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
- National Natural Science Foundation of China
- Shanghai Scientific and Technological Innovation Projects
- Shanghai Aerospace Scientific and Technological Innovation Projects
- [22173030]
- [51833003]
- [21975073]
- [51621002]
- [22ZR1417500]
- [21511103102]
- [SAST2019-120]
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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