Probing machine learning models based on high throughput experimentation data for the discovery of asymmetric hydrogenation catalysts

Chemical Science, Год журнала: 2024, Номер 15(34), С. 13618 - 13630

Опубликована: Янв. 1, 2024

Enantioselective hydrogenation of olefins by Rh-based chiral catalysts has been extensively studied for more than 50 years. Naively, one would expect that everything about this transformation is known and selecting a catalyst induces the desired reactivity or selectivity trivial task. Nonetheless, ligand engineering selection any new prochiral olefin remains an empirical trial-error exercise. In study, we investigated whether machine learning techniques could be used to accelerate identification most efficient ligand. For purpose, high throughput experimentation build large dataset consisting results Rh-catalyzed asymmetric hydrogenation, specially designed applications in learning. We showcased its alignment with existing literature while addressing observed discrepancies. Additionally, computational framework automated reproducible quantum-chemistry based featurization structures was created. Together less computationally demanding representations, these descriptors were fed into our pipeline both out-of-domain in-domain prediction tasks reactivity. purposes, models provided limited efficacy. It found even expensive do not impart significant meaning model predictions. The application, partly successful predictions conversion, emphasizes need evaluating cost-benefit ratio intensive tailored descriptor design. Challenges persist predicting enantioselectivity, calling caution interpreting from small datasets. Our insights underscore importance diversity broad substrate inclusion suggest mechanistic considerations improve accuracy statistical models.

Язык: Английский

---

Transition metal oxides in CO2 driven oxidative dehydrogenation: Uncovering their redox properties

Journal of CO2 Utilization, Год журнала: 2024, Номер 84, С. 102848 - 102848

Опубликована: Июнь 1, 2024

Extensive research efforts have been devoted to using greenhouse gas CO2 in upgrading bio-derived feedstock value-added chemicals the oxidative dehydrogenation route (CO2-ODH) with low-energy input. To realize effective deployment of CO2-ODH at an industrial scale, it is imperative advance development robust catalysts that can selectively catalyze C-H over C-C bonds, while simultaneously demonstrating thermodynamic stability coke formation or sintering. Transition metal-based exhibit significant potential for being highly selective and reactive simultaneous conversion hydrocarbons, owing their surface reducibility, well-balanced acid/base properties, dynamic oxygen storage capacity. stimulate further optimization, it's crucial discover key design principles, trends descriptors, strategies fine-tune catalyst materials. This review comprehensively examines experimental theoretical aimed pinpointing catalytic characteristics affect selectivity transition mono, bi, multimetal oxides. It covers aspects like combinations active metals supports, effects composition alloying, interfacial structures, adsorption strengths, dynamics in-situ restructuring, defect creation, morphology, electronic among others. We wrap up by suggesting approaches overcome present obstacles reactor technology, potentially bridging lab-to-industry gap this domain.

Язык: Английский

---

A New Era in Catalysis: Combining Al, DFT, Single Atom Catalysis, and Comprehensive Characterizations applied to Catalytic Oxidation of C1-C4 Volatile Organic Compounds

Journal of environmental chemical engineering, Год журнала: 2025, Номер 13(1), С. 115282 - 115282

Опубликована: Янв. 5, 2025

Язык: Английский

---

The Structure-Property Relationship of Metallocene-based Ethylene Oligomerization Catalysts Using DFT and Graph Neural Networks

Dalton Transactions, Год журнала: 2025, Номер 54(10), С. 4069 - 4081

Опубликована: Янв. 1, 2025

A combination of DFT calculations and 3D GNN reveals the effect substructures titanocenes on ethylene oligomerization selectivity.

Язык: Английский

---

Iron: From Basic Chemistry to Modern C-H Bond Functionalization Catalysts

IntechOpen eBooks, Год журнала: 2025, Номер unknown

Опубликована: Март 18, 2025

Iron (Fe) is a first-row transition metal that offers several advantages, including low cost, Earth abundance, and environmental safety. These benefits are particularly significant compared to other metals from the second-row beyond. Unlike precious such as palladium platinum, iron readily available, accessible, affordable, in toxicity, recyclable. This chapter aims provide an overview of importance various aspects life highlight impact catalysis. It begins by examining occurrence nature its environment human health. The then discusses compounds, focusing on their uses applications chemistry general organic synthesis particular. includes role compounds catalysts reagents synthetic transformations, electrophilic aromatic substitution reactions, cross-coupling cycloaddition oxidation reduction chemistries. also developments iron-catalyzed C∙H bond functionalization, inspired biological systems. continues covering photocatalyzed functionalization. highlights artificial intelligence machine learning catalyst design, which could be applied Given green features catalysis, represented recyclability, concludes with catalysis preservation environment.

Язык: Английский

---

The formation of nickelalactone in CO2/C2H4 coupling reaction: A benchmark, dispersion correction, and energy decomposition analysis

Molecular Catalysis, Год журнала: 2024, Номер 558, С. 113996 - 113996

Опубликована: Март 11, 2024

Язык: Английский

---

Going beyond the Ordered Bulk: A Perspective on the Use of the Cambridge Structural Database for Predictive Materials Design

Crystal Growth & Design, Год журнала: 2024, Номер 24(17), С. 6911 - 6930

Опубликована: Авг. 19, 2024

When Olga Kennard founded the Cambridge Crystallographic Data Centre in 1965, Structural Database was a pioneering attempt to collect scientific data standard format. Since then, it has evolved into an indispensable resource contemporary molecular materials science, with over 1.25 million structures and comprehensive software tools for searching, visualizing analyzing data. In this perspective, we discuss use of CSD CCDC address multiscale challenge predictive design. We provide overview core capabilities demonstrate their application range design problems recent case studies drawn from topical research areas, focusing particular on mining machine learning techniques. also identify several challenges that can be addressed existing or through new varying levels development effort.

Язык: Английский

---

Revisiting the Reviewed: A Meta‐Analysis of Computational Studies on Transition Metal‐Catalysed Hydrogenation Reactions

ChemCatChem, Год журнала: 2024, Номер unknown

Опубликована: Сен. 2, 2024

Abstract This review of reviews attempts to systematically analyze the recent advancements in transition metal‐catalyzed hydrogenation reactions as discussed previous articles, emphasizing computational insights that enhance our understanding reaction mechanisms. It highlights efficacy density functional theory (DFT) calculating free energies, exploring mechanistic pathways and kinetics processes and, focusing on substrates such alkenes, alkynes, amides, imines, nitriles, carbon dioxide. The details significant studies where models help predict outcomes aid catalyst design. Notable discussions include role solvent effects metal‐ligand interactions, which are crucial for reactivity selectivity but often underestimated models. concludes with current challenges prospects, suggesting enhanced experimental collaborations refine

Язык: Английский

---

In Silico Models for Prediction of Methanol Yield in CO2 Hydrogenation Reaction Using Cu-Based Catalysts

Catalysis Letters, Год журнала: 2024, Номер unknown

Опубликована: Сен. 19, 2024

Язык: Английский

---