Bridging the complexity gap in computational heterogeneous catalysis with machine learning

Mou, Tianyou; Pillai, Hemanth Somarajan; Wang, Siwen; Wan, Mingyu; Han, Xue; Schweitzer, Neil M.; Che, Fanglin; Xin, Hongliang

doi:10.1038/s41929-023-00911-w

Cited by 87 publications

(70 citation statements)

References 129 publications

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“…For more complex reaction networks, such as the initial reactions taking place in the methanol to hydrocarbons process, where a lot of competitive reactions can take place simultaneously, or the CO 2 to hydrocarbons process, which has an equally large number of reactions that are possible, the approach sketched above will potentially fail, as only a limited number of CVs would not be able to capture the full complexity of the reaction network. In these cases, it might be beneficial to rely on methods where we can automatically discover potential reactions and where we could more sample them on the fly without the a priori definition of CVs. , On the positive side, with first-principles MD, one can simulate in one run various competitive pathways simultaneously, in contrast to static approaches, where information on competitive pathways is gathered from separated simulations not necessarily starting from similar points in configuration space.…”

Section: Selected Case Studies Illustrating the Pros And Cons Of Firs...mentioning

confidence: 99%

Operando Modeling of Zeolite-Catalyzed Reactions Using First-Principles Molecular Dynamics Simulations

Van Speybroeck,

Bocus,

Cnudde

et al. 2023

ACS Catal.

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Within this Perspective, we critically reflect on the role of first-principles molecular dynamics (MD) simulations in unraveling the catalytic function within zeolites under operating conditions. First-principles MD simulations refer to methods where the dynamics of the nuclei is followed in time by integrating the Newtonian equations of motion on a potential energy surface that is determined by solving the quantum-mechanical many-body problem for the electrons. Catalytic solids used in industrial applications show an intriguing high degree of complexity, with phenomena taking place at a broad range of length and time scales. Additionally, the state and function of a catalyst critically depend on the operating conditions, such as temperature, moisture, presence of water, etc. Herein we show by means of a series of exemplary cases how first-principles MD simulations are instrumental to unravel the catalyst complexity at the molecular scale.Examples show how the nature of reactive species at higher catalytic temperatures may drastically change compared to species at lower temperatures and how the nature of active sites may dynamically change upon exposure to water. To simulate rare events, firstprinciples MD simulations need to be used in combination with enhanced sampling techniques to efficiently sample low-probability regions of phase space. Using these techniques, it is shown how competitive pathways at operating conditions can be discovered and how broad transition state regions can be explored. Interestingly, such simulations can also be used to study hindered diffusion under operating conditions. The cases shown clearly illustrate how first-principles MD simulations reveal insights into the catalytic function at operating conditions, which could not be discovered using static or local approaches where only a few points are considered on the potential energy surface (PES). Despite these advantages, some major hurdles still exist to fully integrate first-principles MD methods in a standard computational catalytic workflow or to use the output of MD simulations as input for multiple length/time scale methods that aim to bridge to the reactor scale. First of all, methods are needed that allow us to evaluate the interatomic forces with quantum-mechanical accuracy, albeit at a much lower computational cost compared to currently used density functional theory (DFT) methods. The use of DFT limits the currently attainable length/time scales to hundreds of picoseconds and a few nanometers, which are much smaller than realistic catalyst particle dimensions and time scales encountered in the catalysis process. One solution could be to construct machine learning potentials (MLPs), where a numerical potential is derived from underlying quantum-mechanical data, which could be used in subsequent MD simulations. As such, much longer length and time scales could be reached; however, quite some research is still necessary to construct MLPs for the complex systems encountered in industrially used catalysts. Second, most current...

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Section: Selected Case Studies Illustrating the Pros And Cons Of Firs...mentioning

confidence: 99%

Operando Modeling of Zeolite-Catalyzed Reactions Using First-Principles Molecular Dynamics Simulations

Van Speybroeck,

Bocus,

Cnudde

et al. 2023

ACS Catal.

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“…81 Moreover, Xin et al used machine learning to bridge the complexity gap in computational multiphase catalysis. 82 The study of the combination of artificial intelligence and electrocatalytic CO 2 accelerates the feasibility of achieving carbon neutrality.…”

Section: Challenges Gaps and Perspectivesmentioning

confidence: 99%

Minireview and Perspectives of Bimetallic Metal–Organic Framework Electrocatalysts for Carbon Dioxide Reduction

et al. 2023

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The electrocatalytic carbon dioxide reduction reaction (CO 2 RR) is an important energy strategy to address the carbon cycle, achieve carbon neutrality, and provide useful energy resources. As a tunable and multifunctional material, metal− organic frameworks (MOFs) have been extensively studied in the field of clean energy. Bimetallic MOFs exhibit specific synergistic effects in the presence of two different metal components, and their CO 2 RR performance is superior to that of the corresponding monometallic MOFs, especially for product selectivity. In this minireview, the recent progress of bimetallic MOFs as CO 2 RR electrocatalysts is reviewed. The main methods of catalyst synthesis are summarized. Then, from catalyst design considerations, the significance of organic ligands and metal components as well as MOF-derived materials is presented, respectively. To better understand the mechanism of bimetallic MOFs at the CO 2 RR, the advantages of two metal components are also mentioned. Finally, the main challenges, gaps, and perspectives of electrocatalytic CO 2 RR using bimetallic MOF materials are highlighted.

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“…By now, computational chemistry has evolved from the theorists' toy to a distinct method capable of explaining observed reactions and accurately estimating measurable thermochemical quantities. [1][2][3][4][5][6][7][8][9][10][11] However, a single finite-basis-set calculation typically does not possess sufficient accuracy for that. 12 Therefore, composite methods that combine the results of several different computations are used.…”

Section: Main Textmentioning

confidence: 99%

A larger basis set describes atomization energy core-valence correction better than a higher-order coupled-cluster method

Chamkin

Chamkina

2023

Preprint

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The accuracy of coupled-cluster methods for the computation of core-valence correction to atomization energy was assessed. Truncation levels up to CCSDTQP were considered together with (aug-)cc-pwCVnZ (n = D, T, Q, 5) basis sets and three different extrapolation techniques (canonical and flexible Helgaker formula and Riemann zeta function extrapolation). With the exception of CCSD, a more accurate correction can be obtained from a larger basis set with a lower-level coupled-cluster method, and not vice versa.

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Bridging the complexity gap in computational heterogeneous catalysis with machine learning

Cited by 87 publications

References 129 publications

Operando Modeling of Zeolite-Catalyzed Reactions Using First-Principles Molecular Dynamics Simulations

Operando Modeling of Zeolite-Catalyzed Reactions Using First-Principles Molecular Dynamics Simulations

Minireview and Perspectives of Bimetallic Metal–Organic Framework Electrocatalysts for Carbon Dioxide Reduction

A larger basis set describes atomization energy core-valence correction better than a higher-order coupled-cluster method

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