模板设计复杂的区块共聚合物模式使用机器学习方法
Zhihan Liu1, Yi-Xin Liu1, Yuliang Yang1
1The State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai 200433, China.
ACS applied materials & interfaces
|June 19, 2023
概括
本研究介绍了一种机器学习方法,用于设计定向自组装 (DSA) 中的引导模板. 开发的神经网络模型在没有模拟的情况下准确地预测DSA模式的模板,达到97.1%的准确性.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 机器学习 机器学习
背景情况:
- 定向自组装 (DSA) 对于创建纳米尺度模式至关重要.
- 为DSA设计指导模板是一个复杂的逆向设计问题.
- 目前的方法通常依赖于计算上昂贵的前模拟.
研究的目的:
- 开发一种基于机器学习的方法,用于DSA指导模板的反向设计.
- 仅使用机器学习来预测DSA模板,消除了对前模拟的需求.
- 评估各种神经网络架构的性能和概括能力.
主要方法:
- 制定了反向设计问题作为一个多标签分类任务.
- 训练了各种神经网络 (NN) 模型,包括带有残余块的卷积神经网络 (CNN).
- 利用自相一致的场理论 (SCFT) 计算中的模拟图案样本进行训练.
- 应用了针对形态预测的增强技术,以提高NN的性能.
主要成果:
- 在模板预测的准确匹配准确度方面取得了显著的改进,从59.8% (基线) 增加到97.1% (最佳模型).
- 证明机器学习模型可以预测模板,而不需要进行前模拟.
- 性能最好的NN模型显示了人类设计的DSA模式的优秀概括能力.
结论:
- 机器学习,特别是深度神经网络,为DSA指导模板的反向设计提供了强大而高效的解决方案.
- 开发的方法通过消除对广泛模拟的需求,显著优于传统方法.
- 该研究强调了人工智能在加速材料设计和制造过程中的潜力.
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