Single-Site Binding of Pyrene to Poly(ester-Imide)s Incorporating Long Spacer Units: Prediction of NMR Resonance-Patterns from a Fractal Model

Co-polycondensation of the diimide-based diols<i> N</i>,<i>N</i>'-bis(2-hydroxyethyl)hexafluoro-isopropylidene-diphthalimide, (HFDI), and <i>N</i>,<i>N</i>'-bis(2-hydroxy-ethyl)naphthalene-1,4,5,8-tetracarboxylic-diimide, (NDI), with aliphatic diacyl chlorides ClOC(CH₂)<i>_x</i>COCl (<i>x</i> = 5 to 8) affords linear copoly(ester-imide)s. Such copolymers interact with pyrene via supramolecular binding of the polycyclic aromatic molecule at NDI residues. This results in upfield complexation shifts and sequence-related splittings of the NDI ¹H NMR resonances, but gives a very different resonance-pattern from the corresponding copolymer where <i>x</i> = 2. Computational modelling of the polymer with <i>x</i> = 5 suggests that, in this system, each pyrene molecule binds to just a single NDI residue rather than to an adjacent pair of NDI's in a tight chain-fold ("dual-site" binding) as found for <i>x</i> = 2. The new single-site binding model enables the pattern of ¹H NMR resonances for copolymers with longer spacers (<i>x</i> = 5 to 8) to be reproduced and assigned by simulation from sequence-specific shielding factors based on the fractal known as the fourth-quarter Cantor set. As this set also enables an understanding of dual-site binding systems, it evidently provides a general numerical framework for supramolecular sequence-analysis in binary copolymers.

Single-Site Binding of Pyrene to Poly(ester-Imide)s Incorporating Long Spacer Units: Prediction of NMR Resonance-Patterns from a Fractal Model

Co-polycondensation of the diimide-based diols<i> N</i>,<i>N</i>'-bis(2-hydroxyethyl)hexafluoro-isopropylidene-diphthalimide, (HFDI), and <i>N</i>,<i>N</i>'-bis(2-hydroxy-ethyl)naphthalene-1,4,5,8-tetracarboxylic-diimide, (NDI), with aliphatic diacyl chlorides ClOC(CH₂)<i>_x</i>COCl (<i>x</i> = 5 to 8) affords linear copoly(ester-imide)s. Such copolymers interact with pyrene via supramolecular binding of the polycyclic aromatic molecule at NDI residues. This results in upfield complexation shifts and sequence-related splittings of the NDI ¹H NMR resonances, but gives a very different resonance-pattern from the corresponding copolymer where <i>x</i> = 2. Computational modelling of the polymer with <i>x</i> = 5 suggests that, in this system, each pyrene molecule binds to just a single NDI residue rather than to an adjacent pair of NDI's in a tight chain-fold ("dual-site" binding) as found for <i>x</i> = 2. The new single-site binding model enables the pattern of ¹H NMR resonances for copolymers with longer spacers (<i>x</i> = 5 to 8) to be reproduced and assigned by simulation from sequence-specific shielding factors based on the fractal known as the fourth-quarter Cantor set. As this set also enables an understanding of dual-site binding systems, it evidently provides a general numerical framework for supramolecular sequence-analysis in binary copolymers.

Single-Site Binding of Pyrene to Poly(ester-Imide)s Incorporating Long Spacerunits: Prediction of NMR Resonance-Patterns from a Fractal Model

Co-polycondensation of the diimide-based diols<i> N</i>,<i>N</i>'-bis(2-hydroxyethyl)hexafluoro-isopropylidene-diphthalimide, (HFDI), and <i>N</i>,<i>N</i>'-bis(2-hydroxy-ethyl)naphthalene-1,4,5,8-tetracarboxylic-diimide, (NDI), with aliphatic diacyl chlorides ClOC(CH₂)<i>_x</i>COCl (<i>x</i> = 5 to 8) affords linear copoly(ester-imide)s. Such copolymers interact with pyrene via supramolecular binding of the polycyclic aromatic molecule at NDI residues. This results in upfield complexation shifts and sequence-related splittings of the NDI ¹H NMR resonances, but gives a very different resonance-pattern from the corresponding copolymer where <i>x</i> = 2. Computational modelling of the polymer with <i>x</i> = 5 suggests that, in this system, each pyrene molecule binds to just a single NDI residue rather than to an adjacent pair of NDI's in a tight chain-fold ("dual-site" binding) as found for <i>x</i> = 2. The new single-site binding model enables the pattern of ¹H NMR resonances for copolymers with longer spacers (<i>x</i> = 5 to 8) to be reproduced and assigned by simulation from sequence-specific shielding factors based on the fractal known as the fourth-quarter Cantor set. As this set also enables an understanding of dual-site binding systems, it evidently provides a general numerical framework for supramolecular sequence-analysis in binary copolymers.

Single-Site Binding of Pyrene to Poly(ester-Imide)s Incorporating Long Spacer Units: Prediction of NMR Resonance-Patterns from a Fractal Model

Co-polycondensation of the diimide-based diols<i> N</i>,<i>N</i>'-bis(2-hydroxyethyl)hexafluoro-isopropylidene-diphthalimide, (HFDI), and <i>N</i>,<i>N</i>'-bis(2-hydroxy-ethyl)naphthalene-1,4,5,8-tetracarboxylic-diimide, (NDI), with aliphatic diacyl chlorides ClOC(CH₂)<i>_x</i>COCl (<i>x</i> = 5 to 8) affords linear copoly(ester-imide)s. Such copolymers interact with pyrene via supramolecular binding of the polycyclic aromatic molecule at NDI residues. This results in upfield complexation shifts and sequence-related splittings of the NDI ¹H NMR resonances, but gives a very different resonance-pattern from the corresponding copolymer where <i>x</i> = 2. Computational modelling of the polymer with <i>x</i> = 5 suggests that, in this system, each pyrene molecule binds to just a single NDI residue rather than to an adjacent pair of NDI's in a tight chain-fold ("dual-site" binding) as found for <i>x</i> = 2. The new single-site binding model enables the pattern of ¹H NMR resonances for copolymers with longer spacers (<i>x</i> = 5 to 8) to be reproduced and assigned by simulation from sequence-specific shielding factors based on the fractal known as the fourth-quarter Cantor set. As this set also enables an understanding of dual-site binding systems, it evidently provides a general numerical framework for supramolecular sequence-analysis in binary copolymers.

