Density functional theory methods applied to homogeneous and heterogeneous catalysis: a short review and a practical user guide

Butera, Valeria

doi:10.1039/d4cp00266k

Cited by 10 publications

(2 citation statements)

References 195 publications

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“…7–15 On the other hand, computational calculations by means of state-of-the-art density functional theory (DFT) protocols enable the identification of reaction pathways for explaining the selectivity and/or the activity reached with a given metal catalyst. 16–23 This is particularly advantageous for homogeneous metal-catalysed reactions involving gaseous reagents such as H 2 , CO, CO 2 , and others, which are routinely employed in hydrogenations, hydroformylations, (alkoxy)carbonylations, carboxylations and related ones, including also the asymmetric variants. 24–39…”

Section: Introductionmentioning

confidence: 99%

Penta- versus hexa-coordinated iridium catalysts control the reactivity of the direct reductive amination between aliphatic amines and aliphatic ketones: a DFT-guided mechanism

Lin,

Li,

Liu

et al. 2024

Catal. Sci. Technol.

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Section: Introductionmentioning

confidence: 99%

Penta- versus hexa-coordinated iridium catalysts control the reactivity of the direct reductive amination between aliphatic amines and aliphatic ketones: a DFT-guided mechanism

Lin,

Li,

Liu

et al. 2024

Catal. Sci. Technol.

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“…This type of calculation has been the subject of much research and has also been reviewed in various publications showing the importance of DFT calculations. For example, Butera [141] provides practical guidance for researchers in the field of both homogeneous and heterogeneous catalysis, covering atomic-centered basis sets, plane waves, and energy barrier evaluation. The work discusses two concepts used to understand the kinetics of chemical reactions, particularly in catalysis: transition state theory (TST) and the energetic span model (ESM).…”

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confidence: 99%

Advances in Computational Methods for Modeling Photocatalytic Reactions: A Review of Recent Developments

Gusarov

2024

Materials

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Photocatalysis is a fascinating process in which a photocatalyst plays a pivotal role in driving a chemical reaction when exposed to light. Its capacity to harness light energy triggers a cascade of reactions that lead to the formation of intermediate compounds, culminating in the desired final product(s). The essence of this process is the interaction between the photocatalyst’s excited state and its specific interactions with reactants, resulting in the creation of intermediates. The process’s appeal is further enhanced by its cyclic nature—the photocatalyst is rejuvenated after each cycle, ensuring ongoing and sustainable catalytic action. Nevertheless, comprehending the photocatalytic process through the modeling of photoactive materials and molecular devices demands advanced computational techniques founded on effective quantum chemistry methods, multiscale modeling, and machine learning. This review analyzes contemporary theoretical methods, spanning a range of lengths and accuracy scales, and assesses the strengths and limitations of these methods. It also explores the future challenges in modeling complex nano-photocatalysts, underscoring the necessity of integrating various methods hierarchically to optimize resource distribution across different scales. Additionally, the discussion includes the role of excited state chemistry, a crucial element in understanding photocatalysis.

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Catalysis in the digital age: Unlocking the power of data with machine learning

Abraham,

Jyothirmai,

Sinha

et al. 2024

WIREs Comput Mol Sci

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The design and discovery of new and improved catalysts are driving forces for accelerating scientific and technological innovations in the fields of energy conversion, environmental remediation, and chemical industry. Recently, the use of machine learning (ML) in combination with experimental and/or theoretical data has emerged as a powerful tool for identifying optimal catalysts for various applications. This review focuses on how ML algorithms can be used in computational catalysis and materials science to gain a deeper understanding of the relationships between materials properties and their stability, activity, and selectivity. The development of scientific data repositories, data mining techniques, and ML tools that can navigate structural optimization problems are highlighted, leading to the discovery of highly efficient catalysts for a sustainable future. Several data‐driven ML models commonly used in catalysis research and their diverse applications in reaction prediction are discussed. The key challenges and limitations of using ML in catalysis research are presented, which arise from the catalyst's intrinsic complex nature. Finally, we conclude by summarizing the potential future directions in the area of ML‐guided catalyst development.This article is categorized under: Structure and Mechanism > Reaction Mechanisms and Catalysis Data Science > Artificial Intelligence/Machine Learning Electronic Structure Theory > Density Functional Theory

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Density functional theory methods applied to homogeneous and heterogeneous catalysis: a short review and a practical user guide

Abstract: The application of density functional theory (DFT) methods in catalysis has been fast growing in the last decades thanks to both the availability of more powerful high computing resources and...

Cited by 10 publications

References 195 publications

Penta- versus hexa-coordinated iridium catalysts control the reactivity of the direct reductive amination between aliphatic amines and aliphatic ketones: a DFT-guided mechanism

Penta- versus hexa-coordinated iridium catalysts control the reactivity of the direct reductive amination between aliphatic amines and aliphatic ketones: a DFT-guided mechanism

Advances in Computational Methods for Modeling Photocatalytic Reactions: A Review of Recent Developments

Catalysis in the digital age: Unlocking the power of data with machine learning

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