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Previously, sophisticated apparatus is required to measure indicator properties of performance, while machine learning approaches based on feature engineering procedures require a priori expertise that is challenged by the complicated environment of real\u2010world applications. Here, for a more effective real\u2010time prediction of battery life and failure, a novel end\u2010to\u2010end unsupervised machine learning approach is shown; this approach is free from feature engineering and uses only the raw images of the charge\u2013discharge voltage profiles. This model enables unsupervised real\u2010time automatic extraction of latent physical factors that control the performance of Na\u2010ion batteries to classify good or bad cycling performance by using only the voltage profile of the first cycle. This model can also monitor the safety of Li\u2010metal battery systems by giving warnings when the battery is approaching a failure. With the beyond expert\u2010level prediction ability, the abovementioned framework can be a promising prototype to further develop and enable high accuracy predictions of battery performance for real\u2010world applications in the future.<\/jats:p><\/jats:sec>","DOI":"10.1002\/aisy.201900102","type":"journal-article","created":{"date-parts":[[2019,10,6]],"date-time":"2019-10-06T04:58:05Z","timestamp":1570337885000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Beyond Expert\u2010Level Performance Prediction for Rechargeable Batteries by Unsupervised Machine Learning"],"prefix":"10.1002","volume":"1","author":[{"given":"Xi","family":"Chen","sequence":"first","affiliation":[{"name":"John. A. Paulson School of Engineering and Applied Sciences Harvard University  Cambridge MA 02138 USA"}]},{"given":"Luhan","family":"Ye","sequence":"additional","affiliation":[{"name":"John. A. 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