Development of a ReaxFF Reactive Force Field for the Pt–Ni Alloy Catalyst

Shin, Yun Kyung; Gai, Lili; Raman, Sumathy; Duin, Adri C. T. van

doi:10.1021/acs.jpca.6b06770

Cited by 76 publications

(54 citation statements)

References 61 publications

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“…Such processes can span long periods of time (>1 ns) and are not yet accessible to ab initio levels of theory. Therefore, the use of reactive force fields in the study of diffusion and transport mechanisms should be of utmost importance for a smarter, simulation‐based design of batteries, fuel cells, and catalysts among other advanced functional materials.…”

Section: Large Scale Methods/molecular Dynamicsmentioning

confidence: 99%

Modeling Diffusion in Functional Materials: From Density Functional Theory to Artificial Intelligence

Elbaz

Furman

Toroker

2019

Adv Funct Materials

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Diffusion describes the stochastic motion of particles and is often a key factor in determining the functionality of materials. Modeling diffusion of atoms can be very challenging for heterogeneous systems with high energy barriers. In this report, popular computational methodologies are covered to study diffusion mechanisms that are widely used in the community and both their strengths and weaknesses are presented. In static approaches, such as electronic structure theory, diffusion mechanisms are usually analyzed within the nudged elastic band (NEB) framework on the ground electronic surface usually obtained from a density functional theory (DFT) calculation. Another common approach to study diffusion mechanisms is based on molecular dynamics (MD) where the equations of motion are solved for every time step for all the atoms in the system. Unfortunately, both the static and dynamic approaches have inherent limitations that restrict the classes of diffusive systems that can be efficiently treated. Such limitations could be remedied by exploiting recent advances in artificial intelligence and machine learning techniques. Here, the most promising approaches in this emerging field for modeling diffusion are reported. It is believed that these knowledge-intensive methods have a bright future ahead for the study of diffusion mechanisms in advanced functional materials.observed in liquids, gases, and solid-state materials. One of the main cornerstones for the macroscopic understanding of diffusion was laid down in the mid-19th century by Adolf Fick. By observing Graham's experiments in gases and salt water, and by the analogy between Fourier's law of thermal conduction and diffusion, Fick developed two equations known as the Fick's laws , , , J D C x y z t

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Section: Large Scale Methods/molecular Dynamicsmentioning

confidence: 99%

Modeling Diffusion in Functional Materials: From Density Functional Theory to Artificial Intelligence

Elbaz

Furman

Toroker

2019

Adv Funct Materials

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“…Since an increase in complexity concurs with an increase of the computational effort, there is an additional focus on the development of faster methods. These are often based on new developments in the field of machine learning,[77d,92] parameterization, and genetic algorithms …”

Section: Theorymentioning

confidence: 99%

Moving Frontiers in Transition Metal Catalysis: Synthesis, Characterization and Modeling

et al. 2019

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Nanosized transition metal particles are important materials in catalysis witha key role not only in academic research but also in many processes with industrial and societal relevance. Although small improvements in catalytic properties can lead to significant economic and environmental impacts, it is only now that knowledge-based design of such materials is emerging, partly because the understanding of catalytic mechanisms on nanoparticle surfaces is increasingly improving. A knowledge-based design requires bottom-up synthesis of well-defined model catalysts, an understanding of the catalytic nanomaterials "at work" (operando), and both a detailed understanding and a prediction by theoretical methods. This article reports on progress in colloidal synthesis of transition metal nanoparticles for preparation of model catalysts to close the materials gap between the discoveries of fundamental surface science and industrial application. The transition metal particles, however, often undergo extensive transformations when applied to the catalytic process and much progress has recently been achieved operando characterization techniques under relevant reaction conditions. They allow better understanding of size/structure-activity correlations in these systems. Moreover, the growth of computing power and the improvement of theoretical methods uncover mechanisms on nanoparticles and have recently predicted highly active particles for CO/CO 2 hydrogenation or direct H 2 O 2 synthesis.

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“…Here, initial reaction networks are first constructed from the output of our graph-driven sampling (GDS) algorithm, as described below and reported previously. Using a reactive force-field, namely ReaxFF, [42][43][44][45] for computational efficiency (at the obvious expense of some accuracy), we subsequently evaluate the reaction energies and TS barriers for all reactions in the network, enabling us to provide some directionality and weights to all network edges. Finally, we introduce a series of network analyses, based on depth-first search (DFS), which can be used to extract the most likely reaction mechanisms leading to formation of any user-defined product species, given the thermodynamic and kinetic data available in the full network.…”

Section: Introductionmentioning

confidence: 99%

Traversing Dense Networks of Elementary Chemical Reactions to Predict Minimum‐Energy Reaction Mechanisms

2019

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Numerous different algorithms have been developed over the last few years which are capable of generating large, dense chemical reaction networks describing the inherent chemical reactivity of a collection of discrete molecules. For all elementary reactions in a given reaction network, reaction rate calculations, followed by direct micro-kinetic modelling, enables one to predict macroscopic outcomes (e. g. rate laws, product selectivity) based on atomistic input data. However, for chemical reaction networks containing thousands of reactant molecules, such simulations can be extremely time-consuming; in addition, the complex coupled time-dependence of molecular concentrations can present challenges when seeking essential mechanistic features. In this Article, we instead present an algorithm which seeks to predict the "most likely" reaction mechanism, or competing mechanisms, connecting any two user-selected reactant and product species, given a previouslygenerated reaction network as input. The approach is successfully tested for reaction networks (containing tens of thousands of possible reactions) describing the carbon monoxide oxidation on platinum nanoparticles.

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Development of a ReaxFF Reactive Force Field for the Pt–Ni Alloy Catalyst

Cited by 76 publications

References 61 publications

Modeling Diffusion in Functional Materials: From Density Functional Theory to Artificial Intelligence

Modeling Diffusion in Functional Materials: From Density Functional Theory to Artificial Intelligence

Moving Frontiers in Transition Metal Catalysis: Synthesis, Characterization and Modeling

Traversing Dense Networks of Elementary Chemical Reactions to Predict Minimum‐Energy Reaction Mechanisms

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