Related papers: SiC-TGAP: A machine learning interatomic potential…
Silicon carbide is widely used in electronics, ceramics, and renewable energy due to its exceptional hardness and resistance. In this study, we investigate the effects of hydrostatic and uniaxial pressure (both compressive and tensile) on…
We develop an empirical potential for silicon which represents a considerable improvement over existing models in describing local bonding for bulk defects and disordered phases. The model consists of two- and three-body interactions with…
Amorphous carbon (a-C) materials have diverse interesting and useful properties, but the understanding of their atomic-scale structures is still incomplete. Here, we report on extensive atomistic simulations of the deposition and growth of…
New interatomic potentials describing defects, plasticity and high temperature phase transitions for Ti are presented. Fitting the martensitic hcp-bcc phase transformation temperature requires an efficient and accurate method to determine…
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…
Silicon carbide (SiC) is an attractive semiconductor material for applications in power electronic devices. However, fabrication of a high-quality SiC/SiO${}_2$ interface has been a challenge. It is well-known that there is a great…
To increase the scientific output of particle physics experiments, upgrades are underway at all major accelerator facilities to significantly improve the luminosity. Consequently, the solid-state detectors used in the experiments will…
Silicon Carbide (SiC) ceramic matrix composite (CMC) cladding is currently being pursued as one of the leading candidates for accident-tolerant fuels. To enable an improved understanding of SiC-SiC composite performance, the development of…
Machine Learning Interatomic Potentials (MLIPs) are a modern computational method that allows achieving near-quantum mechanical accuracy (DFT) while still describing large-scale systems in molecular dynamics (MD) simulations. In this work,…
Silicon carbide (SiC) is a promising material for radiation monitoring in harsh environments, due to its low dark current, high breakdown voltage, high thermal conductivity, and radiation hardness.~This work investigates a SiC-based…
Based on the parameters from published ab-inito theoretical and experimental studies, and combining Molecular Dynamics (MD) and kinetic Monte Carlo (KMC) simulations, a framework of multi-scale modeling is developed to investigate the…
Molecular dynamics (MD) simulations of successive collision cascades within the same simulation domain were performed using two different inter-atomic potentials (IAP) in tungsten, one EAM based and the other a `quantum accurate' machine…
We present a general-purpose machine learning (ML) interatomic potential for carbon and hydrogen which is capable of simulating various materials and molecules composed of these elements. This ML interatomic potential is trained using the…
We explore different ways to simplify the evaluation of the smooth overlap of atomic positions (SOAP) many-body atomic descriptor [Bart\'{o}k et al., Phys. Rev. B 87, 184115 (2013)]. Our aim is to improve the computational efficiency of…
Tungsten is a promising candidate material in fusion energy facilities. Molecular dynamics (MD) simulations reveal the atomistic scale mechanisms, so they are crucial for the understanding of the macroscopic property deterioration of…
Silicon carbide (SiC) emerges as a promising ceramic material for high-temperature structural applications, especially within the aerospace sector. The utilization of SiC-based ceramic matrix composites (CMCs) instead of superalloys in…
Understanding the sodium-storage mechanism in hard carbon (HC) anodes is crucial for advancing sodium-ion battery (SIB) technology. However, the intrinsic complexity of HC microstructures and their interactions with sodium remain not fully…
We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configurations over a wide range of compositions. We compare two different approaches. Moment tensor potentials (MTP) are polynomial-like functions of…
Molecular dynamics simulations of 3C-SiC have been performed as a function of pressure and temperature. These simulations treat both electrons and atomic nuclei by quantum mechanical methods. While the electronic structure of the solid is…
Machine learning (ML) has become widely used in the development of interatomic potentials for molecular dynamics simulations. However, most ML potentials are still much slower than classical interatomic potentials and are usually trained…