Catalysts informatics: paradigm shift towards data-driven catalyst design

Recent advances in catalyst design, performance, and challenges of metal-heteroatom-co-doped biochar as peroxymonosulfate activator for environmental remediation

The escalation of global water pollution due to emerging pollutants has gained significant attention. To address this issue, catalytic peroxymonosulfate (PMS) activation technology has emerged as a promising treatment approach for effectively decontaminating a wide range of pollutants. Recently, modified biochar has become an increasingly attractive as PMS activator. Metal-heteroatom-co-doped biochar (MH-BC) has emerged as a promising catalyst that can provide enhanced performance over heteroatom-doped and metal-doped biochar due to the synergism between metal and heteroatom in promoting PMS activation. Therefore, this review aims to discuss the fabrication pathways (i.e., internal vs external doping and pre-vs post-modification) and key parameters (i.e., source of precursors, synthesis methods, and synthesis conditions) affecting the performance of MH-BC as PMS activator. Subsequently, an overview of all the possible PMS activation pathways by MH-BC is provided. Subsequently, Also, the detection, identification, and quantification of several reactive species (such as, •OH, SO4•-, O2•-, 1O2, and high valent oxo species) generated in the catalytic PMS system by MH-BC are also evaluated. Lastly, the underlying challenges associated with poor stability, the lack of understanding regarding the interaction between metal and heteroatom during PMS activation and quantification of radicals in multi-ROS system are also deliberated.

Advancing electrocatalytic reactions through mapping key intermediates to active sites via descriptors

Descriptors play a crucial role in electrocatalysis as they can provide valuable insights into the electrochemical performance of energy conversion and storage processes. They allow for the understanding of different catalytic activities and enable the prediction of better catalysts without relying on the time-consuming trial-and-error approaches. Hence, this comprehensive review focuses on highlighting the significant advancements in commonly used descriptors for critical electrocatalytic reactions. First, the fundamental reaction processes and key intermediates involved in several electrocatalytic reactions are summarized. Subsequently, three types of descriptors are classified and introduced based on different reactions and catalysts. These include d-band center descriptors, readily accessible intrinsic property descriptors, and spin-related descriptors, all of which contribute to a profound understanding of catalytic behavior. Furthermore, multi-type descriptors that collectively determine the catalytic performance are also summarized. Finally, we discuss the future of descriptors, envisioning their potential to integrate multiple factors, broaden application scopes, and synergize with artificial intelligence for more efficient catalyst design and discovery.

Toward three-dimensionally ordered nanoporous graphene materials: template synthesis, structure, and applications

Precise template synthesis will realize three-dimensionally ordered nanoporous graphenes (NPGs) with a spatially controlled seamless graphene structure and fewer edges. These structural features result in superelastic nature, high electrochemical stability, high electrical conductivity, and fast diffusion of gases and ions at the same time. Such innovative 3D graphene materials are conducive to solving energy-related issues for a better future. To further improve the attractive properties of NPGs, we review the template synthesis and its mechanism by chemical vapor deposition of hydrocarbons, analysis of the nanoporous graphene structure, and applications in electrochemical and mechanical devices.

MATEO: intermolecular α-amidoalkylation theoretical enantioselectivity optimization. Online tool for selection and design of chiral catalysts and products

The enantioselective Brønsted acid-catalyzed α-amidoalkylation reaction is a useful procedure is for the production of new drugs and natural products. In this context, Chiral Phosphoric Acid (CPA) catalysts are versatile catalysts for this type of reactions. The selection and design of new CPA catalysts for different enantioselective reactions has a dual interest because new CPA catalysts (tools) and chiral drugs or materials (products) can be obtained. However, this process is difficult and time consuming if approached from an experimental trial and error perspective. In this work, an Heuristic Perturbation-Theory and Machine Learning (HPTML) algorithm was used to seek a predictive model for CPA catalysts performance in terms of enantioselectivity in α-amidoalkylation reactions with R2 = 0.96 overall for training and validation series. It involved a Monte Carlo sampling of > 100,000 pairs of query and reference reactions. In addition, the computational and experimental investigation of a new set of intermolecular α-amidoalkylation reactions using BINOL-derived N-triflylphosphoramides as CPA catalysts is reported as a case of study. The model was implemented in a web server called MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity Optimization, available online at: https://cptmltool.rnasa-imedir.com/CPTMLTools-Web/mateo . This new user-friendly online computational tool would enable sustainable optimization of reaction conditions that could lead to the design of new CPA catalysts along with new organic synthesis products.

Automatic feature engineering for catalyst design using small data without prior knowledge of target catalysis

The empirical aspect of descriptor design in catalyst informatics, particularly when confronted with limited data, necessitates adequate prior knowledge for delving into unknown territories, thus presenting a logical contradiction. This study introduces a technique for automatic feature engineering (AFE) that works on small catalyst datasets, without reliance on specific assumptions or pre-existing knowledge about the target catalysis when designing descriptors and building machine-learning models. This technique generates numerous features through mathematical operations on general physicochemical features of catalytic components and extracts relevant features for the desired catalysis, essentially screening numerous hypotheses on a machine. AFE yields reasonable regression results for three types of heterogeneous catalysis: oxidative coupling of methane (OCM), conversion of ethanol to butadiene, and three-way catalysis, where only the training set is swapped. Moreover, through the application of active learning that combines AFE and high-throughput experimentation for OCM, we successfully visualize the machine's process of acquiring precise recognition of the catalyst design. Thus, AFE is a versatile technique for data-driven catalysis research and a key step towards fully automated catalyst discoveries.

Exploring Metal Nanocluster Catalysts for Ammonia Synthesis Using Informatics Methods: A Concerted Effort of Bayesian Optimization, Swarm Intelligence, and First-Principles Computation

This paper details the use of computational and informatics methods to design metal nanocluster catalysts for efficient ammonia synthesis. Three main problems are tackled: defining a measure of catalytic activity, choosing the best candidate from a large number of possibilities, and identifying the thermodynamically stable cluster catalyst structure. First-principles calculations, Bayesian optimization, and particle swarm optimization are used to obtain a Ti8 nanocluster as a catalyst candidate. The N2 adsorption structure on Ti8 indicates substantial activation of the N2 molecule, while the NH3 adsorption structure suggests that NH3 is likely to undergo easy desorption. The study also reveals several cluster catalyst candidates that break the general trade-off that surfaces that strongly adsorb reactants also strongly adsorb products.

Unveiling the Hidden Energy Profiles of the Oxygen Evolution Reaction via Machine Learning Analyses

The oxygen evolution reaction (OER) is a crucial electrochemical process for hydrogen production in water electrolysis. However, due to the involvement of multiple proton-coupled electron transfer steps, it is challenging to identify the specific elementary reaction that limits the rate of the OER. Here we employed a machine-learning-based approach to extract the reaction pathway exhaustively from experimental data. Genetic algorithms were applied to search for thermodynamic and kinetic parameters using the current-electrochemical potential relationship of the OER. Interestingly, analysis of the datasets revealed the energy state distributions of reaction intermediates, which likely originated in the interactions among intermediates or the distribution of multiple sites. Through our exhaustive analyses, we successfully uncovered the hidden energy profiles of the OER. This approach can reveal the reaction pathway to activate for efficient hydrogen production, which facilitates the design of catalysts.