Close Menu
Simply Invest Asia
  • Home
  • About us
  • Explore industries/sectors
    • Automobile
    • Aviation
    • Banking
    • Biotechnology
    • Chemical & Fertilizer
    • Entertainment and Media
    • Food Processing
    • Healthcare
    • Iron and Steel
    • Leather
    • Mining
    • Oil and Gas
    • Pharmaceutical
  • Explore by countries
    • China
    • Dubai / UAE
    • Hong Kong
    • India
    • Indonesia
    • Japan
    • Malaysia
  • Explore cities
    • Bangkok
    • Beijing
    • Chongqing
    • Delhi
    • Dubai
    • Guangzhou
    • Jakarta
    • Kuala Lumpur
  • Why Asia
Facebook X (Twitter) Instagram Threads
Trending:
  • Company behind UK leather retailer plunges into liquidation as high street stores close
  • Planning a UAE Trip? What Every U.S. Visitor Should Know
  • Cebgo to absorb AirSWIFT operations from early 3Q26
  • 38-year-old Dubai resident dies while playing cricket; parents fly in for burial
  • Japan Stocks Take A Breather As BOJ Decision Nears
  • PolyU hosts inaugural HKSAR “3Chuang Competition” Contest, unleashing innovative youth power to drive innovation and technology development in Hong Kong
  • Rockit secures significant PVR enforcement win in China | News
  • Interim trade deal: India, US to hold fresh talks on June 23-24 – Asia News Network
  • CEO Note: Indonesia’s forests become top political issue
  • Uncertainty-aware prediction of the glass transition temperature of aliphatic polycarbonates using ensemble machine learning
  • Political parties showing their ‘true colours’, says Amanah leader | Malaysia
  • Bangkok Airways: Four ways to restore
  • Asia FX Talk – BOJ and RBA kick off central bank week
  • EVE Health Group Initiates Pilot Study of Libbo Oral Soluble Film to Treat ED
  • UAE offering Canadians major travel for trips to Dubai, Abu Dhabi | Daily Hive
  • JD.com Invests $4.5B In Hong Kong Expansion—When Will It See A Return? – JD.com (NASDAQ:JD)
  • Will Beijing’s tighter capital-control rules dampen Hong Kong’s housing rebound?
  • Lifestyle twist: R&F Princess Cove shows Guangzhou R&F’s waterfront play
Tuesday, June 16
Facebook X (Twitter) Instagram
Simply Invest Asia
  • Home
  • About us
  • Explore industries/sectors
    • Automobile
    • Aviation
    • Banking
    • Biotechnology
    • Chemical & Fertilizer
    • Entertainment and Media
    • Food Processing
    • Healthcare
    • Iron and Steel
    • Leather
    • Mining
    • Oil and Gas
    • Pharmaceutical
  • Explore by countries
    • China
    • Dubai / UAE
    • Hong Kong
    • India
    • Indonesia
    • Japan
    • Malaysia
  • Explore cities
    • Bangkok
    • Beijing
    • Chongqing
    • Delhi
    • Dubai
    • Guangzhou
    • Jakarta
    • Kuala Lumpur
  • Why Asia
Simply Invest Asia
Home»Explore industries/sectors»Chemical & Fertilizer»Uncertainty-aware prediction of the glass transition temperature of aliphatic polycarbonates using ensemble machine learning
Chemical & Fertilizer

Uncertainty-aware prediction of the glass transition temperature of aliphatic polycarbonates using ensemble machine learning

By IslaJune 16, 20268 Mins Read
Share
Facebook Twitter Pinterest Threads Bluesky Copy Link


  • Fukushima K. Poly(trimethylene carbonate)-based polymers engineered for biodegradable functional biomaterials. Biomater Sci. 2016;4:9–24. https://doi.org/10.1039/c5bm00123d.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Kernbichl S, Rieger B. Aliphatic polycarbonates derived from epoxides and CO: a comparative study of poly(cyclohexene carbonate) and poly(limonene carbonate). Polymer. 2020;205:122667. https://doi.org/10.1016/j.polymer.2020.122667.

