Investigation of heat transfer in a semicrystalline polymer by combining molecular simulation and machine learning
Source abstract
Abstract
Heat transfer in a semicrystalline polymer is investigated by combining molecular simulation and machine learning and the molecular mechanism is discussed.
Only a short abstract or summary was supplied by the source.
Source: crossref · Quality: short
Research context
OpenAlex topics: Thermal properties of materials · Machine Learning in Materials Science · Block Copolymer Self-Assembly
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Key Laboratory of Beijing City on Preparation and Processing of Novel Polymer Materials, Beijing University of Chemical Technology, Beijing 10029, People's Republic of China
Beijing Key Laboratory of Wood Science and Engineering, Beijing Forestry University, Beijing 100083, People's Republic of China
Institute of Intelligent Innovation, Henan Academy of Sciences, Zhengzhou, 451162, People's Republic of China
Aerospace Research Institute of Materials and Processing Technology, Beijing, 100076, People's Republic of China
Funding
National Natural Science Foundation of China
References (65)
Show source-provided references
1. Chen · Prog. Polym. Sci. · 2016 · 59 · 4110.1016/j.progpolymsci.2016.03.001 ↗
2. Zhang · Nano-Micro Lett. · 2025 · 17 · 26310.1007/s40820-025-01796-z ↗
3. Han · Prog. Polym. Sci. · 2011 · 36 · 91410.1016/j.progpolymsci.2010.11.004 ↗
4. Xu · Adv. Mater. · 2018 · 30 · 170554410.1002/adma.201705544 ↗
5. Liu · ACS Appl. Mater. Interfaces · 2020 · 12 · 650310.1021/acsami.9b21467 ↗
6. Ding · Carbon · 2014 · 66 · 57610.1016/j.carbon.2013.09.041 ↗
7. Guo · Compos. Sci. Technol. · 2020 · 193 · 10813410.1016/j.compscitech.2020.108134 ↗
8. Gu · Nano-Micro Lett. · 2021 · 13 · 11010.1007/s40820-021-00640-4 ↗
9. Cahill · Phys. Rev. B:Condens. Matter Mater. Phys. · 1987 · 35 · 406710.1103/PhysRevB.35.4067 ↗
10. Michalski · Phys. Rev. B:Condens. Matter Mater. Phys. · 1992 · 45 · 705410.1103/PhysRevB.45.7054 ↗
11. Tonpheng · Phys. Chem. Chem. Phys. · 2011 · 13 · 1504710.1039/c1cp20785g ↗
12. Kim · Nat. Mater. · 2015 · 14 · 29510.1038/nmat4141 ↗
13. Singh · Nat. Nanotechnol. · 2014 · 9 · 38410.1038/nnano.2014.44 ↗
14. Zhang · J. Phys. Chem. C · 2014 · 118 · 2114810.1021/jp5051639 ↗
15. Huang · Mater. Sci. Eng., R · 2018 · 132 · 110.1016/j.mser.2018.06.002 ↗
16. Haggenmueller · Macromolecules · 2007 · 40 · 241710.1021/ma0615046 ↗
17. Shen · Nat. Nanotechnol. · 2010 · 5 · 25110.1038/nnano.2010.27 ↗
18. Ronca · Polymer · 2017 · 123 · 20310.1016/j.polymer.2017.07.027 ↗
19. Zhu · ACS Omega · 2017 · 2 · 393110.1021/acsomega.7b00563 ↗
20. Kim · Int. J. Mech. Sci. · 2024 · 282 · 10962210.1016/j.ijmecsci.2024.109622 ↗
21. Sæther · Macromolecules · 2021 · 54 · 656310.1021/acs.macromol.1c00633 ↗
22. Zhang · Chin. J. Polym. Sci. · 2020 · 38 · 141810.1007/s10118-020-2466-y ↗
23. Zhang · J. Phys. Chem. B · 2023 · 127 · 1024310.1021/acs.jpcb.3c06476 ↗
24. Maurya · ACS Macro Lett. · 2022 · 11 · 92510.1021/acsmacrolett.2c00341 ↗
25. Henry · Phys. Rev. Lett. · 2008 · 101 · 23550210.1103/PhysRevLett.101.235502 ↗
26. Liu · Phys. Rev. B:Condens. Matter Mater. Phys. · 2012 · 86 · 10430710.1103/PhysRevB.86.104307 ↗
27. Das · Macromolecules · 2023 · 56 · 39310.1021/acs.macromol.2c01954 ↗
