多体物理学和机器学习使得有前途的太阳能材料的发现成为可能
Tathagata Biswas1, Adway Gupta1, Arunima K Singh1
1Department of Physics, Arizona State University Tempe Arizona 85281 USA arunimasingh@asu.edu.
RSC advances
|March 19, 2025
概括
我们开发了pyGWBSE用于高通量GW-BSE计算,使准确的机器学习模型能够预测准粒子和激发性质. 这加速了新型光吸收材料的发现.
科学领域:
- 计算材料科学科学 计算材料科学
- 量子化学 是一个量子化学.
- 固态物理 固态物理
背景情况:
- GW-BSE方法准确地描述了材料中的准粒子带结构和刺激效应.
- 高的计算成本限制了其在大规模材料发现中的应用.
- 加快材料发现需要高效的计算工具.
研究的目的:
- 开发一个Python工作流包,pyGWBSE,用于高通量GW-BSE模拟.
- 为各种材料创建准粒子和刺激性质数据库.
- 训练准确的机器学习模型来预测这些属性.
主要方法:
- 使用pyGWBSE包进行高通量GW-BSE计算.
- 编制了350多种材料的数据集,计算了QP和刺激性质.
- 开发了监督机器学习模型,用于预测频段间隙和激发结能.
主要成果:
- 在ML模型中实现了高精度:准粒子间隙的RMSE为0.36 eV,激子结合能为0.29 eV.
- 对于高/低激发性结合能量的材料,已证明90%的分类准确度.
- 确定了159个可见光和203个紫外线光的摄影吸收者候选者.
结论:
- pyGWBSE 能够对材料属性进行高效的高通量计算.
- 在pyGWBSE数据上训练的机器学习模型准确地预测了关键材料特性.
- 这种方法显著加速了功能性光吸收材料的发现.
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