{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T15:43:15Z","timestamp":1777995795590,"version":"3.51.4"},"reference-count":46,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T00:00:00Z","timestamp":1708041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Perovskite materials have attracted much attention in recent years due to their high performance, especially in the field of photovoltaics. However, the dark side of these materials is their poor stability, which poses a huge challenge to their practical applications. Double perovskite compounds, on the other hand, can show more stability as a result of their specific structure. One of the key properties of both perovskite and double perovskite is their tunable band gap, which can be determined using different techniques. Density functional theory (DFT), for instance, offers the potential to intelligently direct experimental investigation activities and predict various properties, including band gap. In reality, however, it is still difficult to anticipate the energy band gap from first principles, and accurate results often require more expensive methods such as hybrid functional or GW methods. In this paper, we present our development of high-throughput supervised ensemble learning-based methods: random forest, XGBoost, and Light GBM using a database of 1306 double perovskites materials to predict the energy band gap. Based on elemental properties, characteristics have been vectorized from chemical compositions. Our findings demonstrate the efficiency of ensemble learning methods and imply that scientists would benefit from recently employed methods in materials informatics.<\/jats:p>","DOI":"10.3390\/make6010022","type":"journal-article","created":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T08:02:17Z","timestamp":1708070537000},"page":"435-447","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["High-Throughput Ensemble-Learning-Driven Band Gap Prediction of Double Perovskites Solar Cells Absorber"],"prefix":"10.3390","volume":"6","author":[{"given":"Sabrina","family":"Djeradi","sequence":"first","affiliation":[{"name":"Laboratoire de Physique des Mat\u00e9riaux, Universit\u00e9 Amar Telidji de Laghouat, BP 37G, Laghouat 03000, Algeria"}]},{"given":"Tahar","family":"Dahame","sequence":"additional","affiliation":[{"name":"Laboratoire de Physique des Mat\u00e9riaux, Universit\u00e9 Amar Telidji de Laghouat, BP 37G, Laghouat 03000, Algeria"}]},{"given":"Mohamed Abdelilah","family":"Fadla","sequence":"additional","affiliation":[{"name":"School of Mathematics and Physics, Queen\u2019s University Belfast, Belfast BT7 1NN, UK"}]},{"given":"Bachir","family":"Bentria","sequence":"additional","affiliation":[{"name":"Laboratoire de Physique des Mat\u00e9riaux, Universit\u00e9 Amar Telidji de Laghouat, BP 37G, Laghouat 03000, Algeria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2334-7889","authenticated-orcid":false,"given":"Mohammed Benali","family":"Kanoun","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Sciences, College of Humanities and Sciences, Prince Sultan University, P.O. Box 66833, Riyadh 11586, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9333-7862","authenticated-orcid":false,"given":"Souraya","family":"Goumri-Said","sequence":"additional","affiliation":[{"name":"Department of Physics, College of Science and General studies, Alfaisal University, P.O. Box 5092, Riyadh 11533, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1038\/s41524-021-00495-8","article-title":"Machine learning for perovskite materials design and discovery","volume":"7","author":"Tao","year":"2021","journal-title":"npj Comput. 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