Combined Machine Learning, Computational and Experimental Analysis of the Iridium(III) Complexes with Red to Near-IR Emission

Various coordination complexes have been the subject of experimental or theoretical studies in recent decades because of their fascinating photophysical properties. In this work a combined experimental and computational approach applied to investigate the optical properties of monocationic Ir(III) c...

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Published inChemRxiv
Main Authors Karuth, Anas, Casanola-Martin, Gerardo, Lystrom, Levi, Sun, Wenfang, Kilin, Dmitri, Kilina, Svetlana, Rasulev, Bakhtiyor
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LanguageEnglish
Published Washington American Chemical Society 30.11.2022
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Abstract Various coordination complexes have been the subject of experimental or theoretical studies in recent decades because of their fascinating photophysical properties. In this work a combined experimental and computational approach applied to investigate the optical properties of monocationic Ir(III) complexes. In result, an interpretative machine learning-based Quantitative Structure-Activity Relationship (QSAR) model was successfully developed, which can reliably predict the emission wavelength of the Ir(III) complexes and provides foundations for theoretical evaluation of the optical properties of Ir(III) complexes. A hypothesis was proposed to mechanistically explain the differences in emission wavelengths between structurally different individual Ir(III) complexes. To the best of our knowledge, this is the first attempt to develop predictive machine learning (QSAR) model for the optical properties of Ir(III) complexes. The efficacy of the developed model was demonstrated by high R2 values for the training and test sets of 0.84 and 0.87, respectively, and by performing the validation using y-scrambling techniques. A notable relationship between the N-N distance in the diimine ligands of the Ir(III) complexes and emission wavelengths was revealed. This combined experimental and computational approach shows a great potential for rational design of new Ir(III) complexes with desired optical properties. Moreover, the developed methodology could be extended to other octahedral transition-metal complexes.
AbstractList Various coordination complexes have been the subject of experimental or theoretical studies in recent decades because of their fascinating photophysical properties. In this work a combined experimental and computational approach applied to investigate the optical properties of monocationic Ir(III) complexes. In result, an interpretative machine learning-based Quantitative Structure-Activity Relationship (QSAR) model was successfully developed, which can reliably predict the emission wavelength of the Ir(III) complexes and provides foundations for theoretical evaluation of the optical properties of Ir(III) complexes. A hypothesis was proposed to mechanistically explain the differences in emission wavelengths between structurally different individual Ir(III) complexes. To the best of our knowledge, this is the first attempt to develop predictive machine learning (QSAR) model for the optical properties of Ir(III) complexes. The efficacy of the developed model was demonstrated by high R2 values for the training and test sets of 0.84 and 0.87, respectively, and by performing the validation using y-scrambling techniques. A notable relationship between the N-N distance in the diimine ligands of the Ir(III) complexes and emission wavelengths was revealed. This combined experimental and computational approach shows a great potential for rational design of new Ir(III) complexes with desired optical properties. Moreover, the developed methodology could be extended to other octahedral transition-metal complexes.
Author Lystrom, Levi
Karuth, Anas
Casanola-Martin, Gerardo
Kilin, Dmitri
Kilina, Svetlana
Rasulev, Bakhtiyor
Sun, Wenfang
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Keywords QSPR
near-IR emission
machine learning
iridium(III) complexes
predictive modeling
computational
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Snippet Various coordination complexes have been the subject of experimental or theoretical studies in recent decades because of their fascinating photophysical...
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SubjectTerms Chemistry
Chemoinformatics - Computational Chemistry
Computational Chemistry and Modeling
Coordination compounds
Emission analysis
Inorganic Chemistry
Iridium compounds
Machine Learning
Optical properties
Organometallic Chemistry
Theoretical and Computational Chemistry
Transition metal compounds
Wavelengths
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Title Combined Machine Learning, Computational and Experimental Analysis of the Iridium(III) Complexes with Red to Near-IR Emission
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