈2700 binary intermetallic compounds with stoichiometries AB, A 2 B, and A 3 B made of metallic elements, excluding those that were environmentally hazardous, radioactive, or toxic. This information was used to search for potential good catalysts for the HER and ORR from the criteria that their adsorption energy for H and O/OH, respectively, should be close to that of Pt. Further, this investigation shows that the suitably trained machine learning models can predict adsorption energies with an accuracy not far away from density functional theory calculations with minimum computational cost from descriptors that are readily available in physico-chemical databases for any compound. Moreover, the strategy presented in this paper can be easily extended to other compounds and catalytic reactions, and is expected to foster the use of ML methods in catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A deep learning approach for semantic segmentation of unbalanced data in electron tomography of catalytic materials

In computed TEM tomography, image segmentation represents one of the most basic tasks with implications not only for 3D volume visualization, but more importantly for quantitative 3D analysis. In case of large and complex 3D data sets, segmentation can be an extremely difficult and laborious task, and thus has been one of the biggest hurdles for comprehensive 3D analysis. Heterogeneous catalysts have complex surface and bulk structures, and often sparse distribution of catalytic particles with relatively poor intrinsic contrast, which possess a unique challenge for image segmentation, including the current state-of-the-art deep learning methods. To tackle this problem, we apply a deep learning-based approach for the multi-class semantic segmentation of a γ-Alumina/Pt catalytic material in a class imbalance situation. Specifically, we used the weighted focal loss as a loss function and attached it to the U-Net’s fully convolutional network architecture. We assessed the accuracy of our results using Dice similarity coefficient (DSC), recall, precision, and Hausdorff distance (HD) metrics on the overlap between the ground-truth and predicted segmentations. Our adopted U-Net model with the weighted focal loss function achieved an average DSC score of 0.96 ± 0.003 in the γ-Alumina support material and 0.84 ± 0.03 in the Pt NPs segmentation tasks. We report an average boundary-overlap error of less than 2 nm at the 90th percentile of HD for γ-Alumina and Pt NPs segmentations. The complex surface morphology of γ-Alumina and its relation to the Pt NPs were visualized in 3D by the deep learning-assisted automatic segmentation of a large data set of high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) tomography reconstructions.

36 MATERIALS SCIENCE↗

Synthesis of Pt 3 Zn 1 and Pt 1 Zn 1 intermetallic nanocatalysts for dehydrogenation of ethane

Pt 3 Zn 1 and Pt 1 Zn 1 intermetallic nanoparticles supported on SiO 2 were synthesized by combining atomic layer deposition (ALD) of ZnO, incipient wetness impregnation (IWI) of Pt, and appropriate hydrogen reduction. Here, the formation of Pt 1 Zn 1 and Pt 3 Zn 1 intermetallic nanoparticles was observed by both X-ray diffraction (XRD) and synchrotron X-ray absorption spectroscopy (XAS). STEM images showed that the 2–3 nm Pt-based intermetallic nanoparticles were uniformly dispersed on a SiO 2 support. The relationships between Pt–Zn intermetallic phases and synthesis conditions were established. In situ XAS measurements at Pt L 3 and Zn K edges during hydrogen reduction provided a detailed image of surface species evolution. Owing to a combined electronic and geometric effect, Pt 1 Zn 1 exhibited much higher reactivity and stability than Pt 3 Zn 1 and Pt in both the direct dehydrogenation and oxidative dehydrogenation of ethane to ethylene reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Activity of Silica-Alumina for the Conversion of Polyethylene into Tunable Aromatics Below Pyrolytic Temperatures

