[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127660-en":3,"doc-seo-127660-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127660,962084925636,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Rapid Estimation of the Intermolecular Electronic Couplings and Charge-Carrier Mobilities of Crystalline Molecular Organic Semiconductors through a Machine Learning Pipeline","Organic semiconductors enable broad (opto)electronic applications but materials discovery is constrained by trial-and-error synthesis and by computationally intensive modeling used to evaluate candidates via dense sets of descriptors. Density functional theory can derive quantities such as oxidation and reduction potentials, molecular relaxation and reorganization energies, and intermolecular electronic couplings, yet each workflow may require hours to days. A machine learning model predicts intermolecular electronic couplings within seconds instead of DFT timescales. Coupled with mathematical screening, it rapidly evaluates charge-carrier mobilities and anisotropies for over 60,000 molecular crystal structures, and the pipeline is released through the open-access OCELOT ML infrastructure.","Rapid Estimation of the Intermolecular Electronic Couplings and Charge-Carrier Mobilities of Crystalline Molecular Organic Semiconductors through a Machine Learning Pipeline  \nVinayak Bhat1, Baskar Ganapathysubramanian2 and Chad Risko1  \n1  \nDepartment of Chemistry &  \nCenter for Applied Energy Research  \nUniversity of Kentucky  \nLexington, Kentucky 40506, USA  \n2  \nDepartment of Mechanical Engineering &  \nTranslational AI center  \nIowa State University  \nAmes, Iowa, 50010, USA  \nAbstract  \nOrganic semiconductors offer tremendous potential across a wide range of (opto)electronic applications.  \nHowever, the development of these materials is limited by trial-and-error design approaches, as well as computationally heavy modeling approaches to evaluate/screen candidates using a suite of materials descriptors. For the latter, for instance, density functional theory (DFT) methods are widely used to derive descriptors such as the oxidation and reduction potentials, molecular relaxation and reorganization energies, and intermolecular electronic couplings; these calculations are compute-intensive, often requiring hours to days to determine. Such bottlenecks slow the pace and limit the exploration of the vast chemical space that can comprise organic materials. Here, we introduce a machine learning (ML) model to predict intermolecular electronic couplings in organic, molecule-based crystalline materials that take a few seconds, as compared to hours by DFT. Further, we use the ML model in conjunction with mathematical formulations to rapidly screen the charge-carrier mobilities and associated anisotropies of over 60,000 molecular crystal structures. The ML models and pipeline are made fully available on the open-access OCELOT ML infrastructure.  \nIntroduction  \nThe intermolecular electronic coupling in organic semiconductors is a critical parameter governing chargecarrier transport.1-4 The intermolecular electronic coupling depends critically both on the geometric overlap of neighboring molecules (and, hence, their intermolecular vibrational or phonon modes) and the molecular orbital (MO) overlap of these adjacent molecules – i.e., between the highest-occupied molecular orbitals (HOMO) for hole transport and the lowest-unoccupied molecular orbitals (LUMO) for electron transport.3, 5 Based on the MO overlap symmetry, the phase of the intermolecular electronic coupling is determined.  \nA variety of theoretical models can be used to estimate intermolecular electronic coupling.4, 6-10 In the energy-splitting-in dimer method,7, 11 the intermolecular electronic coupling is estimated to be half the energy difference between the HOMO and HOMO-1 of a (noncovalent) dimer formed by two adjacent molecules. While this method is effective for symmetrically arranged molecules in a dimer, the method fails for systems where molecular asymmetry leads to polarization in the dimer. This shortcoming is overcome in the fragment orbital approach (FMO), wherein an orthonormal basis is used to preserve the local character of the monomer orbitals.12-14 In the FMO method, the effective intermolecular electronic coupling (V12) between the adjacent molecules (i.e., 1 and 2) in a dimer is given by  \n􀜸12 = 􀜪12~~ ~~−~~ ~~0.5~~ ~~1×12􀜵12~~ ~~(􀜪1~~ ~~+~~ ~~􀜪2) (1)  \nwhere H12 is the interaction energy or electronic coupling matrix, S12 is the overlap integral, H1, and H2 are the site energies of the monomers. Intermolecular electronic couplings from ab initio or density functional  \ntheory (DFT) calculations are highly accurate but time-consuming compared to less accurate and fast semiempirical calculations.  \nWith estimates of the intermolecular electronic couplings, charge-carrier transport in organic semiconductors can be evaluated by using these descriptors in combination with kinetic Monte Carlo methods,15-17 molecular dynamics simulations,18, 19 or transient localization theory.20, 21 However, each of these methods requires a large number of intermolecular electron","cbCaim3ehSYnPTFu","https://ap.wps.com/l/cbCaim3ehSYnPTFu","pdf",1353053,2,1,17,"English","en",105,"# Abstract\n# Introduction\n## Intermolecular electronic coupling as a transport parameter\n## Theories and computational cost trade-offs\n## Machine learning approaches and motivations","[{\"question\":\"Why is intermolecular electronic coupling important for charge transport in organic semiconductors?\",\"answer\":\"Intermolecular electronic coupling governs charge-carrier transport and depends on geometric overlap of neighboring molecules and overlap between molecular orbitals such as HOMO and LUMO.\"},{\"question\":\"What are the limitations of DFT-based evaluation of intermolecular electronic couplings?\",\"answer\":\"DFT-derived couplings are accurate but time-consuming, typically requiring hours to days to compute, which slows candidate screening across large chemical spaces.\"},{\"question\":\"How does the proposed machine learning pipeline improve efficiency and throughput?\",\"answer\":\"The ML model predicts intermolecular electronic couplings within seconds and, when combined with mathematical formulations, enables rapid screening of charge-carrier mobilities and anisotropies for more than 60,000 molecular crystal structures.\"}]","Rapid Estimation of the Intermolecular Electronic Couplings and Charge-Carrier Mobilities of Crystalline Molecular Organic Semiconductors through a Machine Learning Pipeline | 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is intermolecular electronic coupling important for charge transport in organic semiconductors?","Question",{"text":76,"@type":77},"Intermolecular electronic coupling governs charge-carrier transport and depends on geometric overlap of neighboring molecules and overlap between molecular orbitals such as HOMO and LUMO.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What are the limitations of DFT-based evaluation of intermolecular electronic couplings?",{"text":81,"@type":77},"DFT-derived couplings are accurate but time-consuming, typically requiring hours to days to compute, which slows candidate screening across large chemical spaces.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the proposed machine learning pipeline improve efficiency and throughput?",{"text":85,"@type":77},"The ML model predicts intermolecular electronic couplings within seconds and, when combined with mathematical formulations, enables rapid screening of charge-carrier mobilities and anisotropies 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