Automated Mechanism Discovery

Sameera, W. M. C.; Sumiya, Yosuke; Skjelstad, Bastian Bjerkem; Maeda, Satoshi

doi:10.1016/b978-0-12-821978-2.00003-9

Cited by 2 publications

(2 citation statements)

References 175 publications

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“…On the other hand, fully automating an exhaustive pathway search for all relevant elementary steps has been a long-standing problem [17][18][19][20][21][22][23][24] . Despite significant advances in DFT-based automated pathway search methods [25][26][27][28][29][30][31][32][33][34][35] , a comprehensive exploration requires years of computation or thousands of CPU cores even for small systems.…”

Section: Introductionmentioning

confidence: 99%

Systematic and unbiased pathway exploration by artificial force application to a generic neural network potential

Ichino,

Nabata,

Matsumoto

et al. 2024

Preprint

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Computational pathway exploration can unravel complex catalytic mechanisms and even predict unexplored catalytic reactions when performed in a fully systematic and unbiased manner. However, such a comprehensive exploration typically requires years of computation or thousands of CPU cores even for small systems. Herein, a generic neural network potential (NNP) trained on a large structure–energy database and force- and kinetics-based pathway exploration algorithm were combined without any tuning. An interface code was developed to combine the NNP in the Matlantis platform with the search algorithm in the GRRM software. The combined approach automatically generated comprehensive pathway ensembles containing over 10,000 local minimum structures for methane oxidation on the Pd(111) and Pd(100) surfaces with reasonable computational effort. The kinetically most plausible mechanism derived from the ensemble was qualitatively consistent with that obtained by density functional theory. These results highlight the considerable predictive power of the proposed approach at low computational cost.

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Section: Introductionmentioning

confidence: 99%

Systematic and unbiased pathway exploration by artificial force application to a generic neural network potential

Ichino,

Nabata,

Matsumoto

et al. 2024

Preprint

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“…Therefore, a method that is able to explore the potential energy surface unbiasedly and efficiently is necessary to reliably study the chemical reactivity leading to MOF formation. Automated reaction mechanism discovery methods − that sample reaction events on the potential energy surface in an automated fashion present a promising approach to investigate these complex reaction environments.…”

Section: Introductionmentioning

confidence: 99%

Early-Stage Formation of the SIFSIX-3-Zn Metal–Organic Framework: An Automated Computational Study

2023

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Metal−organic frameworks (MOFs) have attracted significant attention over the past 2 decades due to their wide applicability as functional materials. However, targeted synthesis of novel MOFs remains problematic as their formation mechanisms are poorly understood, which forces us to rely on serendipity in the synthesis of novel MOFs. Here, we demonstrate a workflow employing the artificial force induced reaction (AFIR) method to investigate the self-assembly process of the node of the SIFSIX-3-Zn MOF, [Zn(pyz) 4 (SiF 6 ) 2 ] 2− (pyz = pyrazine), in an automated manner. The workflow encompassing AFIR calculations, generation of extensive reaction path networks, propagation simulations of intermediates, and further refinements of identified formation pathways showed that the nodal structure can form through multiple competing pathways involving interconvertible intermediates. This finding provides a plausible rationale for the stochastic multistage processes believed to be key in MOF formation. Furthermore, this work represents the first application of an automated reaction mechanism discovery method to a MOF system using a general workflow that is applicable to study the formation of other MOF motifs as well.

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Automated Mechanism Discovery

Cited by 2 publications

References 175 publications

Systematic and unbiased pathway exploration by artificial force application to a generic neural network potential

Systematic and unbiased pathway exploration by artificial force application to a generic neural network potential

Early-Stage Formation of the SIFSIX-3-Zn Metal–Organic Framework: An Automated Computational Study

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