{"id":246186,"date":"2026-10-30T03:52:53","date_gmt":"2026-10-30T08:52:53","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2026\/10\/machine-learning-helps-track-semiconductor-defects-under-more-realistic-temperature-conditions"},"modified":"2026-10-30T03:52:53","modified_gmt":"2026-10-30T08:52:53","slug":"machine-learning-helps-track-semiconductor-defects-under-more-realistic-temperature-conditions","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2026\/10\/machine-learning-helps-track-semiconductor-defects-under-more-realistic-temperature-conditions","title":{"rendered":"Machine learning helps track semiconductor defects under more realistic temperature conditions"},"content":{"rendered":"\n<p>All materials contain inherent defects. These are not always harmful, since they can be used to engineer and introduce certain properties into a material. However, it is crucial to understand how they function and how they affect the material\u2019s properties.<\/p>\n<p>To understand and model defects in different materials, researchers\u2019 standard calculations are usually based on zero Kelvin, minus 273 degrees Celsius, the theoretically lowest possible temperature at which all atoms and molecules are completely stationary. But for materials intended for use in real-world products and everyday applications, it is essential to know how they behave at ordinary temperatures.<\/p>\n<p>Researchers at Chalmers University of Technology have now demonstrated a significantly more effective framework for calculating the properties of defects while accounting for different temperature effects, enabling results that are much closer to reality. The findings are presented in an article <a href=\"https:\/\/doi.org\/10.1038\/s41524-026-02369-3\" target=\"_blank\">published<\/a> in npj Computational Materials.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>All materials contain inherent defects. These are not always harmful, since they can be used to engineer and introduce certain properties into a material. However, it is crucial to understand how they function and how they affect the material\u2019s properties. To understand and model defects in different materials, researchers\u2019 standard calculations are usually based on [\u2026]<\/p>\n","protected":false},"author":427,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[48,6],"tags":[],"class_list":["post-246186","post","type-post","status-publish","format-standard","hentry","category-particle-physics","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/246186","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/427"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=246186"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/246186\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=246186"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=246186"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=246186"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}