341 citations · 346 across the 4 of their papers we have counts for
4 papers
AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions
Stephen G. Dale, Nikita Kazeev, Alastair J. A. Price +65
Artificial intelligence and machine learning are reshaping how we approach scientific discovery, not by replacing established methods but by extending what researchers can probe, p…
Global structure searches under varying temperatures and pressures using polynomial machine learning potentials: A case study on silicon
Hayato Wakai, Atsuto Seko, Isao Tanaka
Polynomial machine learning potentials (MLPs) based on polynomial rotational invariants have been systematically developed for various systems and applied to efficiently predict cr…
Predictive power of polynomial machine learning potentials for liquid states in 22 elemental systems
Hayato Wakai, Atsuto Seko, Hirosato Izuta +2
The polynomial machine learning potentials (MLPs) described by polynomial rotational invariants have been systematically developed for various systems and used in diverse applicati…
Prediction model of band-gap for AX binary compounds by combination of density functional theory calculations and machine learning techniques
Joohwi Lee, Atsuto Seko, Kazuki Shitara +1
Machine learning techniques are applied to make prediction models of the G0W0 band-gaps for 156 AX binary compounds using Kohn-Sham band-gaps and other fundamental information of c…