This paper describes the efficient scale-up synthesis of 1 (BMS-963272) which relies upon a highly selective Mannich-type alkylation strategy to stereospecifically install a quaternary carbon center. ...An intramolecular cyclization reaction is also used to form the aryl dihydropyridone (ADHP) core. The optimized route has been demonstrated to provide more than 100 g of active pharmaceutical ingredient for preclinical toxicology evaluation. A catalyst screening effort is also discussed as part of a complimentary convergent approach which will facilitate a more expedient assessment of back-up molecules bearing aryl diversity at the C4-position of the ADHP core.
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•A microkinetic-machine learning method is proposed to screen bimetallic catalyst.•Microkinetic provides the optimal activity range and key descriptors for prediction.•The R2 score of ...XGBoost is above 97 % for 5000 + C and O formation energy databases.•Mkml obtains 48 promising candidates for steam methane reforming.
With the development of first-principles computing and artificial intelligence, catalyst screening have taken remarkable steps toward machine learning (ML). However, it is difficult to explore universal features and descriptors for catalyst screening due to the high spatial freedom of catalytic materials and the lacking kinetic insights of catalytic activity metrics. In this paper, a universal microkinetic-machine learning method (Mkml) is proposed to screen bimetallic catalysts. First, microkinetic models are built based on reaction information to search for descriptors and activity ranges. Then, datasets consisting of elemental properties and adsorption site encoding are evaluated by ML to predict the reference formation energy of the descriptors to optimize the microkinetic model. Finally, activity, stability and price factors are considered to screen competitive catalysts. Mkml was applied to steam methane reforming (SMR) to prove its high accuracy and low cost. The perform best ML is XGBoost with a 0.973 R2 score, and the feature importance also provides a valuable reference for catalyst design. 48 promising candidates are selected by Mkml from the 5000 + catalytic database, which not only accelerates experimenters' discovery of efficient and cheap SMR catalysts, but also guides the search way in a larger materials space.
DalPhos/Ni-based catalysts have emerged as top performers in C–N and C–O cross-couplings. Expedient means of generating such ligands would facilitate the discovery of effective DalPhos ligand ...variants as well as accelerate reaction development processes for end users. A protocol for generating structurally varied phosphine- and phosphonite-type DalPhos ligands from a single ligand precursor upon treatment with commercial reagents and without the need for chromatographic purification is disclosed. The formation of DalPhos ligands via this divergent synthetic strategy was exploited in the expedited screening of representative Ni-catalyzed C–N and C–O cross-couplings, leading to the identification of the DalPhos ligand variants (i.e., BnPAd-DalPhos, L4, and OAdPAd-DalPhos, L9) that, in turn, were carried forward for reaction scope analysis in challenging cross-couplings of fluoroalkylamines, by use of prepared (DalPhos)Ni(aryl)Cl precatalyst complexes. The reported methodology offers a user-friendly means of generating DalPhos variants in reaction development.
Doping isolated single atoms of a platinum-group metal into the surface of a noble-metal host is sufficient to dramatically improve the activity of the unreactive host yet also facilitates the ...retention of the host’s high reaction selectivity in numerous catalytic reactions. The atomically dispersed highly active sites in these single-atom alloy (SAA) materials are capable of performing facile bond activations allowing for the uptake of species onto the surface and the subsequent spillover of adspecies onto the noble host material, where selective catalysis can be performed. For example, SAAs have been shown to activate C–H bonds at low temperatures without coke formation, as well as selectively hydrogenate unsaturated hydrocarbons with excellent activity. However, to date, only a small subset of SAAs has been synthesized experimentally and it is unclear which metallic combinations may best catalyze which chemical reactions. To shed light on this issue, we have performed a widespread screening study using density functional theory to elucidate the fundamental adsorptive and catalytic properties of 12 SAAs (Ni-, Pd-, Pt-, and Rh-doped Cu(111), Ag(111), and Au(111)). We considered the interaction of these SAAs with a variety of adsorbates often found in catalysis and computed reaction mechanisms for the activation of several catalytically relevant species (H2, CH4, NH3, CH3OH, and CO2) by SAAs. Finally, we discuss the applicability of thermochemical linear scaling and the Brønsted–Evans–Polanyi relationship to SAA systems, demonstrating that SAAs combine weak binding with low activation energies to give enhanced catalytic behavior over their monometallic counterparts. This work will ultimately facilitate the discovery and development of SAAs, serving as a guide to experimentalists and theoreticians alike.
