Work

Accelerating Materials Discovery of Novel Europium(II)-Activated Phosphors through Machine Learning Classification of Europium Valences

Year: 2024

Type: article

Abstract: An approach is presented to accelerate the discovery of host compounds for novel Eu 2+ -activated phosphor materials by integrating systematic data collection, machine learning, and experimental validation. A data set of Eu 2+ - and Eu 3+ -activated phosphors has been constructed using systematic data collection methodology from numerous academic articles. A machine-learning classification model has been developed using the collected data set to predict the oxidation states of Eu ions in potential hosts regarding luminescence. The model considers the nonexclusive nature of the divalent and trivalent oxidation states of Eu ions in phosphor applications. A comprehensive exploration of a materials database was conducted to identify host candidates for novel Eu 2+ -activated phosphor materials, leading to attempts to synthesize them. Photoluminescence analysis revealed the successful synthesis of 12 new Eu 2+ -activated phosphors, demonstrating the potential of the proposed approach for accelerating material discovery.

Source: Chemistry of Materials

DOI: 10.1021/acs.chemmater.4c01981

Authors: Yukinori Koyama, Yukako Kohriki, Masamichi Harada, Naoto Hirosaki, Takashi Takeda

Institution: National Institute for Materials Science

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: green

Funder: Core Research for Evolutional Science and Technology

Award: G459494991