Performance of Metal-Catalyzed Hydrodebromination of Dibromomethane Analyzed by Descriptors Derived from Statistical Learning

Identifying Descriptors for Promoted Rhodium-Based Catalysts for Higher Alcohol Synthesis via Machine Learning

Rhodium-based catalysts offer remarkable selectivities toward higher alcohols, specifically ethanol, via syngas conversion. However, the addition of metal promoters is required to increase reactivity, augmenting the complexity of the system. Herein, we present an interpretable machine learning (ML) approach to predict and rationalize the performance of Rh-Mn-P/SiO2 catalysts (P = 19 promoters) using the open-source dataset on Rh-catalyzed higher alcohol synthesis (HAS) from Pacific Northwest National Laboratory (PNNL). A random forest model trained on this dataset comprising 19 alkali, transition, post-transition metals, and metalloid promoters, using catalytic descriptors and reaction conditions, predicts the higher alcohols space-time yield (STYHA) with an accuracy of R 2 = 0.76. The promoter's cohesive energy and alloy formation energy with Rh are revealed as significant descriptors during posterior feature-importance analysis. Their interplay is captured as a dimensionless property, coined promoter affinity index (PAI), which exhibits volcano correlations for space-time yield. Based on this descriptor, we develop guidelines for the rational selection of promoters in designing improved Rh-Mn-P/SiO2 catalysts. This study highlights ML as a tool for computational screening and performance prediction of unseen catalysts and simultaneously draws insights into the property-performance relations of complex catalytic systems.

Machine Learning for the Discovery, Design, and Engineering of Materials

Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based modes, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward (a) the discovery of new materials through large-scale enumerative screening, (b) the design of materials through identification of rules and principles that govern materials properties, and (c) the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 13 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior of the metal-organic bond, while very tunable for achieving target properties, is challenging to predict and necessitates searching a wide and complex space to identify needles in haystacks for target applications. This review will focus on the techniques that make high-throughput search of transition-metal chemical space feasible for the discovery of complexes with desirable properties. The review will cover the development, promise, and limitations of "traditional" computational chemistry (i.e., force field, semiempirical, and density functional theory methods) as it pertains to data generation for inorganic molecular discovery. The review will also discuss the opportunities and limitations in leveraging experimental data sources. We will focus on how advances in statistical modeling, artificial intelligence, multiobjective optimization, and automation accelerate discovery of lead compounds and design rules. The overall objective of this review is to showcase how bringing together advances from diverse areas of computational chemistry and computer science have enabled the rapid uncovering of structure-property relationships in transition-metal chemistry. We aim to highlight how unique considerations in motifs of metal-organic bonding (e.g., variable spin and oxidation state, and bonding strength/nature) set them and their discovery apart from more commonly considered organic molecules. We will also highlight how uncertainty and relative data scarcity in transition-metal chemistry motivate specific developments in machine learning representations, model training, and in computational chemistry. Finally, we will conclude with an outlook of areas of opportunity for the accelerated discovery of transition-metal complexes.

Nuclearity and Host Effects of Carbon-Supported Platinum Catalysts for Dibromomethane Hydrodebromination

The identification of the active sites and the derivation of structure-performance relationships are central for the development of high-performance heterogeneous catalysts. Here, a platform of platinum nanostructures, ranging from single atoms to nanoparticles of ≈4 nm supported on activated- and N-doped carbon (AC and NC), is employed to systematically assess nuclearity and host effects on the activity, selectivity, and stability in dibromomethane hydrodebromination, a key step in bromine-mediated methane functionalization processes. For this purpose, catalytic evaluation is coupled to in-depth characterization, kinetic analysis, and mechanistic studies based on density functional theory. Remarkably, the single atom catalysts achieve exceptional selectivity toward CH₃ Br (up to 98%) when compared to nanoparticles and any previously reported system. Furthermore, the results reveal unparalleled specific activity over 1.3-2.3 nm-sized platinum nanoparticles, which also exhibit the highest stability. Additionally, host effects are found to markedly affect the catalytic performance. Specifically, on NC, the activity and CH₃ Br selectivity are enhanced, but significant fouling occurs. On the other hand, AC-supported platinum nanostructures deactivate due to sintering and bromination. Simulations and kinetic fingerprints demonstrate that the observed reactivity patterns are governed by the H₂ dissociation abilities of the catalysts and the availability of surface H-atoms.

Nanoscale engineering of catalytic materials for sustainable technologies

Nanostructured materials of diverse architecture are ubiquitous in industrial catalysis. They offer exciting prospects to tackle various sustainability challenges faced by society. Since the introduction of the concept a century ago, researchers aspire to control the chemical identity, local environment and electronic properties of active sites on catalytic surfaces to optimize their reactivity in given applications. Nowadays, numerous strategies exist to tailor these characteristics with varying levels of atomic precision. Making headway relies upon the existence of analytical approaches able to resolve relevant structural features and remains challenging due to the inherent complexity even of the simplest heterogeneous catalysts, and to dynamic effects often occurring under reaction conditions. Computational methods play a complementary and ever-increasing role in pushing forward the design. Here, we examine how nanoscale engineering can enhance the selectivity and stability of catalysts. We highlight breakthroughs towards their commercialization and identify directions to guide future research and innovation.

Status and prospects of the decentralised valorisation of natural gas into energy and energy carriers

We critically review the recent advances in process, reactor, and catalyst design that enable process miniaturisation for decentralised natural gas upgrading into electricity, liquefied natural gas, fuels and chemicals.