{"id":227451,"date":"2025-12-19T01:41:24","date_gmt":"2025-12-19T07:41:24","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2025\/12\/advancing-physical-understanding-with-interpretable-machine-learning"},"modified":"2025-12-19T01:41:24","modified_gmt":"2025-12-19T07:41:24","slug":"advancing-physical-understanding-with-interpretable-machine-learning","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2025\/12\/advancing-physical-understanding-with-interpretable-machine-learning","title":{"rendered":"Advancing Physical Understanding with Interpretable Machine Learning"},"content":{"rendered":"<p style=\"padding-right: 20px\"><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/advancing-physical-understanding-with-interpretable-machine-learning2.jpg\"><\/a><\/p>\n<p>A new artificial neural-network architecture opens a window into the workings of a tool previously regarded as a black box.<\/p>\n<p>Thanks to the extremely large datasets and computing power that have become available in recent years, a new paradigm in scientific discovery has emerged. This new approach is purely data driven, using large amounts of data to train machine-learning models\u2015typically neural networks\u2015to predict the behavior of the natural world [1]. The most prominent achievement of this new methodology has arguably been the AlphaFold model for predicting protein folding (see Research News: Chemistry Nobel Awarded for an AI System That Predicts Protein Structures) [2]. But despite such successes, these data-driven approaches suffer a major drawback in that they are generally \u201cblack boxes\u201d that offer no human-accessible understanding of how they make their predictions. This shortcoming also extends to the models\u2019 inputs: It is often desirable to build known domain knowledge into these models, but the data-driven approach excludes that option.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>A new artificial neural-network architecture opens a window into the workings of a tool previously regarded as a black box. Thanks to the extremely large datasets and computing power that have become available in recent years, a new paradigm in scientific discovery has emerged. This new approach is purely data driven, using large amounts of [\u2026]<\/p>\n","protected":false},"author":427,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[19,6],"tags":[],"class_list":["post-227451","post","type-post","status-publish","format-standard","hentry","category-chemistry","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/227451","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=227451"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/227451\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=227451"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=227451"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=227451"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}