124 citations · 173 across the 4 of their papers we have counts for
4 papers
Unsupervised learning-based structural analysis: Search for a characteristic low-dimensional space by local structures in atomistic simulations
Ryo Tamura, Momo Matsuda, Jianbo Lin +3
Owing to the advances in computational techniques and the increase in computational power, atomistic simulations of materials can simulate large systems with higher accuracy. Compl…
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…
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…
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…