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Though offering unprecedented pathways to molecular dynamics (MD) simulations of technologically-relevant materials and conditions, machine-learning interatomic potentials (MLIPs) are typically trained for ``simple'' materials and…
We examine the structure of compact metal nanoparticles (NPs) forming polyhedral sections of face centered (fcc) and body centered (bcc) cubic lattices, which are confined by facets characterized by highly dense {100}, {110}, and {111}…
Recently, laser-assisted chemical vapor deposition was used to synthesize a free-standing, continuous, and stable monolayer amorphous carbon (MAC). MAC is a pure carbon structure composed of randomly distributed five, six, seven, and eight…
The Gaussian approximation potential (GAP) is an accurate machine-learning interatomic potential that was recently extended to include the description of radiation effects. In this study, we seek to validate a faster version of GAP, known…
Deterministic and versatile approaches to sample preparation on nanoscopic scales are important in many fields including photonics, electronics, biology and material science. However, challenges exist in meeting many nanostructuring…
Porous graphene (PG) forms a class of graphene-related materials with nanoporous architectures. Their unique atomic arrangements present interconnected networks with high surface area and high pore volume. Some remarkable properties of PG,…
The link between composition, microstructure and mechanics of NASH and KASH gels is elusive, even in pure metakaolin-based geopolymers. This article exploits molecular mechanics, coarse-grained nanomechanics and micromechanics, to interpret…
This thesis is focused on numerical simulations of the laser interaction with porous materials. A possibility of well-controlled processing is particularly important for the laser based micro-structuring of porous glass and nano-machining…
The chemical exfoliation of non-van der Waals (vdW) materials to ultrathin nanosheets remains a formidable challenge. This difficulty arises from the strong preference of these materials to engage in three-dimensional chemical bonding,…
Porous nanomaterials have recently attracted a lot of attention due to various properties and potential applications. In this study, carbon nanoparticles (CNPs) were synthesized by the one-pot hydrothermal carbonization (HTC) using…
Graphene nanomeshes (GNMs) are novel materials that recently raised a lot of interest. They are fabricated by forming a lattice of pores in graphene. Depending on the pore size and pore lattice constant, GNMs can be either semimetallic or…
Supported metal nanoparticle (NP) catalysts are vital for the sustainable production of chemicals, but their design and implementation are limited by the ability to identify and characterize their structures and atomic sites that are…
Carbon nanoribbons (CNRs) are graphene (planar) structures with large aspect ratio. Carbon nanobelts (CNBs) are small graphene nanoribbons rolled up into spiral-like structures, i. e., carbon nanoscrolls (CNSs) with large aspect ratio. In…
The highly anisotropic thermal conductivity in layered materials is crucial for a broad range of applications such as thermal management of electronic devices, thermal insulation, and thermoelectrics. Understanding of anisotropic thermal…
Establishing a mapping between nanocatalysts structure and their catalytic properties is essential for efficient design. To this end, we demonstrate the accuracy of a general machine learning framework on a representative and challenging…
In recent years, machine learning interatomic potentials (MLIPs) have attracted significant attention as a method that enables large-scale, long-time atomistic simulations while maintaining accuracy comparable to electronic structure…
Predicting the spatial configuration of gas molecules in nanopores of shale formations is crucial for fluid flow forecasting and hydrocarbon reserves estimation. The key challenge in these tight formations is that the majority of the pore…
In this work, we study the damage in crystalline molybdenum material samples due to neutron bombardment in a primary knock-on atom range of 0.5-10 keV at room temperature. We perform machine learned molecular dynamics (MD) simulations with…
Computational modeling is usually applied to aid experimental exploration of advanced materials to better understand the fundamental plasticity mechanisms during mechanical testing. In this work, we perform Molecular dynamics (MD)…
Twisted layered van-der-Waals materials often exhibit unique electronic and optical properties absent in their non-twisted counterparts. Unfortunately, predicting such properties is hindered by the difficulty in determining the atomic…