Multiscale modeling of nanomaterials

Fitzgerald, George; DeJoannis, J.; Meunier, Marc

doi:10.1016/b978-1-78242-228-0.00001-6

Cited by 13 publications

(6 citation statements)

References 201 publications

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“…43 However, the computational demands of these all-atom models make it challenging to apply them in long and large-scale simulation systems. In this context, the coarse-grained (CG) models 44,45 have been proven to be a reasonable and helpful way to study mesoscale physical processes. As mentioned earlier, machine learning can be employed to develop interatomic potential that offers both high accuracy and fast speed.…”

Section: Computational Model and Detailsmentioning

confidence: 99%

Coupling at the molecular scale between the graphene nanosheet and water and its effect on the thermal conductivity of the nanofluid

Pan,

Jin,

et al. 2024

Phys. Chem. Chem. Phys.

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Section: Computational Model and Detailsmentioning

confidence: 99%

Coupling at the molecular scale between the graphene nanosheet and water and its effect on the thermal conductivity of the nanofluid

Pan,

Jin,

et al. 2024

Phys. Chem. Chem. Phys.

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“…Incorporating the variables of composition, processing, and environment into computational models is thus essential for new material design, performance evaluation, and damage and life prediction. The multi‐scale design [46,47] encompasses spatial scales, including atomic, microscopic, and macroscopic scales as well as temporal scales, such as nanoseconds, microseconds, or longer. Multiple objectives [48,49] encompass a wide range of properties, including physical properties like luminous efficiency, thermal conductivity, electrical conductivity, and dielectric constant and mechanical properties such as elasticity, strength, toughness, and fatigue resistance as well as chemical properties like oxidation resistance and corrosion resistance.…”

Section: Htc In Materials Developmentmentioning

confidence: 99%

Advances in data‐assisted high‐throughput computations for material design

Xu,

Zhang,

Huo

et al. 2023

Materials Genome Engineering Advances

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Extensive trial and error in the variable space is the main cause of low efficiency and high cost in material development. The experimental tasks can be reduced significantly in the case that the variable space is narrowed down by reliable computer simulations. Because of their numerous variables in material design, however, the variable space is still too large to be accessed thoroughly even with a computational approach. High‐throughput computations (HTC) make it possible to complete a material screening in a large space by replacing the conventionally manual and sequential operations with automatic, robust, and concurrent streamlines. The efficiency of HTC, which is one of the pillars of materials genome engineering, has been verified in many studies, but its applications are still limited by demanding computational costs. Introduction of data mining and artificial intelligence into HTC has become an effective approach to solve the problem. In the past years, many studies have focused on the development and application of HTC and data combined approaches, which is considered as a new paradigm in computational materials science. This review focuses on the main advances in the field of data‐assisted HTC for material research and development and provides our outlook on its future development.

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“…Here, W (l) and b (l) are the weights and biases, respectively, of layer l. We have also introduced the degree matrix, (10) so that the term normalizes the adjacency matrix based on the degree of each atom.…”

Section: Model Selection 221 Ecfp Modelmentioning

confidence: 99%

“…Coarse graining, which groups atoms into beads, makes simulating longer time scales accessible. − For example, Khedr and Striolo used dissipative particle dynamics (DPD) to model the CMCs of two polyoxyethylene octyl ether surfactants (C 8 E n ). Rather than using the free surfactant concentration by itself, the authors calculated the volume fraction of the free surfactant in the accessible component of the aqueous phase.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Accuracy of Critical Micelle Concentration Predictions Using Deep Learning

Moriarty,

Kobayashi,

Salvalaglio

et al. 2023

J. Chem. Theory Comput.

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This paper presents a novel approach to predicting critical micelle concentrations (CMCs) by using graph neural networks (GNNs) augmented with Gaussian processes (GPs). The proposed model uses learned latent space representations of molecules to predict CMCs and estimate uncertainties. The performance of the model on a data set containing nonionic, cationic, anionic, and zwitterionic molecules is compared against a linear model that works with extended connectivity fingerprints (ECFPs). The GNN-based model performs slightly better than the linear ECFP model when there is enough well-balanced training data and achieves predictive accuracy that is comparable to published models that were evaluated on a smaller range of surfactant chemistries. We illustrate the applicability domain of our model using a molecular cartogram to visualize the latent space, which helps to identify molecules for which predictions are likely to be erroneous. In addition to accurately predicting CMCs for some surfactant classes, the proposed approach can provide valuable insights into the molecular properties that influence CMCs.

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Multiscale modeling of nanomaterials

Cited by 13 publications

References 201 publications

Coupling at the molecular scale between the graphene nanosheet and water and its effect on the thermal conductivity of the nanofluid

Coupling at the molecular scale between the graphene nanosheet and water and its effect on the thermal conductivity of the nanofluid

Advances in data‐assisted high‐throughput computations for material design

Analyzing the Accuracy of Critical Micelle Concentration Predictions Using Deep Learning

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