Extracting the mechanisms and kinetic models of complex reactions from atomistic simulation data

Wu, Yanze; Sun, Huai; Wu, Liang; Deetz, Joshua D.

doi:10.1002/jcc.25809

Cited by 26 publications

(31 citation statements)

References 45 publications

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“…Since reactive MD simulation tracks all chemical reactions in real time, one can even deduce the rate constants for individual reactions from a single MD trajectory by statistical analysis. We extracted the ten most statistically significant reactions from the trajectory and calculated their rate constants based on the algorithms developed in previous studies 50,51 . As shown in Supplementary Table 2, most of the rate constants agree well with the GRI_Mech data 48 .…”

Section: Discussionmentioning

confidence: 99%

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Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation

et al. 2020

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Combustion is a complex chemical system which involves thousands of chemical reactions and generates hundreds of molecular species and radicals during the process. In this work, a neural network-based molecular dynamics (MD) simulation is carried out to simulate the benchmark combustion of methane. During MD simulation, detailed reaction processes leading to the creation of specific molecular species including various intermediate radicals and the products are intimately revealed and characterized. Overall, a total of 798 different chemical reactions were recorded and some new chemical reaction pathways were discovered. We believe that the present work heralds the dawn of a new era in which neural network-based reactive MD simulation can be practically applied to simulating important complex reaction systems at ab initio level, which provides atomic-level understanding of chemical reaction processes as well as discovery of new reaction pathways at an unprecedented level of detail beyond what laboratory experiments could accomplish.

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Section: Discussionmentioning

confidence: 99%

“…Both networks use the ResNet architecture 75 . The size of the embedding network was set to (25,50,100) and the size of the embedding matrix was set to 12. The size of the fitting network is set to (240, 240, 240).…”

Section: Methodsmentioning

confidence: 99%

Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation

et al. 2020

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“…However, computationally severe storing and time constraints permitting to obtain myriads of “on-the-fly trajectories” require a great effort toward the aim of generating realistic reactive kinetic data: this in spite of the fact that a wide research activity has been pursued, aiming at developing techniques capable of accurately predicting kinetic reaction rate constants from molecular dynamics simulations. Among examples that have been tackled in recent years, we cite (Pomerantz et al, 2005; Coutinho et al, 2015a; Döntgen et al, 2015; Fleming et al, 2016; Wu et al, 2019) and references therein. However, calculation of reaction rate constants has been limited by the arduous procedures both to accurately characterize reactive activated complexes of many body systems and to overcome the inherent difficulties of producing a number of trajectories with statistical consistency and reasonable completeness in the filling of the dynamically relevant parts of the phase-space.…”

Section: Additional and Final Remarksmentioning

confidence: 99%

Temperature Dependence of Rate Processes Beyond Arrhenius and Eyring: Activation and Transitivity

2019

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Advances in the understanding of the dependence of reaction rates from temperature, as motivated from progress in experiments and theoretical tools (e. g., molecular dynamics), are needed for the modeling of extreme environmental conditions (e.g., in astrochemistry and in the chemistry of plasmas). While investigating statistical mechanics perspectives (Aquilanti et al., 2017b , 2018 ), the concept of transitivity was introduced as a measure for the propensity for a reaction to occur. The Transitivity plot is here defined as the reciprocal of the apparent activation energy vs. reciprocal absolute temperature. Since the transitivity function regulates transit in physicochemical transformations, not necessarily involving reference to transition-state hypothesis of Eyring, an extended version is here proposed to cope with general types of transformations. The transitivity plot permits a representation where deviations from Arrhenius behavior are given a geometrical meaning and make explicit a positive or negative linear dependence of transitivity for sub - and super -Arrhenius cases, respectively. To first-order in reciprocal temperature, the transitivity function models deviations from linearity in Arrhenius plots as originally proposed by Aquilanti and Mundim: when deviations are increasingly larger, other phenomenological formulas, such as Vogel-Fulcher-Tammann, Nakamura-Takayanagi-Sato, and Aquilanti-Sanches-Coutinho-Carvalho are here rediscussed from the transitivity concept perspective and with in a general context. Emphasized is the interest of introducing into this context modifications to a very successful tool of theoretical kinetics, Eyring's Transition-State Theory: considering the behavior of the transitivity function at low temperatures, in order to describe deviation from Arrhenius behavior under the quantum tunneling regime, a “ d -TST” formulation was previously introduced (Carvalho-Silva et al., 2017 ). In this paper, a special attention is dedicated to a derivation of the temperature dependence of viscosity, making explicit reference to feature of the transitivity function, which in this case generally exhibits a super -Arrhenius behavior. This is of relevance also for advantages of using the transitivity function for diffusion-controlled phenomena.

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“…Changes in the connectivity are then interpreted as reactions. 6,40,[68][69][70] A related approach monitors changes in the bond order matrix. 71-73…”

Section: State-to-state Networkmentioning

confidence: 99%

Expansive Quantum Mechanical Exploration of Chemical Reaction Paths

Baiardi,

Grimmel,

Steiner

et al. 2021

Preprint

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Quantum mechanical methods have been well established for the elucidation of reaction paths of chemical processes and for the explicit dynamics of molecular systems. While they are usually deployed in routine manual calculations on reactions for which some insights are already available (typically from experiment), new algorithms and continuously increasing capabilities of modern computer hardware allow for exploratory open-ended computational campaigns that are unbiased and therefore enable unexpected discoveries. Highly efficient and even automated procedures facilitate systematic approaches toward the exploration of uncharted territory in molecular transformations and dynamics. In this work, we elaborate on such explorative approaches that range from reaction network explorations with (stationary) quantum chemical methods to explorative molecular dynamics and migrant wave-packet dynamics. The focus is on recent developments that cover the following strategies. (i) Pruning search options for elementary reaction steps by heuristic rules based on the first principles of quantum mechanics: Rules are required for reducing the combinatorial explosion of potentially reactive atom pairings and rooting them in concepts derived from the electronic wave function makes them applicable to any molecular system. (ii) Enforcing reactive events by external biases: Inducing a reaction requires constraints that steer and direct elementary-step searches, which can be formulated in terms of forces, velocities, or supplementary potentials. (iii) Manual steering facilitated by interactive quantum mechanics: As ultrafast quantum chemical methods allow for real-time manual interactions with molecular systems, human-intuition guided paths can be easily explored with suitable human-machine interfaces. (iv) New approaches for transition-state optimization with continuous curve representations can provide stable schemes to be driven in an automated way by allowing for an efficient tuning of the curve's parameters (instead of a manipulation of a collection of structures along the path), and (v) reactive molecular dynamics and direct wave-packet propagation exploit the equations

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Extracting the mechanisms and kinetic models of complex reactions from atomistic simulation data

Cited by 26 publications

References 45 publications

Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation

Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation

Temperature Dependence of Rate Processes Beyond Arrhenius and Eyring: Activation and Transitivity

Expansive Quantum Mechanical Exploration of Chemical Reaction Paths

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