开发高效的深度学习模型,用于预测共聚合物特性
Himanshu1, Kaushik Chakraborty1, Tarak K Patra1
1Department of Chemical Engineering and Center for Atomistic Modeling and Materials Design, Indian Institute of Technology Madras, Chennai, TN 600036, India. tpatra@iitm.ac.in.
Physical chemistry chemical physics : PCCP
|September 15, 2023
概括
深度学习模型可以预测聚合物特性,但需要最佳的架构和足够的数据. 本研究介绍了使用最小数据和高参数进行高效的深度学习模型开发的方法,用于聚合物科学.
科学领域:
- 聚合物科学 聚合物科学
- 材料科学 材料科学 材料科学
- 计算化学计算化学
背景情况:
- 深度学习模型显示出预测聚合物性质的前景.
- 模型性能在很大程度上取决于网络拓和训练数据量.
- 缺乏标准化协议的架构选择和有限的同质聚合物数据阻碍了发展.
研究的目的:
- 解决深度学习模型开发中的瓶问题.
- 提出有效的模型创建策略,使用最小的数据和超参数.
- 评估拓和数据量对模型性能的影响.
主要方法:
- 线性层次扩展以确定最佳的神经网络拓.
- 特性提取用于将离散的聚合物序列映射到连续的潜空间.
- 实现用于预测共聚物旋转半径和共聚物兼容器特性.
主要成果:
- 展示了一种系统识别合适深度学习架构的方法.
- 开发了一种技术,以减少训练聚合物模型的数据要求.
- 成功地应用了方法来预测特定的序列定义的聚合物特性.
结论:
- 用最小的数据和超参数可以构建高效的深度学习模型来预测聚合物属性.
- 拟议的策略克服了当前对聚合物深度学习应用的关键局限性.
- 这项工作促进了聚合物科学中预测模型的快速发展.
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