Open AccessA Combined Machine Learning and High-Energy X-ray Diffraction Approach to Understanding Liquid and Amorphous Metal Oxides
Author(s) -
Ganesh Sivaraman,
Gábor Cśanyi,
Álvaro Vázquez-Mayagoitia,
Ian Foster,
Stephen K. Wilke,
J. K. R. Weber,
C. J. Benmore
Publication year - 2022
Publication title -
journal of the physical society of japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 139
eISSN - 1347-4073
pISSN - 0031-9015
DOI - 10.7566/jpsj.91.091009
Subject(s) - amorphous solid , density functional theory , materials science , gaussian , polyhedron , coordination number , topology (electrical circuits) , diffraction , amorphous metal , range (aeronautics) , statistical physics , chemical physics , computational chemistry , optics , crystallography , physics , ion , mathematics , geometry , quantum mechanics , chemistry , composite material , combinatorics