Modular Software for Generating and Modelling Diverse Polymer Databases

Santana‐Bonilla, Alejandro; Castro, Raquel López-Ríos De; Sun, Peike; Ziolek, Robert M.; Lorenz, Christian D.

doi:10.26434/chemrxiv-2023-j01nk

Cited by 2 publications

(2 citation statements)

References 39 publications

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“…In many recent polymer builders, whose capabilities and particularities are summarized in the Supporting Information, the charge assignation is often not explicitly discussed. [69][70][71][72][73][74][75] On the other hand, Polyply 76 uses parameterized charges from GROMOS 2016H66 FF, 77 Polymatic 78 works with the charges provided by the user, Polymer Structure Predictor 79 uses monomer charges calculated on isolated monomers, and BIOVIA Materials Studio 80 permits to use FF-defined charges, charges obtained from Qeq, 81 or from Gasteiger 82 methodology. To account for the local environment of the monomer to some extent while keeping the computational cost manageable, oligomers of the homopolymeric systems were considered.Within this simplified setup, the atomic charges are calculated based on the AM1-BCC approach 66,67 and stored for each atom of the three different parts (head, repeat unit, tail).…”

Section: Challenges Of Setting Up Polymer Simulationsmentioning

confidence: 99%

Predicting the Glass Transition Temperature of Biopolymers via High-Throughput Molecular Dynamics Simulations and Machine Learning

Martí,

Pétuya,

Bosoni

et al. 2023

Preprint

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Nature has only provided us with a limited number of bio-based and biodegradable building blocks. Therefore, the fine tuning of the sustainable polymer properties is expected to be achieved through the control of the composition of bio-based copolymers for targeted applications such as cosmetics. Until now, the main approaches to alleviate the experimental efforts and accelerate the discovery of new polymers have relied on machine learning models trained on experimental data, which implies an enormous and difficult work in the compilation of data from heterogeneous sources. On the other hand, molecular dynamics simulations of polymers have shown that they can accurately capture the experimental trends for a series of properties. However, the combination of different ratios of monomers in copolymers can rapidly lead to a combinatorial explosion, preventing the investigation of all possibilities via molecular dynamics simulations. In this work, we show that the combination of machine learning approaches and high-throughput molecular dynamics simulations permits to quickly and efficiently sample and characterize the relevant chemical design space for specific applications. Reliable simulation protocols have been implemented to evaluate the glass transition temperature of a series of 58 homopolymers, which exhibit a good agreement with experiments, and 488 copolymers. Overall, 2,184 simulations (4 replicas per polymer) were performed, for a total simulation time of 143.052 µs. These results, constituting a dataset of 546 polymers, have been used to train a machine learning model for the prediction of the MD-calculated glass transition temperature with a mean absolute error of 19.34 K and a R2 score of 0.83. Overall, within its applicability domain, this machine learning model provides an impressive acceleration over molecular dynamics simulations: the glass transition temperature of thousands of polymers can be obtained within seconds, whereas it would have taken node-years to simulate them. This type of approach can be tuned to address different design spaces or different polymer properties and thus have the potential to accelerate the discovery of new polymers.

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Section: Challenges Of Setting Up Polymer Simulationsmentioning

confidence: 99%

Predicting the Glass Transition Temperature of Biopolymers via High-Throughput Molecular Dynamics Simulations and Machine Learning

Martí,

Pétuya,

Bosoni

et al. 2023

Preprint

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show abstract

“…16 PySoftK, the latest Python module capable of generating branched and cyclic copolymer structures, lacks user-defined systems, thus making it challenging to create more complex structures as desired by users, such as hyperbranched star, intricate cyclic, and net. 17 Apart from predefined templates, polymer structure creation can be achieved by predicting polymerization reactions between monomers to form polymer chains. 18−21 However, these predictions may sometimes yield inaccurate results.…”

Section: Introductionmentioning

confidence: 99%

From Linear to Nets: Multiconfiguration Polymer Structure Generation with PolyFlin

Umar,

Limpikirati,

Luckanagul

2023

J. Chem. Inf. Model.

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Molecular modeling and simulations are essential tools in polymer science and engineering, enabling researchers to predict and understand the properties of macromolecules, including their structure, dynamics, thermodynamics, and overall material characteristics. However, one of the key challenges in polymer simulation and modeling lies in the initial topology design, as existing programs often lack the capability to generate all types of polymer forms. In this study, we present PolyFlin, a powerful Python module that addresses this limitation by allowing the generation of a wide range of polymer structures, from simple homopolymers to complex copolymers, including grafts, cyclic, star, dendrimers, and nets. PolyFlin offers a versatile and efficient tool for exploring and creating diverse polymer architectures, facilitating advancements in various fields that require precise polymer modeling and simulation.

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Modular Software for Generating and Modelling Diverse Polymer Databases

Cited by 2 publications

References 39 publications

Predicting the Glass Transition Temperature of Biopolymers via High-Throughput Molecular Dynamics Simulations and Machine Learning

Predicting the Glass Transition Temperature of Biopolymers via High-Throughput Molecular Dynamics Simulations and Machine Learning

From Linear to Nets: Multiconfiguration Polymer Structure Generation with PolyFlin

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