Electronic Structure and Machine Learning Protocols for Pre-screening of Near-room Temperature Spin-crossover Materials

Aug 5, 2026

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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.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages211

Reading Time8 h 52 min

AccessFree to download

Abstract

Spin-crossover background and Fe(II) focus

DFT functional benchmarking

Ligand design and transition-temperature control

Dinuclear and polynuclear cooperative effects

Machine learning models for SCO pre-screening

UNIVERSITATDEBARCELONA # Electronic Structure and machine learning protocolsfor pre-screening of near-room temperaturespin-crossover materials Daniel Vidal Ramon | NC
Aquesta tesi doctoral està subjecta a lallicencia Reconeixement-NoComercial 4.0.Espanva de
Creative Commons.
Esta tesis doctoral está sujeta a la licencia Reconocimiento -NoComercial 4.0.Espana de
Creative Commons.
This doctoral thesis is licensed under the Creative Commons Attribution-NonCommercial 4.0.
Spain License. | | --- | | | | # Electronic Structure and Machine Learningprotocols for pre-screening of near-roomtemperature spin-crossover materials Daniel Vidal Ramon UNIVERSITATDEBARCELONA Memoria presentada perDaniel Vidal Ramon Per optar al grau de Doctor per laUniversitat de Barcelona Programa de doctorat en Química Teorica iModelització Computacional # Electronic Structure and machine learning protocols forpre-screening of near-room temperaturespin-crossover materials Dirigida per: Dr.Jordi Ribas ArinoDr.Jordi Cirera Fernández Universitat de BarcelonaUniversitat de Barcelona Tutor: Dr.Jordi Ribas ArinoUniversitat de Barcelona OMNIA LVCE UNIVERSITATDEBARCELONA Barcelona,2024 The 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). Daniel 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. Abstract Spin-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. This 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. Akey 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

FAQs

What is the goal of the thesis on Fe(II) spin-crossover materials?

To understand and predict SCO behavior of Fe(II) complexes using computational and machine learning approaches, enabling pre-screening of near-room temperature systems.

How does ligand functionalization affect spin-crossover properties?

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.

Why are dinuclear and polynuclear systems treated differently?

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.

Aug 5, 2026

Upload by Liam