4.8 Article

Machine-Learning-Assisted Design of Highly Tough Thermosetting Polymers

期刊

ACS APPLIED MATERIALS & INTERFACES
卷 14, 期 49, 页码 55004-55016

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acsami.2c14290

关键词

thermosetting polymers; modulus; tensile strength; toughness; machine learning; materials genome approach

资金

  1. National Natural Science Foundation of China
  2. Shanghai Scientific and Technological Innovation Projects
  3. Shanghai Aerospace Scientific and Technological Innovation Projects
  4. [22173030]
  5. [51833003]
  6. [21975073]
  7. [51621002]
  8. [22ZR1417500]
  9. [21511103102]
  10. [SAST2019-120]

向作者/读者索取更多资源

This paper proposes a machine-assisted materials genome (MGA) approach to design novel epoxy thermosets with excellent mechanical properties using machine learning models. A proof-of-concept experiment is conducted to verify the designed structures and gene substructures affecting mechanical properties are extracted, revealing the mechanisms of high-performance polymers.
Despite advances in machine learning for accurately predicting material properties, forecasting the performance of thermosetting polymers remains a challenge due to the sparsity of historical experimental data and their complicated crosslinked structures. We proposed a machine-learning-assisted materials genome approach (MGA) for rapidly designing novel epoxy thermosets with excellent mechanical properties (high tensile moduli, high tensile strength, and high toughness) through high-throughput screening in a vast chemical space. Machine-learning models were established by combining attention- and gate-augmented graph convolutional networks, multilayer perceptrons, classical gel theory, and transfer learning from small molecules to polymers. Proof-of-concept experiments were carried out, and the structures designed by the MGA were verified. Gene substructures affecting the modulus, strength, and toughness were also extracted, revealing the mechanisms of polymers with high mechanical properties. The developed strategy can be employed to design other thermosetting polymers efficiently.

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