Issue 13, 2024

How do quantum chemical descriptors shape hydrogen atom abstraction reactivity in cupric-superoxo species? A combined DFT and machine learning perspective

Abstract

Oxygen activation, a crucial function performed by enzymes, prompts the synthesis of biomimetic models utilised to investigate structure–activity relationships, with a particular focus on metal-superoxo species resulting from O2 interaction with the metal centre. Among others, cupric-superoxo species have been extensively studied, showcasing diverse examples and potent catalytic capabilities. While quantum chemical calculations have helped in understanding the mechanistic aspect of their reactivity, recent advances in machine learning (ML) tools have substantiated this further and offered potent predictive power. The development of machine learning tools and associated quantum descriptors for open-shell paramagnetic catalysts is rarely pursued due to the complexity involved. However, if achieved, it has the potential to fundamentally change the existing paradigm in catalytic design and development. In making this connection, a detailed hydrogen atom transfer (HAT) reaction instigated by [(TMPA)Cu(II)–O2˙] species and its analogues gains relevance as they offer a unique set of diverse reactivity pathways among structurally similar cupric-superoxo species. In this study, we embark on a comprehensive exploration of reactivity mechanisms employing the DFT method (B3LYP/TZVP) with five distinct catalysts and three varied substrates, resulting in combinations that lead to fifteen different reactions for the HAT reaction. The reactivity of cupric-superoxo species was found to be correlated not only with the rate-limiting HAT barrier but also with the competitive dimerization barrier. Our comprehensive analysis of mechanisms offered a rationale for the experimentally observed reactivity and the setting of goals for developing suitable ML models. In making this connection, we have arrived at fifteen quantum chemical descriptors, including exchange interaction (J), sterics, hydrogen bonding, and various thermodynamic parameters derived from DFT calculations. Our multivariate linear regression (MLR) model accurately predicts catalytic reactivity towards HAT using these quantum chemical descriptors based simply on ground state geometry. The H-bonding interactions, along with the free energy of the HAT/PT/ET reaction (ΔGPCETGPTGET), were found to yield excellent results for accuracy (R2 = 0.90), setting a stage to study multinuclear paramagnetic catalysts. For the first time, this study provides valuable insights not only into the reactivity of metalloenzymes but also offers design clues to enhance the reactivity of transient species using the ML approach.

Graphical abstract: How do quantum chemical descriptors shape hydrogen atom abstraction reactivity in cupric-superoxo species? A combined DFT and machine learning perspective

Supplementary files

Article information

Article type
Research Article
Submitted
20 Mar 2024
Accepted
11 May 2024
First published
14 May 2024

Inorg. Chem. Front., 2024,11, 3830-3846

How do quantum chemical descriptors shape hydrogen atom abstraction reactivity in cupric-superoxo species? A combined DFT and machine learning perspective

C. Nettem and G. Rajaraman, Inorg. Chem. Front., 2024, 11, 3830 DOI: 10.1039/D4QI00701H

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements