Screening Cu-Zeolites for Methane Activation Using Curriculum-Based Training

Machine learning (ML), when used synergistically with atomistic simulations, has recently emerged as a powerful tool for accelerated catalyst discovery. However, the application of these techniques has been limited by the lack of interpretable and transferable ML models. In this work, we propose a curriculum-based training (CBT) philosophy to systematically develop reactive machine learning potentials (rMLPs) for high-throughput screening of zeolite catalysts. Our CBT approach combines several different types of calculations to gradually teach the ML model about the relevant regions of the reactive potential energy surface. The resulting rMLPs are accurate, transferable, and interpretable. We further demonstrate the effectiveness of this approach by exhaustively screening thousands of [CuOCu]2+ sites across hundreds of Cu-zeolites for the industrially relevant methane activation reaction. Specifically, this large-scale analysis of the entire International Zeolite Association (IZA) database identifies a set of previously unexplored zeolites (i.e., MEI, ATN, EWO, and CAS) that show the highest ensemble-averaged rates for [CuOCu]2+-catalyzed methane activation. We believe that this CBT philosophy can be generally applied to other zeolite-catalyzed reactions and, subsequently, to other types of heterogeneous catalysts. Thus, this represents an important step toward overcoming the long-standing barriers within the computational heterogeneous catalysis community

Direct Methane to Methanol: The Selectivity–Conversion Limit and Design Strategies

Currently, methane is transformed into methanol through the two-step syngas process, which requires high temperatures and centralized production. While the slightly exothermic direct partial oxidation of methane to methanol would be preferable, no such process has been established despite over a century of research. Generally, this failure has been attributed to both the high barriers required to activate methane as well as the higher activity of the CH bonds in methanol compared to those in methane. However, a precise and general quantification of the limitations of catalytic direct methane to methanol has yet to be established. Herein, we present a simple kinetic model to explain the selectivity–conversion trade-off that hampers continuous partial oxidation of methane to methanol. For the same kinetic model, we apply two distinct methods, (1) using ab initio calculations and (2) fitting to a large experimental database, to fully define the model parameters. We find that both methods yield strikingly similar results, namely, that the selectivity of methane to methanol in a direct, continuous process can be fully described by the methane conversion, the temperature, and a catalyst-independent difference in methane and methanol activation free energies, Δ<i>G</i>^a, which is dictated by the relative reactivity of the C–H bonds in methane and methanol. Stemming from this analysis, we suggest several design strategies for increasing methanol yields under the constraint of constant Δ<i>G</i>^a. These strategies include (1) “collectors”, materials with strong methanol adsorption potential that can help to lower the partial pressure of methanol in the gas phase, (2) aqueous reaction conditions, and/or (3) diffusion-limited systems. By using this simple model to successfully rationalize a representative library of experimental studies from the diverse fields of heterogeneous, homogeneous, biological, and gas-phase methane to methanol catalysis, we underscore the idea that continuous methane to methanol is generally limited and provide a framework for understanding and evaluating new catalysts and processes

Direct Methane to Methanol: The Selectivity–Conversion Limit and Design Strategies

Currently, methane is transformed into methanol through the two-step syngas process, which requires high temperatures and centralized production. While the slightly exothermic direct partial oxidation of methane to methanol would be preferable, no such process has been established despite over a century of research. Generally, this failure has been attributed to both the high barriers required to activate methane as well as the higher activity of the CH bonds in methanol compared to those in methane. However, a precise and general quantification of the limitations of catalytic direct methane to methanol has yet to be established. Herein, we present a simple kinetic model to explain the selectivity–conversion trade-off that hampers continuous partial oxidation of methane to methanol. For the same kinetic model, we apply two distinct methods, (1) using ab initio calculations and (2) fitting to a large experimental database, to fully define the model parameters. We find that both methods yield strikingly similar results, namely, that the selectivity of methane to methanol in a direct, continuous process can be fully described by the methane conversion, the temperature, and a catalyst-independent difference in methane and methanol activation free energies, Δ<i>G</i>^a, which is dictated by the relative reactivity of the C–H bonds in methane and methanol. Stemming from this analysis, we suggest several design strategies for increasing methanol yields under the constraint of constant Δ<i>G</i>^a. These strategies include (1) “collectors”, materials with strong methanol adsorption potential that can help to lower the partial pressure of methanol in the gas phase, (2) aqueous reaction conditions, and/or (3) diffusion-limited systems. By using this simple model to successfully rationalize a representative library of experimental studies from the diverse fields of heterogeneous, homogeneous, biological, and gas-phase methane to methanol catalysis, we underscore the idea that continuous methane to methanol is generally limited and provide a framework for understanding and evaluating new catalysts and processes

Control of Metal–Organic Framework Crystal Topology by Ligand Functionalization: Functionalized HKUST‑1 Derivatives

Metal–organic framework (MOF) materials are nanoporous crystals that have attracted intense interest in the fields of adsorption and catalysis. MOFs are interesting in part because of the concept of reticular synthesis, which allows families of isostructural crystals to be developed by varying ligand length and functionality. The predictability associated with MOF crystal structures is complicated in situations where ligand functionalization leads to new crystal structures. In this work, we show experimentally how the crystal structures of derivatives of HKUST-1 (Cu-BTC) vary for a set of six functionalized ligands: methyl, ethyl, methoxy, bromo, nitro, and acetamide. Our experiments indicate that synthesizing MOFs with these functionalized ligands leads to multiple distinct crystal structures. We further show that these structures can be rationalized computationally using density functional theory (DFT) and electron localization function (ELF) calculations. This analysis led to a simple “design principle” for predicting the structure using the bonding characteristics of the functional groups to BTC linkers. This heuristic, in combination with the more detailed calculations of the kind we have presented, will be useful in future efforts to predictively control the crystal structure of similar MOF materials