3 papers
physics.comp-ph2020
Large scale and linear scaling DFT with the CONQUEST code
Ayako Nakata, Jack Baker, Shereif Mujahed +8
We survey the underlying theory behind the large-scale and linear scaling DFT code, Conquest, which shows excellent parallel scaling and can be applied to thousands of atoms with e…
cond-mat.mtrl-sci2019
Highly accurate local basis sets for large-scale DFT calculations in CONQUEST
David R. Bowler, Jack S. Baker, Jack T. L. Poulton +5
Given the widespread use of density functional theory (DFT), there is an increasing need for the ability to model large systems (beyond 1,000 atoms). We present a brief overview of…
physics.comp-ph2018
Machine learning forces trained by Gaussian process in liquid states: Transferability to temperature and pressure
Ryo Tamura, Jianbo Lin, Tsuyoshi Miyazaki
We study a generalization performance of the machine learning (ML) model to predict the atomic forces within the density functional theory (DFT). The targets are the Si and Ge sing…