Plastic waste is a mounting problem that lacks global strategy, largely due to inadequate recycling capabilities. One alternative gaining traction in the heterogeneous catalysis community is the upcycling of polyolefins into value-added products, such as hydrogen-free conversion to alkylaromatic compounds over Pt/Al 2 O 3 . Here, we examined the activity of nominally metal-free, mesoporous silica-alumina mixed oxide materials (SiO 2 –Al 2 O 3 ) for the conversion of polyethylene into aromatic compounds at temperatures of and below 280 °C. Yields with the silica-alumina catalysts are comparable to those obtained over Pt(1 wt %)/Al 2 O 3 under identical conditions, and product selectivity can be tuned by altering reaction conditions or the acid site density of the SiO 2 –Al 2 O 3 . Notably, the fraction of polyaromatic products increases with the Brønsted acid site density of the catalyst, as does the degree of polymer deconstruction. These catalysts can be reused without regeneration, and their activity improves with each recycling event, producing soluble product yields up to 83%. Preliminary work on the mechanism of the reaction suggests that acid sites are responsible for initiating depolymerization and aromatization reactions, in analogy to previous work in the literature. Finally, this work showcases the activity of SiO 2 –Al 2 O 3 for polyolefin deconstruction/aromatization at subpyrolytic temperatures and lays the foundation for future studies involving solid acid and bifunctional catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Electron donation of non-oxide supports boosts O 2 activation on nano-platinum catalysts

Activation of O 2 is a critical step in heterogeneous catalytic oxidation. Here, the concept of increased electron donors induced by nitrogen vacancy is adopted to propose an efficient strategy to develop highly active and stable catalysts for molecular O 2 activation. Carbon nitride with nitrogen vacancies is prepared to serve as a support as well as electron sink to construct a synergistic catalyst with Pt nanoparticles. Extensive characterizations combined with the first-principles calculations reveal that nitrogen vacancies with excess electrons could effectively stabilize metallic Pt nanoparticles by strong p-d coupling. The Pt atoms and the dangling carbon atoms surround the vacancy can synergistically donate electrons to the antibonding orbital of the adsorbed O 2 . This synergistic catalyst shows great enhancement of catalytic performance and durability in toluene oxidation. The introduction of electron-rich non-oxide substrate is an innovative strategy to develop active Pt-based oxidation catalysts, which could be conceivably extended to a variety of metal-based catalysts for catalytic oxidation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Study of the Hydrogen Pretreatment of Gallium and Platinum Promoted ZSM-5 for the Ethane Dehydroaromatization Reaction

Here, the performances of gallium and platinum promoted ZSM-5 catalysts, fresh and reduced, were studied for the dehydroaromatization of ethane to aromatics. Fresh and reduced 2% Ga/ZSM-5, 1.5% Ga–0.5% Pt/ZSM-5, and 0.5% Pt/ZSM-5 were tested in a fixed-bed reactor system at 615 °C for 2 h time on stream. It was observed that the hydrogen reduction of the fresh Pt-containing catalysts led to an increase in catalytic conversion. The addition of platinum to the gallium catalyst resulted in an increase in the reducibility of the Ga species on the surface of the zeolite. The GaPt catalysts exhibited an increase in ethane conversion and the stability of aromatic production selectivity over the Ga and Pt catalysts. The as-prepared fresh catalysts were characterized by ICP and BET methods. The fresh and reduced catalysts were further analyzed by XRD, H 2 -TPR, XPS, pyridine DRIFTS, TGA, and TEM.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Ensembles of Metastable States Govern Heterogeneous Catalysis on Dynamic Interfaces