Efficient catalyst screening necessitates predictive models for adsorption energy, which is a key descriptor of reactivity. Prevailing methods, notably graph neural networks (GNNs), demand precise ...atomic coordinates for constructing graph representations, while the integration of observable attributes remains challenging. This research introduces CatBERTa, an energy prediction Transformer model that uses textual inputs. Built on a Transformer encoder pretrained for language modeling purposes, CatBERTa processes human-interpretable text, incorporating target features. Attention score analysis reveals CatBERTa’s focus on tokens related to adsorbates, bulk composition, and their interacting atoms. Moreover, interacting atoms emerge as effective descriptors for adsorption configurations, while factors such as the bond length and atomic properties of these atoms offer limited predictive contributions. In predicting the adsorption energy from textual representations of initial structures, CatBERTa exhibits a precision comparable to that of conventional GNNs. Notably, in subsets recognized for their high accuracy with GNNs, CatBERTa consistently achieves a mean absolute error of 0.35 eV. Furthermore, the subtraction of the CatBERTa-predicted energies effectively cancels out their systematic errors by as much as 19.3% for chemically similar systems, surpassing the error reduction observed in GNNs. This outcome highlights its potential to enhance the accuracy of the energy difference predictions. This research establishes a fundamental framework for text-based catalyst property prediction without relying on graph representations while also unveiling intricate feature–property relationships.
Complex solid‐solution electrocatalysts (also referred to as high‐entropy alloy) are gaining increasing interest owing to their promising properties which were only recently discovered. With the ...capability of forming complex single‐phase solid solutions from five or more constituents, they offer unique capabilities of fine‐tuning adsorption energies. However, the elemental complexity within the crystal structure and its effect on electrocatalytic properties is poorly understood. We discuss how addition or replacement of elements affect the adsorption energy distribution pattern and how this impacts the shape and activity of catalytic response curves. We highlight the implications of these conceptual findings on improved screening of new catalyst configurations and illustrate this strategy based on the discovery and experimental evaluation of several highly active complex solid solution nanoparticle catalysts for the oxygen reduction reaction in alkaline media.
Elementary: The effect of changing elements within high‐entropy electrocatalysts with respect to changes in adsorption energies and the correlated characteristic activity curves is analyzed. This concept is verified for the oxygen reduction reaction (ORR) in alkaline media. A targeted design of complex solid‐solution electrocatalysts is significantly enhanced by only screening equiatomic alloys without the need to scan the whole composition range.
Energy crisis and environmental pollution have become the bottleneck of human sustainable development. Therefore, there is an urgent need to develop new catalysts for energy production and ...environmental remediation. Due to the high cost caused by blind screening and limited valuable computing resources, the traditional experimental methods and theoretical calculations are difficult to meet with the requirements. In the past decades, computer science has made great progress, especially in the field of machine learning (ML). As a new research paradigm, ML greatly accelerates the theoretical calculation methods represented by first principal calculation and molecular dynamics, and establish the physical picture of heterogeneous catalytic processes for energy and environment. This review firstly summarized the general research paradigms of ML in the discovery of catalysts. Then, the latest progresses of ML in light-, electricity- and enzyme-mediated heterogeneous catalysis were reviewed from the perspective of catalytic performance, operating conditions and reaction mechanism. The general guidelines of ML for heterogeneous catalysis were proposed. Finally, the existing problems and future development trend of ML in heterogeneous catalysis mediated by light, electricity and enzyme were summarized. We highly expect that this review will facilitate the interaction between ML and heterogeneous catalysis, and illuminate the development prospect of heterogeneous catalysis.
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•General guidance on machine learning (ML) for heterogeneous catalysis is proposed.•The recent progress in ML from a new perspective is reviewed for the first time.•Performance prediction and parameter optimization via ML is discussed.•The understanding of catalytic mechanism via ML is highlighted.•Challenges and perspectives of ML for energy and environment are put forward.