    Article 
    CAS 

    Google Scholar
     

  • Yu W, Maynard E, Chiaradia V, Arno MC, Dove AP. Aliphatic polycarbonates from cyclic carbonate monomers and their application as biomaterials. Chem Rev. 2021;121:10865–907. https://doi.org/10.1021/acs.chemrev.0c00883.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Hauenstein O, Agarwal S, Greiner A. Bio-based polycarbonate as synthetic toolbox. Nat Commun. 2016;7:11862. https://doi.org/10.1038/ncomms11862.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Montagna V, Takahashi J, Tsai MY, Ota T, Zivic N, Kawaguchi S, et al. Methoxy-functionalized glycerol-based aliphatic polycarbonate: organocatalytic synthesis, blood compatibility, and hydrolytic property. ACS Biomater Sci Eng. 2021;7:472–81. https://doi.org/10.1021/acsbiomaterials.0c01460.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Wang H, Xu F, Zhang Z, Feng M, Jiang M, Zhang S. Bio-based polycarbonates: progress and prospects. RSC Sustain. 2023;1:2162–79. https://doi.org/10.1039/D3SU00248A.

    Article 
    CAS 

    Google Scholar
     

  • Liu WN, Wang MQ, Ding ZQ, Li YS, Wang B. Modular access to aliphatic polycarbonates with tunable properties and dual closed-loop recyclability by polycondensation-depolymerization-repolymerization strategy. Angew Chem Int Ed. 2025;64:e202505333. https://doi.org/10.1002/anie.202505333.

    Article 
    CAS 

    Google Scholar
     

  • Watanabe Y, Kato R, Fukushima K, Kato T. Degradable and nanosegregated elastomers with multiblock sequences of biobased aromatic mesogens and biofunctional aliphatic oligocarbonates. Macromolecules. 2022;55:10285–93. https://doi.org/10.1021/acs.macromol.2c01747.

    Article 
    CAS 

    Google Scholar
     

  • Watanabe Y, Takaoka S, Haga Y, Kishi K, Hakozaki S, Narumi A, et al. Organic carboxylate salt-enabled alternative synthetic routes for bio-functional cyclic carbonates and aliphatic polycarbonates. Polym Chem. 2022;13:5193–9. https://doi.org/10.1039/d2py00705c.

    Article 
    CAS 

    Google Scholar
     

  • Fukushima K, Hakozaki S, Lang RJ, Haga Y, Nakai S, Narumi A, et al. Hydrolyzable and biocompatible aliphatic polycarbonates with ether-functionalized side chains attached via amide linkers. Polym J. 2024;56:443–53. https://doi.org/10.1038/s41428-023-00874-6.

    Article 
    CAS 

    Google Scholar
     

  • More AS, Palaskar DV, Cloutet E, Gadenne B, Alfos C, Cramail H. Aliphatic polycarbonates and poly(ester carbonate)s from fatty acid derived monomers. Polym Chem. 2011;2:2796–803. https://doi.org/10.1039/c1py00326g.

    Article 
    CAS 

    Google Scholar
     

  • Hauenstein O, Reiter M, Agarwal S, Rieger B, Greiner A. Bio-based polycarbonate from limonene oxide and CO with high molecular weight, excellent thermal resistance, hardness and transparency. Green Chem. 2016;18:760–70. https://doi.org/10.1039/c5gc01694k.

    Article 
    CAS 

    Google Scholar
     

  • Sun JJ, Kuckling D. Synthesis of high-molecular-weight aliphatic polycarbonates by organo-catalysis. Polym Chem. 2016;7:1642–9. https://doi.org/10.1039/c5py01843a.

    Article 
    CAS 

    Google Scholar
     

  • Pilania G, Wang CC, Jiang X, Rajasekaran S, Ramprasad R. Accelerating materials property predictions using machine learning. Sci Rep. 2013;3:2810. https://doi.org/10.1038/srep02810.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ramprasad R, Batra R, Pilania G, Mannodi-Kanakkithodi A, Kim C. Machine learning in materials informatics: recent applications and prospects. npj Comput Mater. 2017;3:54 https://doi.org/10.1038/s41524-017-0056-5.

    Article 

    Google Scholar
     

  • Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Machine learning for molecular and materials science. Nature. 2018;559:547–55. https://doi.org/10.1038/s41586-018-0337-2.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Batra R, Song L, Ramprasad R. Emerging materials intelligence ecosystems propelled by machine learning. Nat Rev Mater. 2021;6:655–78. https://doi.org/10.1038/s41578-020-00255-y.

