Interphase Formation during Curing: Reactive Coarse Grained Molecular Dynamics Simulations

Farah, Karim; Leroy, Frédéric; Müller‐Plathe, Florian; Böhm, Michael

doi:10.1021/jp203480q

Cited by 26 publications

(23 citation statements)

References 46 publications

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“…Interestingly, the correlation between the spatial localization of initiators, the velocity of the reaction and the resulting polymer length distribution discussed here appears pertinent also in the case of a surface‐initiated growth of polymer brushes 53. The present RMD scheme has also been applied to study the formation of an interphase during a curing process in contact with an idealized surface 55…”

Section: Reactive Methods Based On a Reaction Cutoff Distancementioning

confidence: 81%

“…In this RMD branch, a connection between two particles is made if they are found within a pre‐defined sphere. Herein, approaches that rely on a distance criterion are classified as “reaction cutoff distance” methods 39–55. Here, the interaction between reacting particles switches from nonbonded to bonded or vice versa without transition.…”

Section: Introductionmentioning

confidence: 99%

“…44, 47, 50–52, 54 In later articles they have been used to provide qualitative insight into polymerization processes 40. 42, 43, 45, 46, 48, 49, 53–55 The second class of implementations intents to develop accurate and predictive reactive force fields as discussed above. These approaches are classified herein as RMD methods based on “empirical reactive force fields”.…”

Section: Introductionmentioning

confidence: 99%

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Classical Reactive Molecular Dynamics Implementations: State of the Art

2012

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Reactive molecular dynamics (RMD) implementations equipped with force field approaches to simulate both the time evolution as well as chemical reactions of a broad class of materials are reviewed herein. We subdivide the RMD approaches developed during the last decade as well as older ones already reviewed in 1995 by Srivastava and Garrison and in 2000 by Brenner into two classes. The methods in the first RMD class rely on the use of a reaction cutoff distance and employ a sudden transition from the educts to the products. Due to their simplicity these methods are well suited to generate equilibrated atomistic or material‐specific coarse‐grained polymer structures. In connection with generic models they offer useful qualitative insight into polymerization reactions. The methods in the second RMD class are based on empirical reactive force fields and implement a smooth and continuous transition from the educts to the products. In this RMD class, the reactive potentials are based on many‐body or bond‐order force fields as well as on empirical standard force fields, such as CHARMM, AMBER or MM3 that are modified to become reactive. The aim with the more sophisticated implementations of the second RMD class is the investigation of the reaction kinetics and mechanisms as well as the evaluation of transition state geometries. Pure or hybrid ab initio, density functional, semi‐empirical, molecular mechanics, and Monte Carlo methods for which no time evolution of the chemical systems is achieved are excluded from the present review. So are molecular dynamics techniques coupled with quantum chemical methods for the treatment of the reactive regions, such as Car–Parinello molecular dynamics.

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Section: Reactive Methods Based On a Reaction Cutoff Distancementioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Classical Reactive Molecular Dynamics Implementations: State of the Art

2012

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“…[24][25][26] There is a broad range of reviews on the subject of investigating chemical reactions based on established treatments 27 of the potential energy surfaces with applications in gas phase, 28,29 solution, 30,31 and enzymatic reaction [32][33][34][35][36] dynamics and the technology has also been extended to coarse-grained simulations. 37 The present work focuses on more recent developments and applications of machine learning techniques applied to problems involving reactions in the gas phase, in solution, and in enzymes. Most problems concerning the representation of the underlying potential energy surfaces are excluded, as these are already well covered by accompanying contributions to this special issue.…”

Section: Introductionmentioning

confidence: 99%

Transformative Applications of Machine Learning for Chemical Reactions

Meuwly

2021

Preprint

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Machine learning techniques applied to chemical reactions has a long history. The present contribution discusses applications ranging from small molecule reaction dynamics to platforms for reaction planning. ML-based techniques can be of particular interest for problems which involve both, computation and experiments. For one, Bayesian inference is a powerful approach to include knowledge from experiment in improving computational models. ML-based methods can also be used to handle problems that are formally intractable using conventional approaches, such as exhaustive characterization of state-to-state information in reactive collisions. Finally, the explicit simulation of reactive networks as they occur in combustion has become possible using machine-learned neural network potentials. This review provides an overview of the questions that can and have been addressed using machine learning techniques and an outlook discusses challenges in this diverse and stimulating field. It is concluded that ML applied to chemistry problems as practiced and conceived today has the potential to transform the way with which the field approaches problems involving chemical reactions, both, in research and academic teaching.

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“…With this, one can prepare the force‐field elements based on a different atomistic representation that will reflect changes due to the chemical reactions. During the cross‐linking procedure, we can take into account that the underlying atomistic structure is also modified by changing the types of CG beads after the bond is formed …”

Section: Introductionmentioning

confidence: 99%

Coarse‐grained molecular dynamics simulations of polymerization with forward and backward reactions

et al. 2018

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We develop novel parallel algorithms that allow molecular dynamics simulations in which byproduct molecules are created and removed because of the chemical reactions during the molecular dynamics simulation. To prevent large increases in the potential energy, we introduce the byproduct molecules smoothly by changing the non-bonded interactions gradually. To simulate complete equilibrium reactions, we allow the byproduct molecules attack and destroy created bonds. Modeling of such reactions are, for instance, important to study the pore formation due to the presence of e.g. water molecules or development of polymer morphology during the process of splitting off byproduct molecules. Another concept that could be studied is the degradation of polymeric materials, a very important topic in a recycling of polymer waste. We illustrate the method by simulating the polymerization of polyethylene terephthalate (PET) at the coarse-grained level as an example of a polycondensation reaction with water as a byproduct. The algorithms are implemented in a publicly available software package and are easily accessible using a domain-specific language that describes chemical reactions in an input configuration file.

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Interphase Formation during Curing: Reactive Coarse Grained Molecular Dynamics Simulations

Cited by 26 publications

References 46 publications

Classical Reactive Molecular Dynamics Implementations: State of the Art

Classical Reactive Molecular Dynamics Implementations: State of the Art

Transformative Applications of Machine Learning for Chemical Reactions

Coarse‐grained molecular dynamics simulations of polymerization with forward and backward reactions

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