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预测ABN₃矿的带间隙:来自机器学习和第一原则DFT研究的解释

Swarup Ghosh¹, Joydeep Chowdhury¹

¹Department of Physics, Jadavpur University 188, Raja S.C. Mallick Road Kolkata 700032 India joydeep72_c@rediffmail.com joydeep.chowdhury@jadavpuruniversity.in.

|February 21, 2024

概括

机器学习模型准确地预测了无机化物矿的带间隙. 这项研究有助于发现光伏电池和光学设备的新材料.

科学领域:

材料科学材料科学材料科学
计算化学计算化学
固态物理固态物理

背景情况:

预测像带间隙这样的材料特性对于发现新的功能材料至关重要.
机器学习 (ML) 提供了一种强大的方法,通过预测结构和化学描述符的特性来加速材料的发现.

研究的目的:

开发和评估机器学习模型,用于预测无机化 Perowskites 的带间隙.
在这个预测任务中调查各种监督学习算法的性能.
探索 ML 预测的带隙在光电子应用中的潜力.

主要方法:

训练了四个监督机器学习模型 (MLP,GBDT,SVR,RFR),使用特征描述符和1563个化矿的实验频段间隙数据.
使用平均绝对误差,根-平均平方误差和R平方值验证模型准确性.
对两个选定的化物矿 (CeMoN3,CeWN3) 进行密度函数理论 (DFT) 计算 (PBE,HSE06,G0W0@PBE,G0W0@HSE06),以确定它们的电子带结构和光电子特性.

主要成果:

开发的ML模型在预测化 Perowskites的带间隙方面表现出很好的准确性.
双变位图证实了训练和测试数据集预测和输入频段差距之间的强烈相关性.
DFT计算为CeMoN3和CeWN3的电子带结构和光电子特性提供了洞察力,补充了ML预测.

结论:

机器学习模型是预测化 PeroVskites 的带间隙的有效工具.
这项研究促进了在材料科学中对ML的探索,以加速发现.
这些发现支持化 PeroVskites 在先进的光伏和光学发光器件的潜在应用.

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