Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysis

Xu, Jiayan; Cao, Xiaoming; Hu, Peipei

doi:10.1039/d1cp01349a

Cited by 51 publications

(36 citation statements)

References 225 publications

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Section: Modeling Strategies For Realistic Simulationmentioning

confidence: 99%

“…135−138 Two widely utilized modeling methods in this field are mean-field microkinetic modeling and kinetic Monte Carlo (kMC). 139 An emerging alternative is a hybrid method called extended phenomenological kinetics (XPK), which is proposed by Xu and co-workers and is specially designed to describe complex catalytic kinetics under operando conditions. 140 With enhanced accuracy and efficiency, XPK has been successfully described as a complex reaction network on the surface, including syngas conversion, 141 formic acid decomposition, 142 Besides, some automatic techniques has been applied to automatically generate microkinetic mechanisms for the surface catalytic process like graph theory-driven software Reaction Mechanism Generator (RMG) for an oxidative coupling over a Pt surface and the combustion of methane over a Ni surface, 144,145 CatNet for a syngas conversion over Rh(111), 146,147 stochastic surface walking (SSW) for a water gas shift reaction over Cu (111), 148 and the artificial force induced reaction (AFIR) method for a CO oxidation over Pt (111).…”

Section: 72mentioning

confidence: 99%

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Dynamics of Heterogeneous Catalytic Processes at Operando Conditions

Shi

Lin

Luo

et al. 2021

JACS Au

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The rational design of high-performance catalysts is hindered by the lack of knowledge of the structures of active sites and the reaction pathways under reaction conditions, which can be ideally addressed by an in situ / operando characterization. Besides the experimental insights, a theoretical investigation that simulates reaction conditions—so-called operando modeling—is necessary for a plausible understanding of a working catalyst system at the atomic scale. However, there is still a huge gap between the current widely used computational model and the concept of operando modeling, which should be achieved through multiscale computational modeling. This Perspective describes various modeling approaches and machine learning techniques that step toward operando modeling, followed by selected experimental examples that present an operando understanding in the thermo- and electrocatalytic processes. At last, the remaining challenges in this area are outlined.

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Section: Modeling Strategies For Realistic Simulationmentioning

confidence: 99%

Section: 72mentioning

confidence: 99%

Dynamics of Heterogeneous Catalytic Processes at Operando Conditions

Shi

Lin

Luo

et al. 2021

JACS Au

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“…The structure-property relationship forms the basis of modern materials science/chemistry, as the discovery of new materials and further understanding of existing ones neccesitates the need to probe the atomic/nano regime. [1][2][3][4][5] At the heart of this relationship is the fundamental principle that the arrangement of atoms dictates the behavior of the material throughout a spectrum of length and time scales. Reliably capturing structure-property relationships plays a vital role in fields such as crystal structure prediction, 6 the dynamic evolution of complex defect networks, 7 and the construction of interatomic potentials [8][9][10] among others.…”

Section: Introductionmentioning

confidence: 99%

A Physically-informed Graph-based Order Parameter for the Universal Characterization of Atomic Structures

Chapman,

Goldman,

Wood

2021

Preprint

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A new graph-based order parameter is introduced for the characterization of atomistic structures. The order parameter is universal to any material/chemical system, and is transferable to all structural geometries. Three sets of data are used to validate both the generalizability and accuracy of the algorithm: (1) liquid lithium configurations spanning up to 300 GPa, (2) condensed phases of carbon along with nanotubes and buckyballs at ambient and high temperature, and (3) a diverse set of aluminum configurations including surfaces, compressed and expanded lattices, point defects, grain boundaries, liquids, nanoparticles, all at non-zero temperatures. The aluminum configurations are also compared to existing characterization methods for both speed and accuracy. Our order parameter uniquely classifies every configuration and outperforms all crystalline order parameters studied here, opening the door for its use in a multitude of complex application spaces that can require fine configurational characterization of materials.

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“…Innovations in chemistry are often driven by accumulated chemical knowledge, mechanistic understanding, and data analysis such as well-known regression analysis and emerging machine learning, − one of which, the mechanistic understanding, enables rational discovery of new compounds or new reaction systems. Besides available experimental techniques, computations are often carried out to provide mechanistic pictures for a given reaction, in modern research in particular.…”

Section: Introductionmentioning

confidence: 99%

Structure-Based Relative Energy Prediction Model: A Case Study of Pd(II)-Catalyzed Ethylene Polymerization and the Electronic Effect of Ancillary Ligands

2021

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Rapidly mapping a reaction energy profile to understand the reaction mechanism is of great importance and highly desired for the discovery of new chemical reactions. Herein, a combination of density functional theory (DFT) calculations and regression analysis has been applied to construct quantitative structures-based energy prediction models, considering Pd(II)-catalyzed ethylene polymerization as an example, for rapid construction of the reaction energy profile. It is inspiring that only geometrical parameters of the reaction center of one species are capable of predicting the whole energy profile with high accuracy. The reaction energies of ethylene insertion and β-H elimination, which directly correlate with polymerization activity and the possibility of branch formation, were studied to elucidate the electronic effects of ancillary ligands. Further analyses of these models from the statistical and chemical points of view afforded useful information on the design of the catalyst ligand. The current work is expected to methodologically shed new light on rapidly mapping the energy profile of chemical reactions and further provide useful information for the development of the reactions.

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Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysis

Abstract: Machine learning algorithms can facilitate the reaction prediction in heterogeneous catalysis.

Cited by 51 publications

References 225 publications

Dynamics of Heterogeneous Catalytic Processes at Operando Conditions

Dynamics of Heterogeneous Catalytic Processes at Operando Conditions

A Physically-informed Graph-based Order Parameter for the Universal Characterization of Atomic Structures

Structure-Based Relative Energy Prediction Model: A Case Study of Pd(II)-Catalyzed Ethylene Polymerization and the Electronic Effect of Ancillary Ligands

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