A Combined Machine Learning and High-Energy X-ray Diffraction Approach to Understanding Liquid and Amorphous Metal Oxides | Zendy

Ganesh Sivaraman | Zendy; Gábor Cśanyi | Zendy; Álvaro VázquezMayagoitia | Zendy; Ian Foster | Zendy; Stephen K. Wilke | Zendy; Richárd Wéber | Zendy; Chris J. Benmore | Zendy

Open Access

A Combined Machine Learning and High-Energy X-ray Diffraction Approach to Understanding Liquid and Amorphous Metal Oxides

Author(s) -

Ganesh Sivaraman,

Gábor Cśanyi,

Álvaro VázquezMayagoitia,

Ian Foster,

Stephen K. Wilke,

Richárd Wéber,

Chris 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

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