2021
DOI: 10.1016/j.matpr.2021.02.730
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Deep learning based approach for prediction of glass transition temperature in polymers

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Cited by 27 publications
(19 citation statements)
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“…As an exciting alternative, utilizing machine learning (ML) techniques and the increasing amount of polymer data sets offer a new opportunity to tackle the challenge in the polymer field. Successful polymer informatics attempts have touched upon the a number of property predictions like polymers’ electronic bandgap, , dielectric constant, refractive index, etc., but a lot more attention has been paid to the prediction of polymers’ glass transition temperatures. , This is primarily reflective of the facts that (1) the glass transition temperature is an important property controlling the phase transition and therefore the application of polymers and (2) the glass transition temperature T g is the most reported experimental measurement in publicly accessible databases like PoLyInfo, the Polymer Property Predictor and Database (NIST), and the CROW Polymer Properties Database …”
Section: Introductionmentioning
confidence: 99%
“…As an exciting alternative, utilizing machine learning (ML) techniques and the increasing amount of polymer data sets offer a new opportunity to tackle the challenge in the polymer field. Successful polymer informatics attempts have touched upon the a number of property predictions like polymers’ electronic bandgap, , dielectric constant, refractive index, etc., but a lot more attention has been paid to the prediction of polymers’ glass transition temperatures. , This is primarily reflective of the facts that (1) the glass transition temperature is an important property controlling the phase transition and therefore the application of polymers and (2) the glass transition temperature T g is the most reported experimental measurement in publicly accessible databases like PoLyInfo, the Polymer Property Predictor and Database (NIST), and the CROW Polymer Properties Database …”
Section: Introductionmentioning
confidence: 99%
“…Looking at the T g values (Table 3), one can assume that the bigger -C 8 F 13 H 4 group in (CF)6 than -C 3 F 3 H 4 in (CF)1 might have reduced the chain mobility, whereas the longer -C 12 F 21 H 4 group in (CF)10 could induce a more flexible backbone chain, causing the lowering of the T g of this structure in comparison to (CF)6 but maintaining the same level as for (CF)0 [67]. On the other hand, the higher T g for the CF(6) sample may also be related to the higher thickness of the coating [67,68]. A similar phenomenon has been observed in other silica hybrids [69,70].…”
Section: Discussionmentioning
confidence: 94%
“…According to the data in Table 5, PVC/nZB411 composites had lowest molecular or chain mobility. Agglomeration of nanoparticles caused an increase of free volume and Tg value of the composites decreased [36].…”
Section: Dynamic Mechanical Analysismentioning
confidence: 98%