下一代超导体的反向设计使用数据驱动的深度生成模型.
Daniel Wines1, Tian Xie2, Kamal Choudhary1,3
1Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States.
The journal of physical chemistry letters
|July 18, 2023
概括
研究人员开发了一种新的AI扩散模型,以发现新的超导体. 这种方法加速了具有高临界温度的材料的识别,降低了计算和实验成本.
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:
- 发现具有高临界温度 (Tc) 的新超导体至关重要,但受到高计算和实验成本的阻碍.
- 传统的材料选方法往往类似于漏斗,限制了发现的范围.
- 需要先进的计算工具来加速搜索下一代超导材料.
研究的目的:
- 提出一种新的扩散模型,用于产生具有独特结构和化学组成的新型超导体.
- 利用人工智能和现有的数据库来有效地发现材料.
- 为了实现具有所需超导性能的材料的反向设计.
主要方法:
- 使用晶体扩散变异自编码器 (CDVAE) 和原子线图神经网络 (ALIGNN) 预训练模型.
- 从密度函数理论 (DFT) 计算中对大约1000种超导材料的数据集进行了扩散模型的训练.
- 采用了各种集成模拟 (JARVIS) 联合自动化存储库的超导数据库.
主要成果:
- 使用扩散模型生成了3000个新的潜在超导体结构.
- 用预训练的ALIGNN模型选生成的结构,确定了61个有前途的候选人.
- 通过密度函数理论 (DFT) 计算验证了顶级候选人,证明了高的成功率.
结论:
- 开发的扩散模型有效地产生了具有高成功率的新型超导体候选者.
- 这种由人工智能驱动的方法为传统材料选提供了一个强大的替代方案,使逆向设计成为可能.
- 该方法大大降低了与发现高Tc超导体相关的成本.
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