28. Zhang · J. Appl. Phys. · 2012 · 112 · 09430410.1063/1.4759293 ↗
29. Zhang · ACS Nano · 2013 · 7 · 759210.1021/nn401714e ↗
30. Robbins · Appl. Phys. Lett. · 2015 · 107 · 20190810.1063/1.4936195 ↗
31. Liu · Phys. Rev. B:Condens. Matter Mater. Phys. · 2010 · 81 · 17412210.1103/PhysRevB.81.174122 ↗
32. Rossinsky · J. Chem. Phys. · 2009 · 130 · 13490510.1063/1.3103890 ↗
33. He · J. Appl. Phys. · 2021 · 130 · 22510110.1063/5.0067999 ↗
34. Ma · ACS Appl. Mater. Interfaces · 2022 · 14 · 1558710.1021/acsami.1c23610 ↗
35. Ma · Mater. Today Phys. · 2022 · 28 · 10085010.1016/j.mtphys.2022.100850 ↗
36. Huang · npj Comput. Mater. · 2023 · 9 · 19110.1038/s41524-023-01154-w ↗
37. Meyer · Macromolecules · 2002 · 35 · 124110.1021/ma011309l ↗
38. Luo · Phys. Rev. Lett. · 2009 · 102 · 14780110.1103/PhysRevLett.102.147801 ↗
39. Kikugawa · J. Appl. Phys. · 2013 · 114 · 03430210.1063/1.4813505 ↗
40. Torii · J. Chem. Phys. · 2008 · 128 · 04450410.1063/1.2821963 ↗
41. Plimpton · J. Comput. Phys. · 1995 · 117 · 110.1006/jcph.1995.1039 ↗
42. Jabbari-Farouji · ACS Macro Lett. · 2015 · 4 · 14710.1021/mz500754b ↗
43. Yu · Macromolecules · 2008 · 41 · 673310.1021/ma800172t ↗
44. Park · Sci. Rep. · 2019 · 9 · 302610.1038/s41598-019-39825-8 ↗
45. Xie · Compos. Sci. Technol. · 2013 · 85 · 9810.1016/j.compscitech.2013.06.010 ↗
46. Kim · Polymer · 2017 · 129 · 20110.1016/j.polymer.2017.09.055 ↗
47. Wang · J. Phys. Chem. C · 2015 · 119 · 1273110.1021/acs.jpcc.5b02920 ↗
48. Zhang · Langmuir · 2021 · 37 · 1203810.1021/acs.langmuir.1c01697 ↗
49. Wang · Sci. Rep. · 2015 · 5 · 1812210.1038/srep18122 ↗
50. Klemens · Int. J. Thermophys. · 2001 · 22 · 26510.1023/A:1006776107140 ↗
51. Wang · J. Appl. Phys. · 2015 · 117 · 08431710.1063/1.4913600 ↗
52. Holland · Phys. Rev. · 1964 · 134 · A47110.1103/PhysRev.134.A471 ↗
53. Zhao · J. Phys. Chem. C · 2016 · 120 · 2767510.1021/acs.jpcc.6b09706 ↗
54. Kim · J. Mater. Chem. C · 2015 · 3 · 1033610.1039/C5TC01670C ↗
55. Xie · Phys. Rev. B · 2017 · 95 · 03540610.1103/PhysRevB.95.035406 ↗
56. Tomko · Nat. Nanotechnol. · 2018 · 13 · 95910.1038/s41565-018-0227-7 ↗
57. Yu · Polymer · 2014 · 55 · 19510.1016/j.polymer.2013.12.001 ↗
58. G.James , D.Witten , T.Hastie , R.Tibshirani and J.Taylor , Linear Regression , in An Introduction to Statistical Learning: with Applications in Python , Springer International Publishing , Cham , 2023 , pp. 69–13410.1007/978-3-031-38747-0_3 ↗
59. R. M.Heiberger and E.Neuwirth , Polynomial Regression , in R through Excel: A Spreadsheet Interface for Statistics, Data Analysis, and Graphics , Springer , New York, NY , 2009 , pp. 269–28410.1007/978-1-4419-0052-4_11 ↗
60. Tian · Mach. Learn.: Sci. Technol. · 2021 · 2 · 035015
61. T.Chen and C.Guestrin , Xgboost: A Scalable Tree Boosting System , in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , Association for Computing Machinery , San Francisco, California, USA , 2016 , pp. 785–79410.1145/2939672.2939785 ↗
62. M.Injadat , F.Salo , A. B.Nassif , A.Essex and A.Shami , Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection , in 2018 IEEE Global Communications Conference (GLOBECOM) , 9-13 Dec 2018 , pp. 1–6
63. Wang · ACM Comput. Surv. · 2023 · 55 · 287
64. Yang · Atmos. Environ. · 2024 · 338 · 12079710.1016/j.atmosenv.2024.120797 ↗
65. Lundberg · Adv. Neural Inf. Process. Syst. · 2017 · 30