使用机器学习来预测有机-无机混合矿的热力学稳定性
Guocai Lv1, Jie Cheng2, Changxin Wang3
1Basic Experimental Center of Natural Science, University of Science and Technology Beijing, Beijing 100083, People's Republic of China.
Journal of physics. Condensed matter : an Institute of Physics journal
|September 15, 2025
概括
预测混合有机-无机矿 (HOIP) 的稳定性对于太阳能至关重要. 机器学习模型准确地识别了稳定的HOIP,加速了对高效太阳能电池的发现.
科学领域:
- 材料科学 材料科学 材料科学
- 可再生能源可再生能源是可再生能源.
- 计算化学计算化学
背景情况:
- 混合有机-无机矿 (HOIP) 在太阳能应用中显示出高效率.
- 结构的不稳定性阻碍了HOIPs的开发和应用.
- 准确预测HOIP的稳定性对于推进太阳能技术至关重要.
研究的目的:
- 开发机器学习模型来预测HOIPs的热力学稳定性.
- 为了确定控制HOIP稳定的关键特征.
- 为了加速发现太阳能应用的稳定矿材料.
主要方法:
- 使用了1346个HOIP样本的数据集.
- 采用递归特征消除 (RFE) 和逐步方法进行特征选择.
- 开发了一种梯度增强回归模型,实现了0.993.3的R2得分.
主要成果:
- 确定了影响稳定性的关键特征:平均组号 (Ng),平均阳离子半径 (ri),B位格子常数c (cB),X位原子轨道的最低能量 (EX).
- 在1584个选中预测了106个潜在的稳定矿候选物.
- 通过密度函数理论 (DFT) 和初始分子动力学证实了26个稳定候选物.
结论:
- 开发的机器学习工作流程有效地选稳定的HOIP.
- 这种方法比传统的计算方法提高了15倍.
- 为快速识别有前途的矿材料用于太阳能提供了宝贵的指导.
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