[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127924-en":3,"doc-seo-127924-105":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},127924,687207024478,"Liam","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Electronic Structure and Machine Learning Protocols for Pre-screening of Near-room Temperature Spin-crossover Materials","Spin-crossover (SCO) is a reversible high-spin to low-spin transition in certain transition-metal complexes, driven by external stimuli such as temperature, pressure, or light. This work focuses on Fe(II) SCO systems, where ligand field strength and metal–ligand interactions control spin-state energetics and transition temperatures. The thesis benchmarks DFT functionals for accurate predictions, analyzes how ligand substituents tune T1/2 through electronic and steric effects, and extends modeling to dinuclear and polynuclear cooperative behavior. Machine learning methods (KRR and Gaussian Processes) trained on DFT data enable scalable prediction and descriptor-based insight for materials design.","UNIVERSITATDEBARCELONA  \n# Electronic Structure and machine learning protocolsfor pre-screening of near-room temperaturespin-crossover materials\n\nDaniel Vidal Ramon  \n\n| NC  \u003Cbr>Aquesta tesi doctoral està subjecta a lallicencia Reconeixement-NoComercial 4.0.Espanva de  \u003Cbr>Creative Commons.  \u003Cbr>Esta tesis doctoral está sujeta a la licencia Reconocimiento -NoComercial 4.0.Espana de  \u003Cbr>Creative Commons.  \u003Cbr>This doctoral thesis is licensed under the Creative Commons Attribution-NonCommercial 4.0.  \u003Cbr>Spain License.   |\n| --- |\n\n\n|  |\n|\n\n# Electronic Structure and Machine Learningprotocols for pre-screening of near-roomtemperature spin-crossover materials\n\nDaniel Vidal Ramon  \nUNIVERSITATDEBARCELONA  \nMemoria presentada perDaniel Vidal Ramon  \nPer optar al grau de Doctor per laUniversitat de Barcelona  \nPrograma de doctorat en Química Teorica iModelització Computacional  \n# Electronic Structure and machine learning protocols forpre-screening of near-room temperaturespin-crossover materials\n\nDirigida per:  \nDr.Jordi Ribas ArinoDr.Jordi Cirera Fernández  \nUniversitat de BarcelonaUniversitat de Barcelona  \nTutor:  \nDr.Jordi Ribas ArinoUniversitat de Barcelona  \nOMNIA LVCE  \nUNIVERSITATDEBARCELONA  \nBarcelona,2024  \nThe work presented in this doctoral thesis has been carried out at the MaterialsScience and Physical Chemistry Department of the University of Barcelona (UB),withinthe research facilities of the Institute Theoretical and Computational Chemistry (IQTCUB).  \nDaniel Vidal Ramon acknowledges IQTCUB and the Spanish Ministerio de Cienciae Innovación Competitividad for funding three-year predoctoral contract through the \"Mariade Maeztu\"grant number MDM-2017-0767.  \nAbstract  \nSpin-crossover(SCO)is a phenomenon observed in certain transition metal complexes,particularly d⁴-d⁷metals,where a reversible transition between high-spin (HS)and low-spin(LS)electronic states occurs in response to external stimuli like temperature,pressure,or light.This switch alters magnetic,optical,and structural properties,making SCO materials attractivefor applications in molecular electronics,data storage,sensors,and smart devices.Amongtransition metal complexes,FeI SCO complexes are widely studied because of their distinctelectronic configurations and stability.The transition between HS and LS states is governed bythe metal-ligand interaction,specifically the ligand field strength.A stronger ligand fieldstabilizes the LS state,while a weaker one favours the HS state,influencing the system'smagnetic properties.These transitions,characterised by changes in magnetic moment andcolour,make Fe!complexes a key focus for material design.  \nThis thesis investigates the SCO behaviour of Fe!I complexes through computationaland machine learning(ML)techniques,with a focus onligand functionalization,benchmarkingof density functional theory(DFT)methods,and studying dinuclear and polynuclear systems.The research begins with a systematic benchmark analysis of different DFT functionals todetermine the best-suited computational approaches for predicting the spin state energetics andtransition temperatures of Fel complexes.The results show that while certain functionalsprovide accurate predictions of SCO properties,the accuracy depends heavily on the specificcharacteristics of the FeI systems being studied.  \nAkey contribution of the research is the exploration of ligand design and its impact onSCO behaviour.By altering ligand substituents,the electronic environment around the metalion can be fine-tuned,providing control over the transition temperature(T1/2)and other SCOproperties.For instance,electron-donating groups on the ligand tend to lower T1/2,whileelectron-withdrawing groups increase it.These ligand-induced modifications are particularlyimportant in FeII complexes,as both electronic and steric factors play critical roles ingoverning the spin state transition.The study demonstrates how strategic ligand design can beused to tailor SCO properties for specific ","cbCaiv2ZpBRhaUAk","https://ap.wps.com/l/cbCaiv2ZpBRhaUAk","pdf",11982569,4,1,211,"English","en",105,"# Abstract\n## Spin-crossover background and Fe(II) focus\n## DFT functional benchmarking\n## Ligand design and transition-temperature control\n## Dinuclear and polynuclear cooperative effects\n## Machine learning models for SCO pre-screening","[{\"question\":\"What is the goal of the thesis on Fe(II) spin-crossover materials?\",\"answer\":\"To understand and predict SCO behavior of Fe(II) complexes using computational and machine learning approaches, enabling pre-screening of near-room temperature systems.\"},{\"question\":\"How does ligand functionalization affect spin-crossover properties?\",\"answer\":\"Ligand substituents modify the electronic environment around the metal center, tuning the transition temperature T1/2 and related SCO properties via electronic-donating or withdrawing effects and steric factors.\"},{\"question\":\"Why are dinuclear and polynuclear systems treated differently?\",\"answer\":\"They introduce interactions between multiple metal centers, leading to cooperative effects such as two-step transitions and stabilization of intermediate spin states that require more sophisticated computational modeling.\"}]","Electronic Structure and Machine Learning Protocols for Pre-screening of Near-room Temperature Spin-crossover Materials | 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is the goal of the thesis on Fe(II) spin-crossover materials?","Question",{"text":76,"@type":77},"To understand and predict SCO behavior of Fe(II) complexes using computational and machine learning approaches, enabling pre-screening of near-room temperature systems.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does ligand functionalization affect spin-crossover properties?",{"text":81,"@type":77},"Ligand substituents modify the electronic environment around the metal center, tuning the transition temperature T1/2 and related SCO properties via electronic-donating or withdrawing effects and steric factors.",{"name":83,"@type":74,"acceptedAnswer":84},"Why are dinuclear and polynuclear systems treated differently?",{"text":85,"@type":77},"They introduce interactions between multiple metal centers, leading to cooperative effects such as two-step transitions and stabilization of intermediate spin states that require more sophisticated computational 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