{"month":"07","year":"2021","publication_identifier":{"eissn":["1520-6890"],"issn":["0009-2665"]},"title":"Combining machine learning and computational chemistry for predictive insights into chemical systems","quality_controlled":"1","main_file_link":[{"url":"https://doi.org/10.1021/acs.chemrev.1c00107","open_access":"1"}],"intvolume":"       121","citation":{"ama":"Keith JA, Valentin Vassilev-Galindo V, Cheng B, et al. Combining machine learning and computational chemistry for predictive insights into chemical systems. <i>Chemical Reviews</i>. 2021;121(16):9816-9872. doi:<a href=\"https://doi.org/10.1021/acs.chemrev.1c00107\">10.1021/acs.chemrev.1c00107</a>","ieee":"J. A. Keith <i>et al.</i>, “Combining machine learning and computational chemistry for predictive insights into chemical systems,” <i>Chemical Reviews</i>, vol. 121, no. 16. American Chemical Society, pp. 9816–9872, 2021.","mla":"Keith, John A., et al. “Combining Machine Learning and Computational Chemistry for Predictive Insights into Chemical Systems.” <i>Chemical Reviews</i>, vol. 121, no. 16, American Chemical Society, 2021, pp. 9816–72, doi:<a href=\"https://doi.org/10.1021/acs.chemrev.1c00107\">10.1021/acs.chemrev.1c00107</a>.","short":"J.A. Keith, V. Valentin Vassilev-Galindo, B. Cheng, S. Chmiela, M. Gastegger, K.-R. Müller, A. Tkatchenko, Chemical Reviews 121 (2021) 9816–9872.","ista":"Keith JA, Valentin Vassilev-Galindo V, Cheng B, Chmiela S, Gastegger M, Müller K-R, Tkatchenko A. 2021. Combining machine learning and computational chemistry for predictive insights into chemical systems. Chemical Reviews. 121(16), 9816–9872.","apa":"Keith, J. A., Valentin Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., &#38; Tkatchenko, A. (2021). Combining machine learning and computational chemistry for predictive insights into chemical systems. <i>Chemical Reviews</i>. American Chemical Society. <a href=\"https://doi.org/10.1021/acs.chemrev.1c00107\">https://doi.org/10.1021/acs.chemrev.1c00107</a>","chicago":"Keith, John A., Valentin Valentin Vassilev-Galindo, Bingqing Cheng, Stefan Chmiela, Michael Gastegger, Klaus-Robert Müller, and Alexandre Tkatchenko. “Combining Machine Learning and Computational Chemistry for Predictive Insights into Chemical Systems.” <i>Chemical Reviews</i>. American Chemical Society, 2021. <a href=\"https://doi.org/10.1021/acs.chemrev.1c00107\">https://doi.org/10.1021/acs.chemrev.1c00107</a>."},"extern":"1","date_published":"2021-07-07T00:00:00Z","doi":"10.1021/acs.chemrev.1c00107","article_type":"review","page":"9816-9872","type":"journal_article","issue":"16","external_id":{"arxiv":["2102.06321"]},"status":"public","author":[{"first_name":"John A.","last_name":"Keith","full_name":"Keith, John A."},{"full_name":"Valentin Vassilev-Galindo, Valentin","first_name":"Valentin","last_name":"Valentin Vassilev-Galindo"},{"orcid":"0000-0002-3584-9632","first_name":"Bingqing","last_name":"Cheng","full_name":"Cheng, Bingqing","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9"},{"full_name":"Chmiela, Stefan","first_name":"Stefan","last_name":"Chmiela"},{"full_name":"Gastegger, Michael","last_name":"Gastegger","first_name":"Michael"},{"full_name":"Müller, Klaus-Robert","first_name":"Klaus-Robert","last_name":"Müller"},{"last_name":"Tkatchenko","first_name":"Alexandre","full_name":"Tkatchenko, Alexandre"}],"oa_version":"Published Version","date_updated":"2023-05-08T11:31:03Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"No","day":"07","publisher":"American Chemical Society","scopus_import":"1","_id":"9698","oa":1,"publication_status":"published","language":[{"iso":"eng"}],"publication":"Chemical Reviews","date_created":"2021-07-20T11:18:37Z","abstract":[{"text":"Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We then follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.","lang":"eng"}],"volume":121}