Heterogeneous catalysis is at the heart of chemical industry. Being able to tune and design efficient catalysts for processes of interest is of an utmost importance, and for this, the molecular-level understanding of heterogeneous catalysts is the first step, and indeed a prime focus of the modern catalysis research. For a long time, a single most thermodynamically stable structure of the catalytic interface attained in reaction conditions had been envisioned as the reactive phase. However, some catalytic interfaces continue to undergo structural dynamics in the steady state, triggered by high temperatures, pressures, and binding and changing reagents. Among particularly dynamic interfaces are such widely-used catalysts as crystalline and amorphous surfaced supporting (sub-)nano metallic clusters. Recently, it became clear that this dynamic fluxionality causes the supported clusters to populate many distinct structural and stoichiometric states in catalytic conditions. Hence, the catalytic interface should be viewed as an evolving statistical ensemble of many (not one) structures. As such, every member in the ensemble contributes to the properties of the catalyst differently, and in proportion to its probability to be populated. This new notion flips the established paradigm and calls for new theory, modeling approaches, operando measurements, and updated design strategies. The statistical ensemble nature of surface-supported sub-nano cluster catalysts can be exemplified by oxide-supported and adsorbate-covered Pt, Pd, Cu, CuPd clusters, catalytic toward oxidative and non-oxidative dehydrogenation. They have access to a variety of 3D and quasi-2D shapes. The compositions of their thermal ensembles are dependent on the cluster size, leading to size-specific catalytic activities and the famous “every atom counts” phenomenon. The support and adsorbates affect catalyst structures, and state of the reacting species causes the ensemble to change in every reaction intermediate. The most stable member of the ensemble dominates the thermodynamic properties of the corresponding intermediate, whereas the kinetics can be determined by more active but less populated metastable catalyst states, and that suggests that many earlier studies might have overlooked the actual active sites. Both effects depend on the relative time-scales of catalyst restructuring and reaction dynamics. The catalyst may routinely operate off-equilibrium. Ensemble phenomena lead to surprising exceptions from established rules of catalysis, such as scaling relations, and Arrhenius behavior. Catalyst deactivation is also an ensemble property, and its extent of mitigation can be predicted through the new paradigm. These findings were enabled by advances in theory, such as global optimization and subsequent utilization of multiple local minima, and pathways sampling, as well as operando catalyst characterization. The fact that the per-site and per-species resolution is needed for the description and predicting of catalyst properties gives theory the central role in catalysis research, as most experiments provide ensemble-average information and cannot detect the crucial minority species that may be responsible for the catalytic activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Distinct Kinetic Signatures of Photodesorption from Metal Nanoparticles

Visible photon fluxes can influence the rate and selectivity of heterogeneously catalyzed reactions on metal nanoparticle surfaces. Models describing the influence of photon fluxes have typically introduced photon flux dependent apparent thermal kinetic parameters (reaction orders, activation energies, binding energies, etc.). This has relied on empirical fitting of reaction rate data, making mechanistic interpretations of how photon fluxes influence elementary step rates challenging and inconsistent with fundamental descriptions of photochemistry on metal surfaces developed from surface science studies. Using the CO adsorption–desorption quasi-equilibrium reaction on Pt/Al 2 O 3 catalysts as a model system, we measured steady state adsorbed CO (CO*) coverages under isothermal and isobaric (1 mbar CO) conditions as a function of temperature (473–573 K) and of 440 nm photon flux ((0.1–5.2) × 10 3 # hv Pt site –1 s –1 ) using in situ IR spectroscopy. Steady state CO* coverage on Pt was photon flux dependent with increasing photon flux causing decreasing coverage, consistent with photons driving CO* desorption rates faster than thermal CO* desorption rates. However, photon flux dependent CO* coverages were essentially temperature independent, inconsistent with models that describe photon effects using perturbations to apparent thermal kinetic parameters. Instead, 120 steady state CO* coverages as a function of temperature and photon flux are quantitatively described by a kinetic model in which the overall desorption rate is a summation of independent thermal and photon induced CO* desorption rates. Site-resolved analysis reveals distinct kinetic parameters for photon driven desorption of CO* from well-coordinated, under-coordinated, and highly under-coordinated Pt sites, with temperature-dependent apparent quantum efficiencies (AQE) consistent with temperature dependence of vibrational quanta distribution of adsorbed CO. The rigorous kinetic rate laws for independent photon and thermal driven pathways allow for predictive modeling of the influence of photon fluxes on the rates of CO* desorption under catalytic conditions. Further, the analysis provides evidence that steady state continuous wave photon fluxes can drive desorption/adsorption reactions on metal surfaces out of thermal equilibrium, reconciling surface science observations of molecular photodesorption with applied catalysis. The work establishes a general kinetic framework to be considered for photon driven processes on metals, and defines catalyst, reaction, and photon flux characteristic design principles for breaking Sabatier limitations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

CH 4 steam reforming on Pt + Pd/Al 2 O 3 monolith: impact of Mn 0.5 Fe 2.5 O 4 spinel addition

