{"id":95129,"date":"2024-05-30T08:26:19","date_gmt":"2024-05-30T06:26:19","guid":{"rendered":"https:\/\/www.immo-invest.ch\/forschende-nutzen-ki-fuer-entwicklung-neuer-solarzellen\/"},"modified":"2024-05-30T09:27:12","modified_gmt":"2024-05-30T07:27:12","slug":"forschende-nutzen-ki-fuer-entwicklung-neuer-solarzellen","status":"publish","type":"post","link":"https:\/\/www.immo-invest.ch\/en\/forschende-nutzen-ki-fuer-entwicklung-neuer-solarzellen\/","title":{"rendered":"Researchers use AI to develop new solar cells"},"content":{"rendered":"

As part of a research project at the Swiss Federal Institute of Technology in Lausanne(EPFL<\/a>), a method has been developed to search large databases for potential materials for the utilisation of new solar cells. According to a press release<\/a>, several promising halide perovskites were identified through the use of machine learning (ML). ML is a branch of artificial intelligence in which computers learn from data sets or databases made available to them.<\/p>\n

Perovskites represent a promising new group of materials for future photovoltaic applications due to their simple manufacturing processes combined with low costs. It is important that the new materials have a suitable band gap so that they can utilise solar energy optimally. The band gap has the property of absorbing photons with a certain energy and then converting them into electricity.<\/p>\n

The EPFL team led by Haiyuan Wang and Alfredo Pasquarello developed a machine learning model that was able to identify 14 completely new perovskites from 15,000 materials. These are excellent candidates for future high-efficiency solar cells. The researchers were thus able to show that the use of ML can significantly accelerate the discovery and validation of new photovoltaic materials. <\/p>\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Researchers at EPFL have used artificial intelligence (AI) to identify new materials for use in solar cells. They found 14 promising materials.<\/p>\n","protected":false},"author":2,"featured_media":61029,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"pmpro_default_level":"0","inline_featured_image":false,"_monsterinsights_skip_tracking":false,"footnotes":""},"categories":[12482,12476,22698,46426],"tags":[47032,42823,19198,47033,12564,12526,13861,12752],"class_list":["post-95129","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-energy","category-immonews","category-sustainability","category-waadt-en","tag-datenbanken-en","tag-datensaetze-en","tag-forschungsprojekt-en","tag-herstellungsverfahren-2-en","tag-immobilien-en","tag-immonews-en","tag-kuenstliche-intelligenz-en","tag-strom-en","pmpro-has-access"],"acf":{"bild_mit_ki_erstellt_worden":[],"lead":"Researchers at EPFL have used artificial intelligence (AI) to identify new materials for use in solar cells. They found 14 promising materials.","autor":"ce\/eb","ort":"Lausanne ","bildunterschrift":"","bild_im_grid":false,"gallerie":"","newsim":""},"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t