桥梁尺度:用于形状记忆聚合物的合理设计和建模的机器学习
1Southern University and A&M College, Department of Mechanical Engineering, Baton Rouge, LA 70813, USA.
Soft matter
|December 22, 2025
概括
机器学习 (ML) 加快了形状记忆聚合物 (SMP) 设计和属性预测. 本综述强调了ML应用在识别新的SMP和建模其行为方面,同时注意到当前的局限性和未来的方向.
科学领域:
- 材料科学 材料科学 材料科学
- 聚合物科学 聚合物科学
- 计算科学 计算科学
背景情况:
- 形状记忆聚合物 (SMP) 是具有广泛应用的刺激响应材料.
- 传统的SMP设计依赖于实证方法,限制了发现速度.
- 预测SMP行为往往需要复杂的理论力学.
研究的目的:
- 批判性地审查最近在将机器学习 (ML) 应用于SMPs方面的进展.
- 讨论 ML 辅助的 SMP 设计,化学识别和属性预测.
- 概述SMP研究中的ML的未来方向.
主要方法:
- 对SMP中ML应用的当前文献的综述.
- 讨论SMP化学识别的ML技术.
- 分析ML用于预测热力学和形状记忆特性.
主要成果:
- 在加速SMP发现和财产预测方面,ML显示出前景.
- 已使用各种ML工具来识别新的SMP化学物质.
- ML有助于预测SMP的热力学和形状记忆特性.
结论:
- 机器学习辅助的SMP发现和建模仍处于早期阶段.
- 目前的局限性包括不完整的结构表示和整合热/时间效应的挑战.
- 未来的工作应该专注于用于复杂的SMP拓和聚合物特定网络的高级ML工具.
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