Uncertainty-aware molecular dynamics from Bayesian active learning for phase transformations and thermal transport in SiC
Abstract Machine learning interatomic force fields are promising for combining high
computational efficiency and accuracy in modeling quantum interactions and simulating …
computational efficiency and accuracy in modeling quantum interactions and simulating …
Phonon background from gamma rays in sub-GeV dark matter detectors
KV Berghaus, R Essig, Y Hochberg, Y Shoji… - Physical Review D, 2022 - APS
High-energy photons with O (MeV) energies from radioactive contaminants can scatter in a
solid-state target material and constitute an important low-energy background for sub-GeV …
solid-state target material and constitute an important low-energy background for sub-GeV …
[HTML][HTML] First-Principles Investigation of Charge Transfer Mechanism of B-Doped 3C-SiC Semiconductor Material
AA Dauda, MY Onimisi, AJ Owolabi, HA Lawal… - World Journal of …, 2024 - scirp.org
This study delves into the charge transfer mechanism of boron (B)-doped 3C-SiC through
first-principles investigations. We explore the effects of B doping on the electronic properties …
first-principles investigations. We explore the effects of B doping on the electronic properties …
Machine Learning Bayesian Force Fields and Applications to Phase Transformations
Y Xie - 2023 - search.proquest.com
This thesis develops machine learning Bayesian force fields for ecient and accurate
molecular dynamics simulations of materials. The Gaussian process regression model …
molecular dynamics simulations of materials. The Gaussian process regression model …