Predicting the bandgap of ABX 2 materials: supervised machine learning on small dataset
Source abstract
Abstract
Predicting the electronic bandgap in ABX 2 -type materials (where A = {Al, Ga, Zn, Cd}, B = {Ga, In, Ge, Sn, Si}, and X = {N, P, As, Sb}) is essential for photovoltaic applications.
Only a short abstract or summary was supplied by the source.
Source: crossref · Quality: short
Research context
OpenAlex topics: Machine Learning in Materials Science · Electron and X-Ray Spectroscopy Techniques · Ga2O3 and related materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
State Key Laboratory of Critical Mineral Research and Exploration, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550081, China
Department of Electronics and Communication Engineering, School of Engineering, SR University, Warangal-506371, Telangana, India
Funding
National Key Research and Development Program of China
References (46)
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