用机器学习控制Janus 2D批量异构结构稳定性的基本因素
Rachel Gorelik1, Tara M Boland2, Arunima K Singh3
1School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, Arizona 85281, United States.
ACS applied materials & interfaces
|May 16, 2025
概括
本研究使用模拟和机器学习 (ML) 探索了1000多个二维Janus和散装材料异构结构. 散装材料的特性显著影响异构结构的稳定性和界面结构,加速材料的发现.
科学领域:
- 材料科学 材料科学 材料科学
- 凝聚物质物理学 凝聚物质物理学
- 计算化学计算化学
背景情况:
- 超过6000个2D材料为纳米电子,传感和能量转换中的功能异构结构提供了巨大的潜力.
- 了解二维材料批量异构结构中的结构-属性关系对于推进这些应用至关重要.
研究的目的:
- 通过初始模拟和机器学习 (ML) 来研究近1000个二维Janus和散装材料异构结构.
- 在这些异构结构中的复杂接口上推断结构-属性关系.
- 开发用于结合能量和界面分离的预测ML模型.
主要方法:
- 在51个Janus二维材料和19个散装材料上进行了高通量范德瓦尔斯纠正密度函数理论 (DFT) 模拟.
- 分析了 1147 个得到的异构结构的热力学稳定性,确定了 828 个稳定配置.
- 机器学习模型被训练在计算数据上,以预测异构结构属性.
主要成果:
- 在1147个模拟的Janus 2D批量异构结构中,有828个被发现是热力学稳定的.
- ML模型准确地预测了结合能 (RMSE:0.05 eV/原子) 和z分离 (RMSE:0.14 Å).
- 散装材料的特性被确定为影响异构结构能量和界面结构的主导因素.
结论:
- 这项研究提供了对2D散装异构结构的基本见解,证实了散装材料性质的重要作用.
- 开发的预测性ML模型可以加速新型异构结构的发现和应用.
- 2D-Bulk异构结构数据库 (aiHD) 中的自由可用的数据支持在电子,量子计算,传感和能源领域的进一步研究.
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