    Article 

    Google Scholar
     

  • Audus DJ, de Pablo JJ. Polymer informatics: opportunities and challenges. ACS Macro Lett. 2017;6:1078–82. https://doi.org/10.1021/acsmacrolett.7b00228.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pruksawan S, Lambard G, Samitsu S, Sodeyama K, Naito M. Prediction and optimization of epoxy adhesive strength from a small dataset through active learning. Sci Technol Adv Mat. 2019;20:1010–21. https://doi.org/10.1080/14686996.2019.1673670.

    Article 
    CAS 

    Google Scholar
     

  • Takano S, Kaneko H. Monomer design of polymer materials with high refractive index and high glass transition temperature. J Comput Chem Jpn. 2019;18:115–21. https://doi.org/10.2477/jccj.2019-0004.

    Article 
    CAS 

    Google Scholar
     

  • Chen LH, Pilania G, Batra R, Huan TD, Kim C, Kuenneth C, et al. Polymer informatics: current status and critical next steps. Mat Sci Eng R. 2021;144:100595. https://doi.org/10.1016/j.mser.2020.100595.

    Article 

    Google Scholar
     

  • Oka H, Yoshizawa A, Shindo H, Matsumoto Y, Ishii M. Machine extraction of polymer data from tables using XML versions of scientific articles. Sci Technol Adv Mater Methods. 2021;1:12–23. https://doi.org/10.1080/27660400.2021.1899456.

    Article 

    Google Scholar
     

  • Patra TK. Data-Driven Methods for Accelerating Polymer Design. ACS Polym Au. 2022;2:8–26. https://doi.org/10.1021/acspolymersau.1c00035.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Kuenneth C, Ramprasad R. polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics. Nat Commun. 2023;14:4099. https://doi.org/10.1038/s41467-023-39868-6.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Amamoto Y, Mototake Y, Ohnishi T. Natural language processing-based topic models for analyzing trends in polymer science. Polym J. 2025;57:1033–41. https://doi.org/10.1038/s41428-025-01060-6.

    Article 
    CAS 

    Google Scholar
     

  • Amamoto Y, Ohnishi T. Large language model-based topic modeling for analyzing research trends in polymer science literature. Mater Today Commun. 2026;52:114945. https://doi.org/10.1016/j.mtcomm.2026.114945.

    Article 
    CAS 

    Google Scholar
     

  • Bertinetto C, Duce C, Micheli A, Solaro R, Starita A, Tiné MR. Prediction of the glass transition temperature of (meth)acrylic polymers containing phenyl groups by recursive neural network. Polymer. 2007;48:7121–9. https://doi.org/10.1016/j.polymer.2007.09.043.

    Article 
    CAS 

    Google Scholar
     

  • Pilania G, Iverson CN, Lookman T, Marrone BL. Machine-learning-based predictive modeling of glass transition temperatures: a case of polyhydroxyalkanoate homopolymers and copolymers. J Chem Inf Model. 2019;59:5013–25. https://doi.org/10.1021/acs.jcim.9b00807.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Tao L, Chen G, Li Y. Machine learning discovery of high-temperature polymers. Patterns. 2021;2:100225. https://doi.org/10.1016/j.patter.2021.100225.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Casanola-Martin GM, Karuth A, Pham-The H, Gonzalez-Diaz H, Webster DC, Rasulev B. Machine learning analysis of a large set of homopolymers to predict glass transition temperatures. Commun Chem. 2024;7:226 https://doi.org/10.1038/s42004-024-01305-0.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yang JH, Lee J, Kwon H, Sohn EH, Chang H, Jang S. High glass transition temperature fluorinated polymers based on transfer learning with small experimental data. Macromol Rapid Comm. 2024;45:2400161. https://doi.org/10.1002/marc.202400161.

    Article 
    CAS 

    Google Scholar
     

  • Ming YQ, Li JL, Wen JL, Shuai L, Yang J, Nie YJ. Machine learning in constructing structure-property relationships of polymers. Chem Phys Rev. 2025;6:021305. https://doi.org/10.1063/5.0251012.

    Article 
    CAS 

    Google Scholar
     

  • Amamoto Y, Kojio K, Takahara A, Masubuchi Y, Ohnishi T. Complex network representation of the structure-mechanical property relationships in elastomers with heterogeneous connectivity. Patterns. 2020;1:100135.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Amamoto Y. Data-driven approaches for structure-property relationships in polymer science for prediction and understanding. Polym J. 2022;54:957–67. https://doi.org/10.1038/s41428-022-00648-6.

    Article 
    CAS 

    Google Scholar
     

  • Hatakeyama-Sato K. Recent advances and challenges in experiment-oriented polymer informatics. Polym J. 2023;55:117–31. https://doi.org/10.1038/s41428-022-00734-9.

    Article 
    CAS 

    Google Scholar
     

  • Tamura R, Nagata K, Sodeyama K, Nakamura K, Tokuhira T, Shibata S, et al. Machine learning prediction of the mechanical properties of injection-molded polypropylene through X-ray diffraction analysis. Sci Technol Adv Mat. 2024;25:2388016. https://doi.org/10.1080/14686996.2024.2388016.

    Article 
    CAS 

    Google Scholar
     

  • Higashi Y, Okuwaki K, Mochizuki Y, Fujigaya T, Kato K. Feature vectorization of microphase-separated structures in polymeric materials using dissipative particle dynamics and persistent homology for machine learning applications. Digit Discov. 2025;4:1339–51. https://doi.org/10.1039/d4dd00376d.

    Article 
    CAS 

    Google Scholar
     

  • Yang CI, Li YP. Explainable uncertainty quantifications for deep learning-based molecular property prediction. J Cheminform. 2023;15:13. https://doi.org/10.1186/s13321-023-00682-3.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li TY, Chen ZY, Zhang Z, Wei ZH, Zhong GJ, Li ZM, et al. Predicting stress-strain curve with confidence: balance between data minimization and uncertainty quantification by a dual Bayesian model. Polymers. 2025;17:550 https://doi.org/10.3390/polym17040550.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zheng Y, Biswal AK, Guo Y, Thakolkaran P, Kokane Y, Varshney V, et al. Toward sustainable polymer design: a molecular dynamics-informed machine learning approach for vitrimers. Digit Discov. 2025;4:2559–69. https://doi.org/10.1039/D5DD00239G.

    Article 

    Google Scholar
     

  • Deringer VL, Bartok AP, Bernstein N, Wilkins DM, Ceriotti M, Csanyi G. Gaussian process regression for materials and molecules. Chem Rev. 2021;121:10073–141. https://doi.org/10.1021/acs.chemrev.1c00022.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Amamoto Y, Koganemaru C, Kojio K, Takahara A, Yamamoto S, Okazawa K, et al. A machine learning approach to designing and understanding tough, degradable polyamides. npj Comput Mater. 2025;11:198 https://doi.org/10.1038/s41524-025-01696-1.

    Article 
    CAS 

    Google Scholar
     

  • Tang H, Yue TL, Li Y. Assessing uncertainty in machine learning for polymer property prediction: a benchmark study. J Chem Inf Model. 2025;65:6585–98. https://doi.org/10.1021/acs.jcim.5c00550.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Alfaraj YS, Mohapatra S, Shieh P, Husted KEL, Ivanoff DG, Lloyd EM, et al. A model ensemble approach enables data-driven property prediction for chemically deconstructable thermosets in the low-data regime. ACS Cent Sci. 2023;9:1810–9. https://doi.org/10.1021/acscentsci.3c00502.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jacobs R, Schultz LE, Scourtas A, Schmidt KJ, Price-Skelly O, Engler W, et al. Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility. Mach Learn Sci Technol. 2024;5:045051 https://doi.org/10.1088/2632-2153/ad95db.

    Article 

    Google Scholar
     

  • Jiang XY, Sun HF, Choudhary K, Zhuang HL, Nian Q. Interpretable ensemble learning for materials property prediction with classical interatomic potentials. npj Comput Mater. 2025;11:319 https://doi.org/10.1038/s41524-024-01468-3.

    Article 

    Google Scholar
     

  • Choi S, Lee J, Seo J, Han SW, Lee SH, Seo J-H, et al. Automated BigSMILES conversion workflow and dataset for homopolymeric macromolecules. Sci Data. 2024;11:371. https://doi.org/10.1038/s41597-024-03212-4.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • RDKit: Open-source cheminformatics. https://www.rdkit.org.

  • Moriwaki H, Tian Y-S, Kawashita N, Takagi T. Mordred: a molecular descriptor calculator. J Cheminform. 2018;10:4. https://doi.org/10.1186/s13321-018-0258-y.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • McInnes L, Healy J, Saul N, Großberger L. UMAP: Uniform Manifold Approximation and Projection. J. Open Source Softw. 2018;3:861. https://doi.org/10.21105/joss.00861.

  • Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, et al. Lightgbm: a highly efficient gradient boosting decision tree. Adv Neural Inf Process Syst. 2017;30:3146–54.


    Google Scholar
     

  • Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825–30.


    Google Scholar
     

  • Akiba T, Sano S, Yanase T, Ohta T, Koyama M. Optuna: A Next-Generation Hyperparameter Optimization Framework.KDD ’19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019;2623–31. https://doi.org/10.1145/3292500.3330701.

  • Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, et al. PyTorch: an imperative style, high-performance deep learning library. Adv Neural Inf Process Syst. 2019;32:8024–35.


    Google Scholar
     

  • Gal Y, Ghahramani Z. Dropout as a Bayesian approximation: representing model uncertainty in deep learning. Proc 33rd Int Conf Mach Learn. 2016;48:1050–9.


    Google Scholar
     



  • Source link

    Related Posts

    My Chemical Romance release “Common People” cover

    June 15, 2026

    Five reasons you should use structure strips | Feature

    June 15, 2026

    Sunscreen myths debunked: Truth about chemical filters, white cast and SPF in foundation – Mamabella | Everyday Beautiful

    June 14, 2026
    Add A Comment
    Leave A Reply Cancel Reply

    Top Posts

    Chinese Wall may stem India tech flows for electronics and automobile

    June 1, 2026

    Abandoned malls, whispers of nuclear war and young foreigners detained. This is what’s REALLY going on in Dubai… and the chilling warning one taxi driver gave to the Mail’s IAN BIRRELL

    April 11, 2026

    Von der Leyen warned about China. Europe didn’t listen. Will it now?

    June 6, 2026
    Don't Miss

    Company behind UK leather retailer plunges into liquidation as high street stores close

    By IslaJune 16, 2026

    A company behind a UK leather retailer has plunged into liquidation as stores across the…

    Planning a UAE Trip? What Every U.S. Visitor Should Know

    June 16, 2026

    Cebgo to absorb AirSWIFT operations from early 3Q26

    June 16, 2026

    38-year-old Dubai resident dies while playing cricket; parents fly in for burial

    June 16, 2026
    SUBSCRIBE TO OUR NEWSLETTER

    Get our latest downloads and information first. Complete the form below to subscribe to our weekly newsletter.


    I consent to being contacted via telephone and/or email and I consent to my data being stored in accordance with European GDPR regulations and agree to the terms of use and privacy policy.

    Stay In Touch
    • Facebook
    • YouTube
    • TikTok
    • WhatsApp
    • Twitter
    • Instagram
    Top Trending

    Asia FX Talk – BOJ and RBA kick off central bank week

    By IslaJune 16, 2026

    EVE Health Group Initiates Pilot Study of Libbo Oral Soluble Film to Treat ED

    By IslaJune 16, 2026

    UAE offering Canadians major travel for trips to Dubai, Abu Dhabi | Daily Hive

    By IslaJune 16, 2026
    Most Popular

    100 robots at Hong Kong’s InnoEX reveal the state of humanoid robotics

    June 7, 2026

    Trump orders U.S. to attack Iran boats mining Strait of Hormuz

    April 23, 2026

    India’s first space tech unicorn emerges as Skyroot gears up for orbital launch

    May 7, 2026
    Our Picks

    Hwajing charters Adora cruise ship for Malaysia season: Travel Weekly Asia

    April 27, 2026

    EnPlusOne Adds Former Dicerna Executive Jim Weissman to Board as RNA Manufacturing Efforts Expand

    May 28, 2026

    Japan-China JV to Release Electric Minivehicle Next Year

    May 28, 2026
    SUBSCRIBE TO OUR NEWSLETTER

    Get our latest downloads and information first. Complete the form below to subscribe to our weekly newsletter.


    I consent to being contacted via telephone and/or email and I consent to my data being stored in accordance with European GDPR regulations and agree to the terms of use and privacy policy.

    © 2026 Simply Invest Asia.
    • Get In Touch
    • Cookie Policy
    • Privacy policy
    • Terms & Conditions

    Type above and press Enter to search. Press Esc to cancel.

    SUBSCRIBE TO OUR NEWSLETTER

    Get our latest downloads and information first.

    Complete the form below to subscribe to our weekly newsletter.


    I consent to being contacted via telephone and/or email and I consent to my data being stored in accordance with European GDPR regulations and agree to the terms of use and privacy policy.