{"id":1119,"date":"2023-06-15T09:19:43","date_gmt":"2023-06-15T07:19:43","guid":{"rendered":"https:\/\/compbat.eu\/?p=1119"},"modified":"2023-06-15T09:19:43","modified_gmt":"2023-06-15T07:19:43","slug":"last-publications","status":"publish","type":"post","link":"https:\/\/compbat.eu\/2023\/06\/15\/last-publications\/","title":{"rendered":"Last publications from Aalto University and the\u00a0University of Turku teams"},"content":{"rendered":"<p>Here you can find one of our last publications, from Aalto University and the University of Turku teams, entitled: &#8220;<em><strong>Density Functional Theory and Machine Learning for Electrochemical Square-Scheme Prediction: An Application to Quinone-type Molecules Relevant to Redox Flow Batteries<\/strong><\/em>&#8221;<\/p>\n<p>In CompBat we are interested of molecules redox potentials (Ered) but depending the pH or the charge state of the molecule the molecules protonation state can change.<\/p>\n<p>We have developed a machine learning (ML) model to predict Ered and acidity constants (pKa) of a rather large set of molecules. Quantum chemical DFT methods has been used to compute Ered and pKa values of more than 8200 quinone-type organic molecules.<\/p>\n<p>Supervised om Forest Regression model is used to teach the ML method. All studied molecules underwent two-proton and two-electron transfer process. Both structural and chemical descriptors are used.<\/p>\n<p>The HOMO of the reactant and LUMO of the product participating in the oxidation reaction appeared to be inversely associated with Ered. Trained models using a SMILES-based descriptor can efficiently predict the pKa and Ered with a mean absolute error of less than 1 and 66 mV, respectively.<\/p>\n<p>High prediction accuracy of R2 &gt; 0.76 and &gt; 0.90 was also obtained on the external test set for Ered and pKa, respectively. This hybrid DFT-ML study can be applied to speed up the screening of quinone-type molecules for energy storage and other applications.<\/p>\n<p>Here you can find the full text, available in ChemRxiv (loaded 22.5.2023):<br \/>\n<a href=\"https:\/\/doi.org\/10.26434\/chemrxiv-2023-wfv75\">https:\/\/doi.org\/10.26434\/chemrxiv-2023-wfv75<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Here you can find one of our last publications, from Aalto University and the University of Turku teams, entitled: &#8220;Density Functional Theory and Machine Learning for Electrochemical Square-Scheme Prediction: An Application to Quinone-type Molecules Relevant to Redox Flow Batteries&#8221; In CompBat we are interested of molecules redox potentials (Ered) but depending the pH or the [&hellip;]<\/p>\n","protected":false},"author":6,"featured_media":971,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"off","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[7],"tags":[],"_links":{"self":[{"href":"https:\/\/compbat.eu\/wp-json\/wp\/v2\/posts\/1119"}],"collection":[{"href":"https:\/\/compbat.eu\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/compbat.eu\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/compbat.eu\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/compbat.eu\/wp-json\/wp\/v2\/comments?post=1119"}],"version-history":[{"count":4,"href":"https:\/\/compbat.eu\/wp-json\/wp\/v2\/posts\/1119\/revisions"}],"predecessor-version":[{"id":1123,"href":"https:\/\/compbat.eu\/wp-json\/wp\/v2\/posts\/1119\/revisions\/1123"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/compbat.eu\/wp-json\/wp\/v2\/media\/971"}],"wp:attachment":[{"href":"https:\/\/compbat.eu\/wp-json\/wp\/v2\/media?parent=1119"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/compbat.eu\/wp-json\/wp\/v2\/categories?post=1119"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/compbat.eu\/wp-json\/wp\/v2\/tags?post=1119"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}