[et_pb_section fb_built=”1″ _builder_version=”4.0.5″][et_pb_row _builder_version=”4.0.5″][et_pb_column type=”4_4″ _builder_version=”4.0.5″][et_pb_text _builder_version=”4.0.5″ hover_enabled=”0″]<\/p>\n
We will perform quantum mechanics based molecular dynamics simulations (AIMD)<\/strong> for studying the electron transfer from electrode to molecules that can be used in the redox flow battery (RFB)<\/strong>.<\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/compbat.eu\/wp-content\/uploads\/2021\/10\/Copia-di-Generale.jpg” _builder_version=”4.0.5″ hover_enabled=”0″][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.0.5″][et_pb_column type=”4_4″ _builder_version=”4.0.5″][et_pb_text _builder_version=”4.0.5″ hover_enabled=”0″]<\/p>\n To be able to model the charge transfer we use the Constrained Density Functional Theory (cDFT)<\/strong>. With cDFT we can force an electron to be either on the electrode (in our case a graphene sheet) or on the molecule. We need to run two parallel AIMD simulations with different charge localization and from these simulations we can compute parameters used in Marcus theory.<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.0.5″][et_pb_column type=”1_2″ _builder_version=”4.0.5″][et_pb_image src=”https:\/\/compbat.eu\/wp-content\/uploads\/2021\/10\/Copia-di-Zoom-1.jpg” _builder_version=”4.0.5″ hover_enabled=”0″][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.0.5″][et_pb_image src=”https:\/\/compbat.eu\/wp-content\/uploads\/2021\/10\/Copia-di-Zoom-2.jpg” _builder_version=”4.0.5″ hover_enabled=”0″][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.0.5″][et_pb_column type=”4_4″ _builder_version=”4.0.5″][et_pb_text _builder_version=”4.0.5″ hover_enabled=”0″]<\/p>\n The Marcus theory will predict the electron transfer rate.<\/strong> This approach does not have any parameters that should be fitted to experiments so it will provide unbiased predictions of electrons transfer rates for particular molecules. The electrode to molecule charge transfer rate is an important property for RFB. If it is very slow the whole RFB can be inefficient. Unfortunately, this quantity is difficult to measure and difficult to simulate.<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.0.5″][et_pb_column type=”1_2″ _builder_version=”4.0.5″][et_pb_image src=”https:\/\/compbat.eu\/wp-content\/uploads\/2021\/10\/Copia-di-Zoom-3.jpg” _builder_version=”4.0.5″ hover_enabled=”0″][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.0.5″][et_pb_text _builder_version=”4.0.5″ hover_enabled=”0″]<\/p>\n Our cDFT simulations are among the first this type of simulations in the world, and they will open a new way to study the electrode-molecule charge transfer. The first AIMD simulations have been quite time consuming but we are exploring more approximate but much faster ways to get the charge transfer rates.<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" We will perform quantum mechanics based molecular dynamics simulations (AIMD) for studying the electron transfer from electrode to molecules that can be used in the redox flow battery (RFB). To be able to model the charge transfer we use the Constrained Density Functional Theory (cDFT). With cDFT we can force an electron to be either […]<\/p>\n","protected":false},"author":6,"featured_media":664,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":" The TTK research team is involved in developing high-throughput screening (HTS) methods that enables the identification of promising candidates of water-soluble redox-active compounds for experimental synthesis and electrochemical characterization. The focus is on bioinspired molecules derived from vitamins and amino acids, which are promising candidate compounds for novel redox flow batteries (RFBs). Our general strategy along these lines is to build an initial database for two basic quantities relevant to RFBs (redox potentials and aqueous solubilities), and utilize various machine learning techniques to provide efficient screening methodology applicable for a large and diverse set of molecules.<\/span><\/p> \u00a0<\/p> Molecular design<\/b> via systematic variation of functional groups is the initial step in our approach. The molecular database is built in a combinatorial manner: We define various molecular frameworks and introduce substituents with broadly varying electronic and steric properties. In our developed protocol, we first generate the 3D structures of the molecules, which is followed by an extensive conformational analysis to find the energetically most favored molecular structures.<\/span><\/p> \u00a0<\/p> The redox potentials and aqueous solubilities are computed via an efficient composite protocol that employs a combination of various modelling tools ranging from simple force-field methods to advanced quantum chemical calculations, but also including semiempirical computational methods. Accurate <\/span>quantum chemical calculations<\/b> represent the computationally most demanding step of our protocol. These calculations are carried out using density functional theory (DFT) and we use high-performance computer facilities to obtain accurate electronic structure data.<\/span><\/p> \u00a0<\/p> The developed molecular database is used to train and validate various <\/span>machine learning<\/b> approaches in the next phase of the project. The molecular library is then expanded iteratively in terms of the number of molecules, as well as the diversity of molecular frameworks. This procedure will thus result in a <\/span>HTS tool<\/b> that is applicable to a large set of redox-active compounds and will assist the discovery of new prospective candidates for next generation flow batteries.<\/span><\/p> \u00a0<\/p>","_et_gb_content_width":"","footnotes":""},"categories":[7],"tags":[],"_links":{"self":[{"href":"https:\/\/compbat.eu\/wp-json\/wp\/v2\/posts\/662"}],"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=662"}],"version-history":[{"count":3,"href":"https:\/\/compbat.eu\/wp-json\/wp\/v2\/posts\/662\/revisions"}],"predecessor-version":[{"id":671,"href":"https:\/\/compbat.eu\/wp-json\/wp\/v2\/posts\/662\/revisions\/671"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/compbat.eu\/wp-json\/wp\/v2\/media\/664"}],"wp:attachment":[{"href":"https:\/\/compbat.eu\/wp-json\/wp\/v2\/media?parent=662"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/compbat.eu\/wp-json\/wp\/v2\/categories?post=662"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/compbat.eu\/wp-json\/wp\/v2\/tags?post=662"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}