This work aims to address the challenge of developing interpretable ML-based models when access to large-scale computational resources is limited. Using CoMoFeNiCu high-entropy alloy catalysts as an example, we present a cost-effective workflow that synergistically combines descriptor-based approaches, machine learning-based force fields, and low-cost density functional theory (DFT) calculations to predict high-quality adsorption energies for H, N, and NH_x (x = 1, 2, and 3) adsorbates. This is achieved using three specific modifications to typical DFT workflows including: (1) using a sequential optimization protocol, (2) developing a new geometry-based descriptor, and (3) repurposing the already-available low-cost DFT optimization trajectories to develop a ML-FF. Taken together, this study illustrates how cost-effective DFT calculations and appropriately designed descriptors can be used to develop cheap but useful models for predicting high-quality adsorption energies at significantly lower computational costs. We anticipate that this resource-efficient philosophy may be broadly relevant to the larger surface catalysis community.

Machine learning potentials (MLPs) capable of accurately describing complex ab initio potential energy surfaces (PESs) have revolutionized the field of multiscale atomistic modeling. In this work, using an extensive density functional theory (DFT) data set (denoted as Si-ZEO22) consisting of 219 unique zeolite topologies (350,000 unique DFT calculations) found in the International Zeolite Association (IZA) database, we have trained a DeePMD-kit MLP to model the dynamics of silica frameworks. The performance of our model is evaluated by calculating various properties that probe the accuracy of the energy and force predictions. This MLP demonstrates impressive agreement with DFT for predicting zeolite structural properties, energy-volume trends, and phonon density of states. Furthermore, our model achieves reasonable predictions for stress-strain relationships without including DFT stress data during training. These results highlight the ability of MLPs to capture the flexibility of zeolite frameworks and motivate further MLP development for nanoporous materials with near-ab initio accuracy.

While the search for catalysts capable of directly converting methane to higher value commodity chemicals and liquid fuels has been active for over a century, a viable industrial process for selective methane activation has yet to be developed. Electronic structure calculations are playing an increasingly relevant role in this search, but large-scale materials screening efforts are hindered by computationally expensive transition state barrier calculations. The purpose of the present letter is twofold. First, we show that, for the wide range of catalysts that proceed via a radical intermediate, a unifying framework for predicting C-H activation barriers using a single universal descriptor can be established. Second, we combine this scaling approach with a thermodynamic analysis of active site formation to provide a map of methane activation rates. Our model successfully rationalizes the available empirical data and lays the foundation for future catalyst design strategies that transcend different catalyst classes.

Isolated platinum(II) ions anchored at acid sites in the pores of zeolite HZSM-5, initially introduced by aqueous ion exchange, were reduced to form platinum nanoparticles that are stably dispersed with a narrow size distribution (1.3 ± 0.4 nm in average diameter). The nanoparticles were confined in reservoirs within the porous zeolite particles, as shown by electron beam tomography and the shape-selective catalysis of alkene hydrogenation. When the nanoparticles were oxidatively fragmented in dry air at elevated temperature, platinum returned to its initial in-pore atomically dispersed state with a charge of +2, as shown previously by X-ray absorption spectroscopy. The results determine the conditions under which platinum is retained within the pores of HZSM-5 particles during redox cycles that are characteristic of the reductive conditions of catalyst operation and the oxidative conditions of catalyst regeneration.

Atomically dispersed metals on metal oxide supports are a rapidly growing class of catalysts. Developing an understanding of where and how the metals are bonded to the supports is challenging because support surfaces are heterogeneous, and most reports lack a detailed consideration of these points. Herein, we report two atomically dispersed CO oxidation catalysts having markedly different metal-support interactions: platinum in the first layer of crystalline MgO powder and platinum in the second layer of this support. Structural models have been determined on the basis of data and computations, including those determined by extended X-ray absorption fine structure and X-ray absorption near edge structure spectroscopies, infrared spectroscopy of adsorbed CO, and scanning transmission electron microscopy. The data demonstrate the transformation of surface to subsurface platinum as the temperature of sample calcination increased. Catalyst performance data demonstrate the lower activity but greater stability of the subsurface platinum than of the surface platinum.

Supported noble metal catalysts, ubiquitous in chemical technology, often undergo dynamic transformations between reduced and oxidized states-which influence the metal nuclearities, oxidation states, and catalytic properties. In this investigation, we report the results of in situ X-ray absorption spectroscopy, scanning transmission electron microscopy, and other physical characterization techniques, bolstered by density functional theory, to elucidate the structural transformations of a set of MgO-supported palladium catalysts under oxidative treatment conditions. As the calcination temperature increased, the as-synthesized supported metallic palladium nanoparticles underwent oxidation to form palladium oxides (at approximately 400 °C), which, at approximately 500 °C, were oxidatively fragmented to form mixtures of atomically dispersed palladium cations. The data indicate two distinct types of atomically dispersed species: palladium cations located at MgO steps and those embedded in the first subsurface layer of MgO. The former exhibit significantly higher (>500 times) catalytic activity for ethylene hydrogenation than the latter. The results pave the way for designing highly active and stable supported palladium hydrogenation catalysts with optimized metal utilization.