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

Karuth, Anas; Casanola-Martin, Gerardo M.; Lystrom, Levi; Sun, Wenfang; Kilin, Dmitri; Kilina, Svetlana; Rasulev, Bakhtiyor

doi:10.1021/acs.jpclett.3c02533

Cited by 7 publications

(2 citation statements)

References 65 publications

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“…The electronic structural information on compounds is dependent on the topological structure, which can be expressed through numerical values by cheminformatics using descriptors. − Specifically the application of quantitative structure–activity relationships (QSAR) has been used to investigate the influence that topological and electronic structural information has on photoactivity. − …”

Section: Introductionmentioning

confidence: 99%

Relationships between the Photodegradation Reaction Rate and Structural Properties of Polymer Systems

Erickson,

Casañola-Martin,

Han

et al. 2024

J. Phys. Chem. B

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The development of reusable polymeric materials inspires an attempt to combine renewable biomass with upcycling to form a biorenewable closed system. It has been reported that 2,5-furandicarboxylic acid (FDCA) can be recovered for recycling when incorporated as monomers into photodegradable polymeric systems. Here, we develop a procedure to better understand the photodegradation reactions combining density functional theory (DFT) based time-dependent excited-state molecular dynamics (TDESMD) studies with machine learning-based quantitative structure−activity relationships (QSAR) methodology. This procedure allows for the unveiling of hidden structural features between active orbitals that affect the rate of photodegradation and is coined InfoTDESMD. Findings show that electrotopological features are influential factors affecting the rate of photodegradation in differing environments. Additionally, statistical validations and knowledge-based analysis of descriptors are conducted to further understand the structural features' influence on the rate of photodegradation of polymeric materials.

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Section: Introductionmentioning

confidence: 99%

Relationships between the Photodegradation Reaction Rate and Structural Properties of Polymer Systems

Erickson,

Casañola-Martin,

Han

et al. 2024

J. Phys. Chem. B

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“…As an alternative, machine learning techniques have succeeded in many cases to extract hidden patterns and develop predictions for complex problems only from data points of the observed phenomenon. 12 Specifically, neural networks -the computational model behind deep learning-have shown efficiency in Chemistry [13][14][15] as to classify organic reaction mechanisms, [16][17][18][19][20][21][22][23] to accelerate DFT calculations, 24,25 and to predict molecular properties [26][27][28][29][30][31][32][33] and antibacterial activities. 34 Indeed, despite machine learning methodologies have been applied to achiral nanomaterials, there is no examples including chirality in these structures.…”

Section: Introductionmentioning

confidence: 99%

Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case

G. Uceda,

Gijón,

Míguez-Lago

et al. 2024

Preprint

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In this work we predict, among more than a billion possibilities, the best candidates of halogenated [6]helicenes in order to obtain excellent chiroptical properties in terms of the rotatory strength (R). We have used DFT calculations to randomly create derivatives from 1 to 16 halogen atoms, that were then used as a data set to train different deep neural network models. It is worth noting that the simplest model affords a parametrization that allows to easily predict the value of R for any hexahalogenated [6]helicene. The correlation between calculated and predicted data increases together with the complexity of the model. The results show that some positions and halogens are preferred to increase the R value. In this sense, we have also synthesized the derivatives with the higher predicted R, obtaining excellent correlation among the values obtained experimentally, by DFT-calculations and machine learning predictions.

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Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case

Uceda,

Gijón,

Míguez‐Lago

et al. 2024

Angewandte Chemie

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The relationship between chemical structure and chiroptical properties is not always clearly understood. Nowadays, efforts to develop new systems with enhanced optical properties follow the trial‐error method. A large number of data would allow us to obtain more robust conclusions and guide research toward molecules with practical applications. In this sense, in this work we predict the chiroptical properties of millions of halogenated [6]helicenes in terms of the rotatory strength (R). We have used DFT calculations to randomly create derivatives including from 1 to 16 halogen atoms, that were then used as a data set to train different deep neural network models. These models allow us to i) predict the Rmax for any halogenated [6]helicene with a very low computational cost, and ii) to understand the physical reasons that favour some substitutions over others. Finally, we synthesized derivatives with higher predicted Rmax obtaining excellent correlation among the values obtained experimentally and the predicted ones.

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

Cited by 7 publications

References 65 publications

Relationships between the Photodegradation Reaction Rate and Structural Properties of Polymer Systems

Relationships between the Photodegradation Reaction Rate and Structural Properties of Polymer Systems

Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case

Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case

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