相关论文: Implicit Chain Particle Model for Polymer Grafted …

The advent of Graph Neural Networks (GNNs) has revolutionized the field of machine learning, offering a novel paradigm for learning on graph-structured data. Unlike traditional neural networks, GNNs are capable of capturing complex…

Polymers, macromolecules formed from covalently bonded monomers, underpin countless technologies and are indispensable to modern life. While deep learning is advancing polymer science, existing methods typically represent the whole polymer…

Materials exhibit geometric structures across mesoscopic to microscopic scales, influencing macroscale properties such as appearance, mechanical strength, and thermal behavior. Capturing and modeling these multiscale structures is…

Successful ways of fully exploiting the excellent structural and multifunctional performance of graphene and related materials are of great scientific and technological interest. New opportunities are provided by the fabrication of a novel…

Brush-like structures emerge from stretching of long polymer chains, densely grafted on to the surface of an impermeable substrate. They arise due to the competition between conformational entropic elasticity of polymer chains and excluded…

Particle-based systems provide a flexible and unified way to simulate physics systems with complex dynamics. Most existing data-driven simulators for particle-based systems adopt graph neural networks (GNNs) as their network backbones, as…

Polymers are large macromolecules composed of repeating structural units known as monomers and are widely applied in fields such as energy storage, construction, medicine, and aerospace. However, existing graph neural network methods,…

Simulation of the dynamics of physical systems is essential to the development of both science and engineering. Recently there is an increasing interest in learning to simulate the dynamics of physical systems using neural networks.…

A large body of experimental work on the microstructure and dynamics of simplifiedindustrial nanocomposites made of disordered silica filler in a styrene-butadiene matrixby solid-phase mixing is regrouped and critically discussed in this…

Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale processes for which some data are also available. In some…

Finite element analysis of solid mechanics is a foundational tool of modern engineering, with low-order finite element methods and assembled sparse matrices representing the industry standard for implicit analysis. We use performance models…

In simulations, particles are traditionally treated as rigid platforms with variable sizes, shapes and interaction parameters. While this representation is applicable for rigid core platforms, particles consisting of soft platforms (e.g.…

This paper presents $\Psi$-GNN, a novel Graph Neural Network (GNN) approach for solving the ubiquitous Poisson PDE problems with mixed boundary conditions. By leveraging the Implicit Layer Theory, $\Psi$-GNN models an "infinitely" deep…

Porous graphene (PG) is a graphene derivative endowed of nanoporous architectures. This material possesses a particular structure with interconnected networks of high pore volume, producing membranes with a large surface area. Experiments…

Molecular dynamics (MD) simulations were performed to study the formation process of nanopores in a suspended graphene sheet irradiated by using energetic ions though a mask. By controlling the ion parameters including mass, energy and…

A hallmark of meso-scale interfacial fluids is the multi-faceted, scale-dependent interfacial energy, which often manifests different characteristics across the molecular and continuum scale. The multi-scale nature imposes a challenge to…

We calculate statistical properties of amorphous polymer chains between crystalline lamellae by self-consistent field model simulations. In our model, an amorphous subchain is modelled as a polymer chain of which ends are grafted onto the…

Graph neural networks (GNNs) have been used extensively for addressing problems in drug design and discovery. Both ligand and target molecules are represented as graphs with node and edge features encoding information about atomic elements…

Engineering components must meet increasing technological demands in ever shorter development cycles. To face these challenges, a holistic approach is essential that allows for the concurrent development of part design, material system and…

We study the stress distribution in a polymer brush material over a range of graft densities using molecular dynamics (MD) simulations and theory. Flexible polymer chains are treated as beads connected by nonlinear springs governed by a…