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The\u00a0atom-based uncertainty method provides an extra layer of chemical insight to\u00a0the estimated uncertainties, i.e.,\u00a0one can analyze individual atomic uncertainty values to diagnose the chemical component that introduces uncertainty to the prediction.\u00a0Our experiments suggest that\u00a0atomic uncertainty can detect unseen chemical structures and identify chemical species whose data are potentially associated with significant noise.\u00a0Furthermore, we propose a\u00a0post-hoc\u00a0calibration method to refine the uncertainty\u00a0quantified by ensemble models\u00a0for better confidence interval estimates.\u00a0This work improves uncertainty calibration and provides a framework for assessing whether and why a prediction should be considered unreliable.<\/jats:p>\n                  <jats:p>\n                    <jats:bold>Graphical Abstract<\/jats:bold>\n                  <\/jats:p>","DOI":"10.1186\/s13321-023-00682-3","type":"journal-article","created":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T11:04:12Z","timestamp":1675422252000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Explainable uncertainty quantifications for deep learning-based molecular property prediction"],"prefix":"10.1186","volume":"15","author":[{"given":"Chu-I","family":"Yang","sequence":"first","affiliation":[]},{"given":"Yi-Pei","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,3]]},"reference":[{"key":"682_CR1","doi-asserted-by":"publisher","first-page":"3370","DOI":"10.1021\/acs.jcim.9b00237","volume":"59","author":"K Yang","year":"2019","unstructured":"Yang K, Swanson K, Jin W et al (2019) Analyzing learned molecular representations for property prediction. 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