范德瓦尔斯异构结构的反向设计的深度生成模型
Shikun Gao1,2,3, Qinyuan Huang4,5, Chen Huang3,6
1School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin, 644000, China.
Scientific reports
|July 2, 2025
概括
本研究介绍了ConditionCDVAE+,一种用于设计范德瓦尔斯异构结构的生成模型. 它改善了晶体重建,并加速了用于光电子设备的新材料的发现.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 人工智能的人工智能
背景情况:
- 设计范德瓦尔斯 (vdW) 异构结构的传统实验方法通常是低效的,依赖于试错.
- 现有的计算模型很难有效地纳入反向设计的目标属性约束.
研究的目的:
- 开发一个深度生成模型,ConditionCDVAE+,用于具有所需属性的vdW异构结构的反向设计.
- 提高VDW异构生成和材料发现的质量和效率.
主要方法:
- 使用SE(3) -等价图神经网络EquiformerV2作为晶体扩散变异自编码器 (CDVAE) 框架中的编码解码器.
- 集成低级多模式融合和生成对抗网络,将属性和结构映射到一个共同的潜空间中.
- 开发了一个专门针对VDW异构结构的新型生成模型,在Janus III-VI vdW异构结构上进行验证.
主要成果:
- 条件CDVAE+实现了晶体重建的最佳根平均平方误差,并证明了更好的生成质量.
- 密度函数理论的计算证实,99.51%的生成样本汇聚到能量最小值,这表明了优越的基态收.
- 在有条件指导下模型的有效性得到了广泛的验证.
结论:
- 条件CDVAE+为vdW异构结构的目标导向设计提供了一个有效的框架.
- 这种方法显着有望通过快速发现材料来加速新型光电子设备的开发.
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