Computational high-throughput screening of polymeric photocatalysts: exploring the effect of composition, sequence isomerism and conformational degrees of freedom

Heath-Apostolopoulos, Isabelle; Zwijnenburg, Martijn A.

doi:10.1039/c8fd00171e

Cited by 23 publications

(19 citation statements)

References 44 publications

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“…The IPEA-xTB method was successfully used to make accurate predictions of electron ionization mass spectra 51 and for high-throughput screening of polymers. 52,53 For the medium-sized organic molecules, AIMNet-NSE model raises accuracy/computational performance ratio to the a new level. For the ChEMBL-20 dataset, the RMSE of IPEA-xTB EA and IP vs PBE0/ma-def2-SVP are 4.6 and 10.6 kcal/mol, compared to AIMNet-NSE errors of 2.7 and 2.4 kcal/mol, respectively.…”

Section: Resultsmentioning

confidence: 99%

Teaching a Neural Network to Attach and Detach Electrons from Molecules

Zubatyuk¹,

Smith²,

Nebgen³

et al. 2021

Preprint

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<p>Physics-inspired Artificial Intelligence (AI) is at the forefront of methods development in molecular modeling and computational chemistry. In particular, interatomic potentials derived with Machine Learning algorithms such as Deep Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and allow performing massive simulations. The applicability domain of DNN potentials is usually limited by the type of training data. As such, transferable models are aimed to be extensible in the description of chemical and conformational diversity of organic molecules. However, most DNN potentials, such as the AIMNet model we proposed previously, were parametrized for neutral molecules or closed-shell ions due to architectural limitations. In this work, we extend our AIMNet framework toward open-shell anions and cations. This model explores a new dimension of transferability by adding the charge-spin space. The resulting AIMNet model is capable of reproducing reference QM energies for cations, neutrals and anions with errors of 4.1, 2.1, 2.8 kcal/mol, respectively, compared to the reference QM simulations. The spin-charges have errors 0.01-0.06 electrons for small organic molecules containing nine chemical elements {H, C, N, O, F, Si, P, S and Cl}. Thus the proposed AIMNet model allows researchers to fully bypass QM calculations and derive the ionization potential, electron affinity, and conceptual Density Functional Theory quantities like electronegativity, hardness, and condensed Fukui functions. We show that these descriptors, along with learned atomic representations, could be used to model chemical reactivity through an example of regionselectivity in electrophilic aromatic substitution reactions.</p>

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Section: Resultsmentioning

confidence: 99%

Teaching a Neural Network to Attach and Detach Electrons from Molecules

Zubatyuk¹,

Smith²,

Nebgen³

et al. 2021

Preprint

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“…To date, the Linear polymer class has been used to explore very large (∼ 200000 molecules) chemical spaces of organic aromatic molecules. 9,[44][45][46][47][48][49] and (c) shows that with a family of building blocks at hand (FIG. 8(a)), we can easily construct arbitrary Linear polymers and Macrocycle structures.…”

Section: A Polymers and Macrocyclesmentioning

confidence: 99%

Stk: An Extendable Python Framework for Automated Molecular and Supramolecular Structure Assembly and Discovery

Turcani¹,

Tarzia²,

Szczypiński³

et al. 2021

Preprint

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<div>Computational software workflows are emerging as all-in-one solutions to speed up the discovery of new materials.</div><div>Many computational approaches require the generation of realistic structural models for property prediction and candidate screening. However, molecular and supramolecular materials represent classes of materials with many potential applications for which there is no go-to database of existing structures or general protocol for generating structures. Here, we report a new version of the supramolecular toolkit, <i>stk</i>, an open-source, extendable and modular Python framework for general structure generation of (supra)molecular structures. Our construction approach follows a bottom-up process and minimises the input required from the user, making <i>stk</i> user-friendly and applicable to many material classes. This version of <i>stk</i> includes metal-containing structures and rotaxanes as well as general implementation and interface improvements. Additionally, this version includes built-in tools for exploring chemical space with an evolutionary algorithm and tools for database generation and visualisation. The latest version of <i>stk</i> is freely available at github.com/lukasturcani/stk</div>

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“…Using trimers instead of the entire oligomer chain to obtain molecular ngerprints dramatically reduces the computational effort required for ngerprinting, while preserving all of the sub-structural information of the polymer. The use of 2D SMILES rather than representations of the 3D structures of the polymers is supported by the weak dependence of the optoelectronic properties of the polymer on the conformational degrees of freedom, 35,39 already alluded to in the introduction (see also Fig. S1 †).…”

Section: Neural Network Training and Evaluationmentioning

confidence: 99%

“…35 Further, we used the resulting high-throughput approach to demonstrate the weak dependence of the predicted properties on the exact polymer conformation. 39 In turn, these two observations suggest that (i) xTB can be used to generate DFT-quality training data and (ii) 3D structural models of polymer chains may not be necessary for the prediction of optoelectronic properties (i.e. we can ignore conformation effects while focussing only on composition, see below), permitting the use of 2D molecular representations as descriptors.…”

Section: Introductionmentioning

confidence: 99%

Mapping Binary Copolymer Property Space with Neural Networks

Sprick¹,

Jelfs²,

Zwijnenburg

2019

Preprint

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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>

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Computational high-throughput screening of polymeric photocatalysts: exploring the effect of composition, sequence isomerism and conformational degrees of freedom

Abstract: We discuss a low-cost computational workflow for the high throughput screening of polymeric photocatalysts.

Cited by 23 publications

References 44 publications

Teaching a Neural Network to Attach and Detach Electrons from Molecules

Teaching a Neural Network to Attach and Detach Electrons from Molecules

Stk: An Extendable Python Framework for Automated Molecular and Supramolecular Structure Assembly and Discovery

Mapping Binary Copolymer Property Space with Neural Networks

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