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Here, we introduce a machine learning approach for the (semi)automatic construction of reactive coordinates. After generating a meaningful data set from trajectory calculations, we train an autoencoder to find a low-dimensional set of non-linear coordinates for use in molecular quantum dynamics. We compare the wave packet dynamics of proton transfer reactions in both linear and non-linear coordinate spaces and find significant improvement for physical properties like reaction timescales.<\/jats:p>","DOI":"10.1007\/978-3-030-30493-5_73","type":"book-chapter","created":{"date-parts":[[2019,9,10]],"date-time":"2019-09-10T20:03:41Z","timestamp":1568145821000},"page":"783-787","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Using an Autoencoder for Dimensionality Reduction in Quantum Dynamics"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7381-4222","authenticated-orcid":false,"given":"Sebastian","family":"Reiter","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4538-811X","authenticated-orcid":false,"given":"Thomas","family":"Schnappinger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7877-5979","authenticated-orcid":false,"given":"Regina","family":"de Vivie-Riedle","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,9,9]]},"reference":[{"key":"73_CR1","doi-asserted-by":"publisher","first-page":"2696","DOI":"10.1103\/PhysRevLett.68.2696","volume":"68","author":"AE Garc\u00eda","year":"1992","unstructured":"Garc\u00eda, A.E.: Large-amplitude nonlinear motions in proteins. 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