Mapping Binary Copolymer Property Space with Neural Networks

The extremely large number of unique polymer compositions that can be achieved through copolymerisation makes it an attractive strategy for tuning their optoelectronic properties. However, this same attribute also makes it challenging to explore the resulting property space and understand the range of properties that can be realised. In an effort to enable the rapid exploration of this space in the case of binary copolymers, we train a neural network using a tiered data generation strategy to accurately predict the optical and electronic properties of 350,000 binary copolymers that are, in principle, synthesizable from their dihalogen monomers via Yamamoto, or Suzuki-Miyaura and Stille coupling after one-step functionalisation. By extracting general features of this property space that would otherwise be obscured in smaller datasets, we identify simple models that effectively relate the properties of these copolymers to the homopolymers of their constituent monomers, and challenge common ideas behind copolymer design. We find that binary copolymerisation does not appear to allow access to regions of the optoelectronic property space that are not already sampled by the homopolymers, although conceptually allows for more fine-grained property control. Using the large volume of data available, we test the hypothesis that copolymerisation of ‘donor’ and ‘acceptor’ monomers can result in copolymers with a lower optical gap than their related homopolymers. Overall, despite the prevalence of this concept in the literature, we observe that this phenomenon is relatively rare, and propose conditions that greatly enhance the likelihood of its experimental realisation. Finally, through a ‘topographical’ analysis of the co-polymer property space, we show how this large volume of data can be used to identify dominant monomers in specific regions of property space that may be amenable to a variety of applications, such as organic photovoltaics, light emitting diodes, and thermoelectrics. <div> <div> <div> <p> </p> </div> </div> </div>

Mapping Binary Copolymer Property Space with Neural Networks

The extremely large number of unique polymer compositions that can be achieved through copolymerisation makes it an attractive strategy for tuning their optoelectronic properties. However, this same attribute also makes it challenging to explore the resulting property space and understand the range of properties that can be realised. In an effort to enable the rapid exploration of this space in the case of binary copolymers, we train a neural network using a tiered data generation strategy to accurately predict the optical and electronic properties of 350,000 binary copolymers that are, in principle, synthesizable from their dihalogen monomers via Yamamoto, or Suzuki-Miyaura and Stille coupling after one-step functionalisation. By extracting general features of this property space that would otherwise be obscured in smaller datasets, we identify simple models that effectively relate the properties of these copolymers to the homopolymers of their constituent monomers, and challenge common ideas behind copolymer design. We find that binary copolymerisation does not appear to allow access to regions of the optoelectronic property space that are not already sampled by the homopolymers, although conceptually allows for more fine-grained property control. Using the large volume of data available, we test the hypothesis that copolymerisation of ‘donor’ and ‘acceptor’ monomers can result in copolymers with a lower optical gap than their related homopolymers. Overall, despite the prevalence of this concept in the literature, we observe that this phenomenon is relatively rare, and propose conditions that greatly enhance the likelihood of its experimental realisation. Finally, through a ‘topographical’ analysis of the co-polymer property space, we show how this large volume of data can be used to identify dominant monomers in specific regions of property space that may be amenable to a variety of applications, such as organic photovoltaics, light emitting diodes, and thermoelectrics. <div> <div> <div> <p> </p> </div> </div> </div>

Mapping binary copolymer property space with neural networks

We map the property space of binary copolymers to understand how copolymerisation can be used to tune the optoelectronic properties of polymers.

Enabling the Exploration of Binary Copolymer Property Space with Neural Networks

<div> <div> <div> <p>The extremely large number of unique polymer compositions that can be achieved through copolymerisation makes it an attractive strategy for tuning their optoelectronic properties. However, this same attribute also makes it challenging to explore the resulting property space and understand the range of properties that can be realised. In an effort to enable the rapid exploration of this space in the case of binary copolymers, we train a neural network using a tiered data generation strategy to accurately predict the optical and electronic properties of 350,000 binary copolymers that are, in principle, synthesizable from their dihalogen monomers via Yamamoto, or Suzuki-Miyaura and Stille coupling after one-step functionalisation. By extracting general features of this property space that would otherwise be obscured in smaller datasets, we identify simple models that effectively relate the properties of these copolymers to the homopolymers of their constituent monomers. We find that binary copolymerisation does not appear to allow access to regions of the optoelectronic property space that are not already sampled by the homopolymers, although conceptually allows for more fine-grained property control. Using the large volume of data available, we test the hypothesis that copolymerisation of ‘donor’ and ‘acceptor’ monomers can result in copolymers with a lower optical gap than their related homopolymers and propose a heuristic to predict promising combinations of monomers for which this behaviour is likely. Finally, through a ‘topographical’ analysis of the co-polymer property space, we show how this large volume of data can be used to identify dominant monomers in specific regions of property space that may be amenable to a variety of applications, such as organic photovoltaics, light emitting diodes, and thermoelectrics. </p> </div> </div> </div>