The performance of Pt + Pd/Al 2 O 3 monolith catalysts was analyzed under a feed (H 2 + CO + CH 4 + NO + O 2 + H 2 O + CO 2 ) simulating the exhaust gas of stoichiometric natural gas vehicles (NGVs). The flow reactor results show enhanced low temperature (<400 °C) CH 4 oxidation activity under cyclic lean/rich feed modulation and with Mn 0.5 Fe 2.5 O 4 (MFO) spinel addition to the platinum group metal (PGM) catalyst. In the absence of the MFO spinel, the PGM catalyst can achieve full CH 4 conversion, but the presence of spinel in the catalyst limits the high temperature (>500 °C) CH 4 conversion. The depletion of gas phase O 2 , as indicated by the detection of products CO and H 2 in the reactor effluent at higher temperature and CH 4 conversion, portends the emergence of steam reforming of CH 4 (SRM). To investigate the cause of the detrimental impact of spinel under the high temperature, net rich regime, we show through a combination of SRM experiments and post-reaction catalyst characterization that SRM is negatively impacted by the accumulation of Mn and Fe spinel species on the PGM catalyst. This is due to the migration of the base metal species and potential encapsulation and blockage of PGM active sites. The latter is supported by density functional theory (DFT) calculations that indicate favorable metal oxide decoration of active Pt step sites. Lastly, a zoned catalyst design is developed that mitigates the detrimental metals migration while exploiting the methane conversion enhancement afforded by spinel oxides under oxygen rich conditions.

03 NATURAL GAS↗

Azo(xy) vs Aniline Selectivity in Catalytic Nitroarene Reduction by Intermetallics: Experiments and Simulations

Intermetallic nanoparticles are promising catalysts in hydrogenation and fuel cell technologies. Much is known about the ability of intermetallic nanoparticles to selectively reduce nitro vs alkene, alcohol, or halide functional groups; less is known about their selectivity toward aniline vs azo or azoxy condensation products that result from the reduction of a nitro group alone. Because azo(xy)arenes bear promise as dyes, chemical stabilizers, and building blocks to functional materials but can be difficult to isolate, developing high surface area nanoparticle catalysts that display azo(xy) selectivity is desirable. To address this question, we studied a family of nanocrystalline group 10 metal (Pd, Pt)- and group 14 metal (Ge, Sn, Pb)-containing intermetallics-Pd 2 Ge, Pd 2 Sn, Pd 3 Sn 2 , Pd 3 Pb, and PtSn-in the catalytic reduction of nitroarenes. In contrast to monometallic Au, Pt, and Pd nanoparticles and "random" PdxSn1 – x nanoalloys, which are selective for aniline, nanoparticles of atomically precise intermetallic Pd 2 Ge, Pd 2 Sn, Pd 3 Sn 2 , and PtSn prefer an indirect condensation pathway and have a high selectivity for the azo(xy) products. The only exception is Pd 3 Pb, the most active among the intermetallic nanoparticles studied here, which is instead selective for aniline. Employing a novel application of molecular dynamics-based on machine learned potentials within a DeePMD framework-to heterogeneous catalysis, we are able to identify key reaction species on the different types of catalysts employed, furthering our understanding of the unique selectivity of these materials. By demonstrating how intermetallic nanoparticles can be as active yet more selective than other more traditional catalysts, this work provides new physical insights and opens new opportunities in the use of these materials in other important chemical transformations and applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt

Abstract Atomistic modeling of chemically reactive systems has so far relied on either expensive ab initio methods or bond-order force fields requiring arduous parametrization. Here, we describe a Bayesian active learning framework for autonomous “on-the-fly” training of fast and accurate reactive many-body force fields during molecular dynamics simulations. At each time-step, predictive uncertainties of a sparse Gaussian process are evaluated to automatically determine whether additional ab initio training data are needed. We introduce a general method for mapping trained kernel models onto equivalent polynomial models whose prediction cost is much lower and independent of the training set size. As a demonstration, we perform direct two-phase simulations of heterogeneous H 2 turnover on the Pt(111) catalyst surface at chemical accuracy. The model trains itself in three days and performs at twice the speed of a ReaxFF model, while maintaining much higher fidelity to DFT and excellent agreement with experiment.

42 ENGINEERING↗