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Energetic and geometric aspects of the permeation of low-Z atoms through graphene sheets are investigated. Energy barriers and deformations are calculated via density functional theory for the permeation of H, He, Li and Be atoms at several…
While several studies confirmed that machine-learned potentials (MLPs) can provide accurate free energies for determining phase stabilities, the abilities of MLPs for efficiently constructing a full phase diagram of multi-component systems…
Solid state qubits from paramagnetic point defects in solids are promising platforms to realize quantum networks and novel nanoscale sensors. Recent advances in materials engineering make possible to create proximate qubits in solids that…
Nowadays sp carbon chains terminated by graphene or graphitic-like carbon are synthesized routinely in several nanotech labs. We propose an ab-initio study of such carbon-only materials, by computing their structure and stability, as well…
Graphitic carbon nitride has emerged as a versatile, metal-free semiconductor with applications spanning over broad range of domains encompassing energy storage, environmental remediation and sensing. Despite significant progress in recent…
Machine learning interatomic potentials (MLIPs) offer an efficient and accurate framework for large-scale molecular dynamics (MD) simulations, effectively bridging the gap between classical force fields and \textit{ab initio} methods. In…
We present a detailed comparison of the structural predictions of the effective many-body Brenner potential with those of ab-initio studies for known reconstructions of diamond (100) and (111) surfaces. These results suggest high…
Polymer-derived ceramics combine the thermal stability of ceramics with the versatile properties of carbon domains, but modeling their atomic-scale evolution during processing remains elusive due to the limitations of traditional…
The interaction between carbon nanostructures like quantum dots and radiation can generate different effects inside the nanomaterial, with the use of computational methods such effects can be predicted and optimize the material allowing a…
Ab initio simulations are capable of providing detailed information of material behavior at the nanoscale. Simulating experimentally relevant situations is, however, often computationally intense. Using hybrid approaches between ab initio…
In recent years, nanostructures with hexagonal polytypes of gold have been synthesised, opening new possibilities in nanoscience and technology. As bulk gold crystallizes in the \textit{fcc} phase, surface effects can play an important role…
The experimental knowledge on interlayer potential of graphenites is summarized and compared with computational results based on phenomenological models. Besides Lennard-Jones approximation, the Mie potential is discussed, Kolmogorov-Crespy…
We demonstrate how machine-learning based interatomic potentials can be used to model guest atoms in host structures. Specifically, we generate Gaussian approximation potential (GAP) models for the interaction of lithium atoms with…
A design for an ultra-high Q photonic crystal nanocavity engineered to interact with nitrogen-vacancy (NV) centers located near the surface of a single crystal diamond sample is presented. The structure is based upon a nanowire photonic…
Neural networks are being used to make new types of empirical chemical models as inexpensive as force fields, but with accuracy close to the ab-initio methods used to build them. Besides modeling potential energy surfaces, neural-nets can…
Chemical segregation and structural transitions at interfaces are important nanoscale phenomena, making them natural targets for atomistic modeling, yet interatomic potentials must be fit to secondary physical properties. To isolate the…
There is significant interest in potential experimental tests of macroscopic quantum effects, both to test potential modifications to quantum theory and to probe the quantum nature of gravity. A proposed platform with which to generate the…
Nitrogen vacancy (NV) centers in diamond have emerged in recent years as leading quantum sensors in various modalities. Most applications benefit from shallow NVs, enabling higher sensitivity and resolution. However, near surface NVs ($<$…
Carbon materials are attracting increasing attention due to the novelty of the associated physical properties and the potential applications in high-tech devices. The possibility to achieve outstanding properties in macroscopic carbon…
Large-scale simulations of plastic deformation and phase transformations in alloys require reliable classical interatomic potentials. We construct an embedded-atom method potential for niobium as the first step in alloy potential…