{"status":"public","year":"2021","author":[{"last_name":"Klus","full_name":"Klus, Stefan","first_name":"Stefan"},{"full_name":"Gelß, Patrick","last_name":"Gelß","first_name":"Patrick"},{"full_name":"Nüske, Feliks","id":"81513","last_name":"Nüske","orcid":"0000-0003-2444-7889","first_name":"Feliks"},{"last_name":"Noé","full_name":"Noé, Frank","first_name":"Frank"}],"title":"Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry","_id":"24170","publication_identifier":{"issn":["2632-2153"]},"department":[{"_id":"101"}],"user_id":"81513","citation":{"mla":"Klus, Stefan, et al. “Symmetric and Antisymmetric Kernels for Machine Learning Problems in Quantum Physics and Chemistry.” <i>Machine Learning: Science and Technology</i>, 045016, 2021, doi:<a href=\"https://doi.org/10.1088/2632-2153/ac14ad\">10.1088/2632-2153/ac14ad</a>.","bibtex":"@article{Klus_Gelß_Nüske_Noé_2021, title={Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry}, DOI={<a href=\"https://doi.org/10.1088/2632-2153/ac14ad\">10.1088/2632-2153/ac14ad</a>}, number={045016}, journal={Machine Learning: Science and Technology}, author={Klus, Stefan and Gelß, Patrick and Nüske, Feliks and Noé, Frank}, year={2021} }","ama":"Klus S, Gelß P, Nüske F, Noé F. Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry. <i>Machine Learning: Science and Technology</i>. Published online 2021. doi:<a href=\"https://doi.org/10.1088/2632-2153/ac14ad\">10.1088/2632-2153/ac14ad</a>","ieee":"S. Klus, P. Gelß, F. Nüske, and F. Noé, “Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry,” <i>Machine Learning: Science and Technology</i>, Art. no. 045016, 2021, doi: <a href=\"https://doi.org/10.1088/2632-2153/ac14ad\">10.1088/2632-2153/ac14ad</a>.","short":"S. Klus, P. Gelß, F. Nüske, F. Noé, Machine Learning: Science and Technology (2021).","apa":"Klus, S., Gelß, P., Nüske, F., &#38; Noé, F. (2021). Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry. <i>Machine Learning: Science and Technology</i>, Article 045016. <a href=\"https://doi.org/10.1088/2632-2153/ac14ad\">https://doi.org/10.1088/2632-2153/ac14ad</a>","chicago":"Klus, Stefan, Patrick Gelß, Feliks Nüske, and Frank Noé. “Symmetric and Antisymmetric Kernels for Machine Learning Problems in Quantum Physics and Chemistry.” <i>Machine Learning: Science and Technology</i>, 2021. <a href=\"https://doi.org/10.1088/2632-2153/ac14ad\">https://doi.org/10.1088/2632-2153/ac14ad</a>."},"date_created":"2021-09-12T08:52:57Z","language":[{"iso":"eng"}],"publication_status":"published","article_number":"045016","doi":"10.1088/2632-2153/ac14ad","type":"journal_article","date_updated":"2022-01-06T06:56:08Z","publication":"Machine Learning: Science and Technology"}