Work

Predicting Optical Bandgaps in C60 and Functionalized Derivatives from Limited Data for Renewable Energy Applications

Year: 2025

Type: conference-paper

Source: IEEE International Conference on Compatibility, Power Electronics and Power Engineering

DOI: 10.1109/cpe-powereng63314.2025.11027245

Authors: Mehmet Tuncel, Hasan Kurban, Erchin Serpedin, Mustafa Kurban

Institutions: Texas A&M University at Qatar, Hamad bin Khalifa University, Texas A&M University, Ankara University

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: closed

OpenAlex ID: W4411337561

FWCI:

Cites: 17

Cited by:

Related to: 10