A comparative study of 10 wt% Co-based catalysts supported on La2O3, AlZnOx and AlLaOx was performed for glycerol steam reforming (GSR). The catalysts physicochemical characterization was done ...through several techniques. All catalysts were screened in terms of catalytic activity and time-on-stream stability for GSR. The catalytic activity experiments aimed to assess the effect of temperature (400–700 °C) on the glycerol conversion and yield of gaseous products (H2, CO2, CO and CH4). Additionally, catalytic stability experiments were conducted at 625 °C to investigate deactivation of the catalysts, in which a drop in the activity was observed, especially for Co/La2O3. The glycerol conversion into gaseous products as a function of the time-on-stream was more affected for all catalysts in comparison to total glycerol conversion, being this effect assigned to the increase in the formation of liquid products and to the formation of coke. CoAlLaOx was observed to be more carbon-resistant, followed by CoAlZnOx, through the measurement of the quantity of carbonaceous species formed during the GSR experiments. A NiAlLaOx catalyst was also prepared and assessed in terms of catalytic stability for GSR; a stable behavior was observed throughout all experiment in relation to glycerol conversion into gaseous products and H2 yield.
Scheme of the hydrogen production from steam reforming of glycerol – by-product of the biodiesel manufacture process – using Co-based catalysts supported on La2O3, AlZnOx and AlLaOx. Display omitted
•Co-based catalysts supported on La2O3, AlZnOx and AlLaOx were tested for GSR.•The increase in temperature favors both glycerol conversion and H2 production.•CoAlLaOx showed higher activity and a H2 yield of 3.85 molH2/molG,inat 700 °C.•CoAlLaOx was more carbon-resistant by determination of the quantity of coke formed.•GSR and oxidative regeneration combined is a way to revert catalyst deactivation.
Sewage sludge is bio-solid with high moisture content generated from wastewater treatment plants. Due to the avoidance of energy-intensive dewatering, hydrothermal conversion of sewage sludge becomes ...a promising technology to simultaneously achieve energy recovery and solid waste management. In order to obtain an entire understanding of applicability of hydrothermal gasification for hydrogen rich gas production from sewage sludge, this review article discussed hydrothermal conversion and gasification processes in terms of fundamental principles, operating conditions, partial oxidative gasification, and detrimental effects of intermediates. Furthermore, since organic compounds in sewage sludge are mainly composed of carbohydrates, proteins, lipids, and lignin, this article comprehensively reviewed hydrogen production from these biomass model compounds and their hydrolysis products under sub- and supercritical water. Additionally, introduction of alkali salts and heterogeneous catalysts to enhance hydrogen yield under mild temperatures and pressures in hydrothermal gasification process was also discussed. Based on bench and pilot scale studies, supercritical water gasification of sewage sludge for hydrogen production is feasible in terms of technical and economic evaluation. Given issues concerning corrosion, plugging and high operating cost, a combined supercritical water gasification and catalytic hydrothermal gasification concept is proposed as a practical strategy to directly harness hydrogen from sewage sludge in future applications.
Heterogeneous catalysts are the key components in industrial chemical transformations. Metal oxides are particularly appealing as catalysts owing to their inherent Lewis acid–base properties that ...facilitate the activation of chemically inert paraffinic C–H bonds. Computational chemistry provides a rich mechanistic understanding of catalyst functionality through the calculation of accurate thermodynamic and kinetic data that cannot be experimentally accessible. Using these data, one can relate the energy needed for elementary reaction steps with properties of the catalyst, paving the way for computational catalyst discovery. At the heart of this process is the development of structure–activity relationships (SARs) that facilitate the rapid prediction of promising catalytic materials for energy intense industrial transformations, guiding experimentation. In this review article, we highlight SARs on oxides for chemical reactions of high industrial relevance including (i) methane activation and conversion, (ii) alkane dehydrogenation, and (iii) alcohol dehydration. We also discuss current limitations and challenges on SARs and propose future steps to advance catalyst discovery.
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