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This paper focuses on molecular dynamics (MD) modeling of graphene reinforced cross-linked epoxy (Gr-Ep) nanocomposite. The goal is to study the influence of geometry, and concentration of reinforcing nanographene sheet (NGS) on interfacial…
This study utilises linear-scaling density functional theory (DFT) and develops a new machine-learning potential for carbon and nitrogen (GAP-CN), based on the carbon potential (GAP20), to investigate the interaction between carbon…
Tensile tests at room temperature are performed using molecular dynamics on all configurations of single-walled carbon nanotubes up to 4 nm in diameter. Distributions of the Young's modulus, Poisson's ratio, ultimate tensile strength and…
Bottom-up on-surface synthesized nanoporous graphenes (NPGs), realized as 2D arrays of laterally covalently bonded $\pi$-conjugated graphene nanoribbons (GNRs), are a family of carbon nanomaterials that are receiving increasing attention…
We study the deposition of tetrahedral amorphous carbon (ta-C) films from molecular dynamics simulations based on a machine-learned interatomic potential trained from density-functional theory data. For the first time, the high $sp^3$…
Accurate and efficient characterization of nanoparticles (NPs), particularly regarding particle size distribution, is essential for advancing our understanding of their structure-property relationships and facilitating their design for…
Two-dimensionally extended amorphous carbon ("amorphous graphene") is a prototype system for disorder in 2D, showing a rich and complex configurational space that is yet to be fully understood. Here we explore the nature of amorphous…
We introduce interatomic potentials for tungsten in the bcc crystal phase and its defects within the Gaussian Approximation Potential (GAP) framework, fitted to a database of first principles density functional theory (DFT) calculations. We…
An accurate description of atomic interactions, such as that provided by first principles quantum mechanics, is fundamental to realistic prediction of the properties that govern plasticity, fracture or crack propagation in metals. However,…
In this paper, we propose a combined modeling of molecular mechanics (MM) and the tight-binding (TB) approach, which enables us to study the effect of factors such as external local forces, constraints, and vacancy defects on electronic…
Incorporating nanomaterials into hydrogels allows for the creation of versatile materials with properties that can be precisely tailored by manipulating their nanoscale structures, leading to a wide range of bulk properties. Investigating…
Interatomic potentials are essential for driving molecular dynamics (MD) simulations, directly impacting the reliability of predictions regarding the physical and chemical properties of materials. In recent years, machine-learned potentials…
The elastic properties of materials derive from their electronic and atomic nature. However, simulating bulk materials fully at these scales is not feasible, so that typically homogenized continuum descriptions are used instead. A seamless…
Bone structure is generally hierarchically organized into organic (collagen, proteins,...), inorganic (hydroxyapatite (HAP)) components. However, many fundamental mechanisms of the biomineralization processes such as HAP formation, the…
Low energy ion-beam sputtering of GaSb results in self-organized nanostructures, with the potential of structuring large surface areas. Characterisation of such nanostructures by optical methods is studied and compared to direct (local)…
The separation of carbon dioxide CO$_2$ from nitrogen gas (N$_2$), the main component of flue gas, has become an emerging action to mitigate climate change. Feasible and efficient approaches to exploring the separation properties of…
Granular packing structures of cohesive micro-sized particles with different sizes and size distributions, including mono-sized, uniform and Gaussian distribution, are investigated by using two different history dependent contact models…
Polymer nanocomposites (PNCs) offer a broad range of thermophysical properties that are linked to their compositions. However, it is challenging to establish a universal composition-property relation of PNCs due to their enormous…
Machine-learned coarse-grained (CG) models often suffer from noisy training data, limiting their accuracy and transferability. We propose a method to generate low-noise training data based on the potential of mean force by constraining CG…
We examine nanoparticles (NPs) forming polyhedral sections of the ideal cubic lattice, simple (sc), body centered (bcc), and face centered (fcc) cubic, which are confined by facets characterized by densest and second densest {h k l}…