Enhancing Automated Reaction Discovery with Boxed Molecular Dynamics in Energy Space

Jara‐Toro, Rafael A.; Pino, Gustavo A.; Glowacki, David R.; Shannon, Robin J.; Martı́nez-Núñez, Emilio

doi:10.1002/syst.201900024

Cited by 20 publications

(42 citation statements)

References 108 publications

(174 reference statements)

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“…The figure also shows (in red) some of the most important pathways that were overlooked in the previous ab initio study and found in the BXDE sampling 76 . A full account of the new mechanisms predicted by BXDE is detailed elsewhere 76 …”

Section: Methodsmentioning

confidence: 87%

“…An example of the use of BXDE combined with AutoMeKin's TS search algorithms is the recent study of the ozonolysis of α‐pinene 76 . This reaction is known to follow the “Criegee mechanism” of alkene ozonolysis (see Figure 2), and was previously studied using ab initio methods 91 .…”

Section: Methodsmentioning

confidence: 99%

“…Despite the low level of electronic structure (PM7) employed to run BXDE and to optimize the stationary points, 76 not only was the new approach capable of predicting the major pathways (shown in black in Figure 2), but a significant number of new intermediates and pathways were also predicted. The figure also shows (in red) some of the most important pathways that were overlooked in the previous ab initio study and found in the BXDE sampling 76 . A full account of the new mechanisms predicted by BXDE is detailed elsewhere 76 …”

Section: Methodsmentioning

confidence: 99%

“…Up until now, locating TSs from MD simulations has been difficult, but the procedure described here has proven to be very effective and useful in predicting unexpected mechanisms. Our approach has been applied in combustion chemistry, 66,67 cycloaddition reactions, 68 photodissociations, 69–71 organometallic catalysis, 43 radiation damage of biological systems, 72 simulation of mass spectrometry experiments, 73,74 and other applications 75–77 …”

Section: Introductionmentioning

confidence: 99%

“…AutoMeKin2021 includes the following new features: (a) the rare‐event acceleration method boxed molecular dynamics in energy space (BXDE) 76,79 ; (b) a generalization of a graph theory‐based algorithm to locate TS structures for the study of non‐covalent interactions 80 ; (c) a chemical knowledge‐based method for reaction discovery; (d) a new TS search algorithm based on a bond‐order time series analysis 81 ; (e) a statistical analysis of the chemical reaction networks using the Python library NetworkX 82 ; (f) a web application for online job submission; as well as other features.…”

Section: Introductionmentioning

confidence: 99%

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AutoMeKin2021: An open‐source program for automated reaction discovery

et al. 2021

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AutoMeKin2021 is an updated version of tsscds2018, a program for the automated discovery of reaction mechanisms (J. Comput. Chem. 2018, 39, 1922). This release features a number of new capabilities: rare‐event molecular dynamics simulations to enhance reaction discovery, extension of the original search algorithm to study van der Waals complexes, use of chemical knowledge, a new search algorithm based on bond‐order time series analysis, statistics of the chemical reaction networks, a web application to submit jobs, and other features. The source code, manual, installation instructions and the website link are available at: https://rxnkin.usc.es/index.php/AutoMeKin

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Section: Methodsmentioning

confidence: 87%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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AutoMeKin2021: An open‐source program for automated reaction discovery

et al. 2021

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A Reactive Molecular Dynamics Study of Chlorinated Organic Compounds. Part II: A ChemTraYzer Study of Chlorinated Dibenzofuran Formation and Decomposition Processes

et al. 2023

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In our two-paper series, we first present the development of ReaxFF CHOCl parameters using the recently published ParAMS parametrization tool. In this second part, we update the reactive Molecular Dynamics -Quantum Mechanics coupling scheme ChemTraYzer and combine it with our new ReaxFF parameters from Part I to study formation and decomposition processes of chlorinated dibenzofurans. We introduce a self-learning method for recovering failed transition-state searches that improves the overall ChemTraYzer transition-state search success rate by 10 percentage points to a total of 48 %. With ChemTraYzer, we automatically find and quantify more than 500 reactions using transition state theory and DFT. Among the discovered chlorinated dibenzofuran reactions are numerous reactions that are new to the literature. In three case studies, we discuss the set of reactions that are most relevant to the dibenzofuran literature: (i) bimolecular reactions of the chlorinated-dibenzofuran precursors phenoxy radical and 1,3,5-trichlorobenzene, (ii) dibenzofuran chlorination and pyrolysis, and (iii) oxidation of chlorinated dibenzofurans.

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Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis

Steiner

Reiher

2022

Top Catal

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Autonomous computations that rely on automated reaction network elucidation algorithms may pave the way to make computational catalysis on a par with experimental research in the field. Several advantages of this approach are key to catalysis: (i) automation allows one to consider orders of magnitude more structures in a systematic and open-ended fashion than what would be accessible by manual inspection. Eventually, full resolution in terms of structural varieties and conformations as well as with respect to the type and number of potentially important elementary reaction steps (including decomposition reactions that determine turnover numbers) may be achieved. (ii) Fast electronic structure methods with uncertainty quantification warrant high efficiency and reliability in order to not only deliver results quickly, but also to allow for predictive work. (iii) A high degree of autonomy reduces the amount of manual human work, processing errors, and human bias. Although being inherently unbiased, it is still steerable with respect to specific regions of an emerging network and with respect to the addition of new reactant species. This allows for a high fidelity of the formalization of some catalytic process and for surprising in silico discoveries. In this work, we first review the state of the art in computational catalysis to embed autonomous explorations into the general field from which it draws its ingredients. We then elaborate on the specific conceptual issues that arise in the context of autonomous computational procedures, some of which we discuss at an example catalytic system. Graphical Abstract

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Enhancing Automated Reaction Discovery with Boxed Molecular Dynamics in Energy Space

Cited by 20 publications

References 108 publications

AutoMeKin2021: An open‐source program for automated reaction discovery

AutoMeKin2021: An open‐source program for automated reaction discovery

A Reactive Molecular Dynamics Study of Chlorinated Organic Compounds. Part II: A ChemTraYzer Study of Chlorinated Dibenzofuran Formation and Decomposition Processes

Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis

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