A computational structure–property relationship study of glass transition temperatures for a diverse set of polymers

Chen, Min; Jabeen, Farukh; Rasulev, Bakhtiyor; Ossowski, Martin; Boudjouk, Philip

doi:10.1002/polb.24602

Cited by 30 publications

(18 citation statements)

References 29 publications

(78 reference statements)

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“…It is estimated that the flow velocity (i.e., the gradient of the displacement profile) in the flow stage was about 0.3 Å/ps, which was more than 30 times faster than that in the diffusion stage (~9.1 × 10 −3 Å/ps). Since pressure is the driving force for flow transportation, we can thus define a transition nominal pressure (

) for the diamond nanochannel that determines when flow transportation occurred, similar to the definition of glass transition temperature in polymers [ 60 , 61 , 62 , 63 ]. As plotted in Figure 4 d, the pressure threshold between the diffusion and flow stages is defined as the transition nominal pressure, which was about 7.7 GPa for the diamond nanochannel.…”

Section: Resultsmentioning

confidence: 99%

Atomistic Simulations of the Permeability and Dynamic Transportation Characteristics of Diamond Nanochannels

Dong

Shi

et al. 2022

Nanomaterials

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Through atomistic simulations, this work investigated the permeability of hexagonal diamond nanochannels for NaCl solution. Compared with the multilayer graphene nanochannel (with a nominal channel height of 6.8 Å), the diamond nanochannel exhibited better permeability. The whole transportation process can be divided into three stages: the diffusion stage, the transition stage and the flow stage. Increasing the channel height reduced the transition nominal pressure that distinguishes the diffusion and flow stages, and improved water permeability (with increased water flux but reduced ion retention rate). In comparison, channel length and solution concentration exerted ignorable influence on water permeability of the channel. Further simulations revealed that temperature between 300 and 350 K remarkably increased water permeability, accompanied by continuously decreasing transition nominal pressure. Additional investigations showed that the permeability of the nanochannel could be effectively tailored by surface functionalization. This work provides a comprehensive atomic insight into the transportation process of NaCl solution in a diamond nanochannel, and the established understanding could be beneficial for the design of advanced nanofluidic devices.

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Section: Resultsmentioning

confidence: 99%

Atomistic Simulations of the Permeability and Dynamic Transportation Characteristics of Diamond Nanochannels

Dong

Shi

et al. 2022

Nanomaterials

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“…By solving the multi-electron system of the atom numerically, it was possible to know the structural, electronic, optoelectronic, thermodynamic, and other properties of atoms and compounds at quantum mechanical level theory [7][8][9][10][11][12][13][14][15]. With a large amount of chemical descriptor data availability, chem-informatic space saw the rise in statistical relations being derived between desired material properties and chemical descriptors in what came to be known as Quantitative Structure-Property/Structure-Activity studies and is said to have accelerated new material or drug molecule discovery [16][17][18][19][20][21].…”

Section: Computational Chemistry and Chem-informaticsmentioning

confidence: 99%

Recent advances in the application of data science and machine/deep learning methods to research in chemical and biological sciences : a review

S¹

2020

Preprint

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The rise in application of methods of data science and machine/deep learning in chemical and biological sciences must be discussed in the light of the fore-running disciplines of bio/chem-informatics and computational chemistry and biology which helped in the accumulation ofenormous research data because of which successful application of data-driven approaches have been made possible now. Many of the tasks and goals of Ab initio methods in computational chemistry such as determination of optimized structure and other molecular properties of atoms, molecules, and compounds are being carried out with much lesser computational cost with data-driven machine/deep learning-based predictions. One observes a similar trend in computational biology, wherein, data-driven machine/deep learning methods are being proposed to predict the structure and dynamical of interactions of biological macromolecules such as proteins and DNA over computational expensive molecular dynamics based methods. In the cheminformatics space,one sees the rise of deep neural network-based methods that have scaled traditional structure-property/structure-activity to handle big data to design new materials with desired property and drugs with required activity in deep learning-based de novo molecular design methods. In thebioinformatics space, data-driven machine/deep learning approaches to genomic and proteomic data have led to interesting applications in fields such as precision medicine, prognosis prediction, and more. Thus the success story of the application of data science, machine/deep learning, andartificial intelligence to the disciple of chem/bio-informatics, and computational chemistry and biology has been told in light of how these fore-running disciplines had created huge repositories of data for data-driven approaches to be successful in these disciplines.

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“…The GA‐MLR technique found that a model consisting of seven variables provided the best fit for the training and test data sets without overfitting. The nature of these structural descriptors indicated that the glass transition temperature could be governed by the electronegative groups on polymers …”

Section: Machine Learning For Polymer Systemsmentioning

confidence: 99%

Soft Matter Informatics: Current Progress and Challenges

Peerless¹,

Milliken²,

Oweida³

et al. 2018

Advcd Theory and Sims

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The number of applications of informatics or data-driven discovery is growing in many fields, including materials science. The large amount of data that is readily available, combined with high-level statistical algorithms, is proving to be extremely useful in developing complex predictive models with little to no human supervision or bias. However, in the field of soft matter, which includes complex materials such as polymers, liquids, emulsions, colloids, and gels, there is a slower adoption of informatics strategies than in adjacent fields. Here, the current state of soft matter informatics is discussed. Challenges specific to soft materials, including data classification, various degrees of organization at multiple length scales, and process-dependent properties require unique approaches by researchers in order to develop robust informatics approaches in soft matter. The current ability to extract and analyze the information from the PoLyInfo database is demonstrated by the fitting of the Flory-Fox equation for glass transition temperature for several polymers. This Progress Report serves to introduce and excite the scientific community about the remarkable potential of informatics for exploring the properties of soft materials.

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A computational structure–property relationship study of glass transition temperatures for a diverse set of polymers

Cited by 30 publications

References 29 publications

Atomistic Simulations of the Permeability and Dynamic Transportation Characteristics of Diamond Nanochannels

Atomistic Simulations of the Permeability and Dynamic Transportation Characteristics of Diamond Nanochannels

Recent advances in the application of data science and machine/deep learning methods to research in chemical and biological sciences : a review

Soft Matter Informatics: Current Progress and Challenges

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