Atomically dispersed supported metal catalysts offer new properties and the benefits of maximized metal accessibility and utilization. The characterization of these materials, however, remains challenging. Using atomically-dispersed Pt supported on crystalline MgO (chosen for its well-defined bonding sites for Pt) as a prototypical example, in this work, we demonstrate how systematic density functional theory calculations (for assessing all the potentially stable Pt sites) combined with automated EXAFS analysis can lead to unbiased identification of isolated, surfaceenveloped platinum cations as the catalytic species for CO oxidation. The catalyst has been characterized by atomic-resolution imaging, EXAFS, and HERFD-XANES spectroscopies; the proposed Pt site are in full agreement with experiment. This theory-guided workflow leads to rigorously determined structural models and provides a more detailed picture of the structure of the catalytically active sites than what is currently possible with conventional EXAFS analysis. As this approach is efficient and agnostic to the metal, support, and catalytic reaction, we posit that it will be of broad interest to the materials characterization and catalysis communities.

It has been well-established that unfavorable scaling relationships between *OOH, *OH, and *O are responsible for the high overpotentials associated with oxygen electrochemistry. A number of strategies have been proposed for breaking these linear constraints for traditional electrocatalysts (e.g., metals, alloys, metal-doped carbons); such approaches have not yet been validated experimentally for heterogeneous catalysts. Development of a new class of catalysts capable of circumventing such scaling relations remains an ongoing challenge in the field. In this work, we use density functional theory (DFT) calculations to demonstrate that bimetallic porphyrin-based MOFs (PMOFs) are an ideal materials platform for rationally designing the 3-D active site environments for oxygen reduction reaction (ORR). Specifically, we show that the *OOH binding energy and the theoretical limiting potential can be optimized by appropriately tuning the transition metal active site, the oxophilic spectator, and the MOF topology. Our calculations predict theoretical limiting potentials as high as 1.07 V for Fe/Cr-PMOF-Al, which exceeds the Pt/C benchmark for 4e ORR. More broadly, by highlighting their unique characteristics, this work aims to establish bimetallic porphyrin-based MOFs as a viable materials platform for future experimental and theoretical ORR studies.

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.

It has been well-established that unfavorable scaling relationships between *OOH, *OH, and *O are responsible for the high overpotentials associated with oxygen electrochemistry. A number of strategies have been proposed for breaking these linear constraints for traditional electrocatalysts (e.g. metals, alloys, metal-doped carbons); such approaches have not yet been validated experimentally for heterogenous catalysts. Development of a new class of catalysts capable of circumventing such scaling relations remains an ongoing challenge in the field. In this work, we use density functional theory (DFT) calculations to demonstrate that bimetallic porphyrin-based MOFs (PMOFs) are an ideal materials platform for rationally-designing the 3D active site environments for oxygen reduction reaction (ORR). Specifically, we show that the *OOH binding energy and the theoretical limiting potential can be optimized by appropriately tuning the transition metal active site, the oxophilic spectator, and the MOF topology. Our calculations predict theoretical limiting potentials as high as 1.07 V for Fe/Cr-PMOF-Al, which exceeds the Pt/C benchmark for 4e ORR. More broadly, by highlighting their unique characteristics, this works aims to establish bimetallic porphyrin-based MOFs as a viable materials platform for future experimental and theoretical ORR studies.

Mycobacteria synthesize intracellular, 6-O-methylglucose–containing lipopolysaccharides (mGLPs) proposed to modulate bacterial fatty acid metabolism. Recently, it has been shown that Mycobacterium tuberculosis mGLP specifically induces a specific subset of protective γ9δ2 T cells. Mild base treatment, which removes all the base-labile groups, reduces the specific activity of mGLP required for induction of these T cells, suggesting that acylation of the saccharide moieties is required for γ9δ2 T-cell activation. On the basis of this premise, we used analytical LC/MS and NMR methods to identify and locate the acyl functions on the mGLP saccharides. We found that mGLP is heterogeneous with respect to acyl functions and contains acetyl, isobutyryl, succinyl, and octanoyl groups and that all acylations in mGLP, except for succinyl and octanoyl residues, reside on the glucosyl residues immediately following the terminal 3-O-methylglucose. Our analyses also indicated that the octanoyl residue resides at position 2 of an internal glucose toward the reducing end. LC/MS analysis of the residual product obtained by digesting the mGLP with pancreatic α-amylase revealed that the product is an oligosaccharide terminated by α-(1→4)–linked 6-O-methyl-d-glucosyl residues. This oligosaccharide retained none of the acyl groups, except for the octanoyl group, and was unable to induce protective γ9δ2 T cells. This observation confirmed that mGLP induces γ9δ2 T cells and indicated that the acylated glucosyl residues at the nonreducing terminus of mGLP are required for this activity.

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.

Machine learning potentials (MLPs) capable of accurately describing complex ab initio potential energy surfaces (PES) have revolutionized the field of multiscale atomistic modeling. In this work, using an extensive density functional theory (DFT) dataset (denoted as Si-ZEO22) consisting of 187 unique silica topologies 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 motivates further MLP development for nanoporous materials with near-ab initio accuracy.

Natural gas remains an essential energy source for the industrial and residential sectors. However, selective valorization of methane (the main component of natural gas) into more mobile liquid energy carriers such as methanol remains challenging. Inspired by pMMO enzymes, many recent studies have examined Cu-exchanged zeolites as promising catalysts, specifically through [CuOCu]2+ sites. These efforts, in part, have been motivated by the possibility of finding an elusive “Goldilocks” active site or topology that can outperform known catalysts while also maintaining selectivity towards methanol. As large-scale experiments with 1000s of material variations are impossible, theory will likely play an important role. Although computational screening studies are now routine for metals and alloys, similar studies for zeolites are not as straightforward due to the diversity of local chemical environments, and the aforementioned studies are not trivial using the traditional density functional theory (DFT)-based approach. Therefore, the overarching goal of this study is to leverage large-scale DFT calculations to develop a reactive machine learning-based potential (rMLP) capable of systematically sampling the stability and reactivity of all [CuOCu]2+ sites within a representative set of zeolites. Specifically, using methane activation as a prototypical example of an industrially relevant zeolite-catalyzed reaction, we have developed a novel multistage active learning algorithm that preferentially samples the potential energy surface of the system near the transition state of methane activation. We show that the resulting rMLP replaces the expensive DFT-based NEB calculations without any appreciable loss in accuracy (within 0.07 eV of the DFT computed energy barriers) – we evaluate C-H bond activation energies for 5,400 distinct sites across 52 zeolites and obtain 3,356 valid sites suitable for methane activation. By replacing the expensive DFT-based NEB calculations with rMLPs, we now report an exhaustive high-throughput screening study of thousands of [CuOCu]2+ sites in zeolites, comparing the maximum rates of methane activation across 52 zeolite topologies and more than 3,000 sites. To the best of our knowledge, this work represents the first example of using reactive MLPs to identify the transition state geometries and screen the catalytic performance of thousands of zeolite-based active sites at DFT accuracies.