使用可逆分子图生成模型设计的高温聚合物电介质
Di-Fan Liu1, Yong-Xin Zhang1, Wen-Zhuo Dong1
1State Key Laboratory of Power Systems, Department of Electrical Engineering, Tsinghua University, Beijing 100084, People's Republic of China.
Journal of chemical information and modeling
|December 7, 2023
概括
本研究引入了一个可逆图形生成模型,用于设计新型高温聚合物介电材料. 深度学习方法加速发现具有高玻璃过渡温度和宽带差距的材料,用于储能应用.
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:
- 计算材料设计在产生具有特定性质的分子方面面临着挑战.
- 数据驱动的材料信息学,利用深度学习,为加速材料发现提供了一个有前途的途径.
- 高温聚合物介电材料对于先进的储能应用至关重要.
研究的目的:
- 开发和应用一个可逆图生成模型来设计假设的高温聚合物介电.
- 利用深度生成模型,有效地发现具有所需性质的材料.
- 为了产生适用于高温储能膜电容的聚合物.
主要方法:
- 在25万个聚合物分子图的数据集上训练了一种可逆正常化的基于流量的分子图生成模型.
- 利用潜分布和分子图结构之间的可逆变换.
- 采用一次性生成过程,包括从潜空间取样,并通过两个可逆流产生分子图.
主要成果:
- 成功生成了具有所需属性的假设聚合物分子图.
- 该模型展示了准确的概率训练和高效的生成.
- 确定了具有高玻璃过渡温度 (Tg) 和宽带间隙 (Eg) 的潜在聚合物候选物.
结论:
- 可逆图形生成模型有效地促进了高温聚合物介电物的设计.
- 深度生成模型显示了加速发现用于储能先进材料的巨大潜力.
- 这项工作有助于功能性聚合物的高效计算设计.
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