机器学习预测铁的实验过渡温度 (II) 旋转交叉复合体
Vyshnavi Vennelakanti1,2, Irem B Kilic1, Gianmarco G Terrones1
1Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
The journal of physical chemistry. A
|December 27, 2023
概括
机器学习模型准确地预测了旋转交叉 (SCO) 过渡温度,优于密度函数近似. 这一进步有助于设计用于分子电子的SCO材料,因为它可以更好地预测它们的温度依赖行为.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 机器学习应用 机器学习应用
背景情况:
- 旋转交叉 (SCO) 复合体在旋转状态之间进行过渡,在分子电子学中提供了潜力.
- 为室温附近的特定过渡温度设计SCO配体是具有挑战性的,因为复杂的热和热因素.
- 准确预测SCO过渡温度对于材料设计和应用至关重要.
研究的目的:
- 开发和评估用于预测SCO过渡温度的机器学习 (ML) 模型 (T1/2).
- 将ML模型的预测精度与已建立的密度函数近似 (DFAs) 进行比较.
- 通过特征选择确定影响SCO过渡温度的关键分子特征.
主要方法:
- 使用基于图形的修订自相关性作为在SCO-95数据集上训练的ML模型的特征.
- 采用随机森林排名的递归特征添加 (RF-RFA) 来进行特征选择,以提高模型的可转移性.
- 将ML预测与实验和估计的T1/2值,以及与DFA预测进行比较.
主要成果:
- ML模型,特别是具有完整和选定的特征的随机森林 (RF),与实验T1/2值有中等的相关性.
- 与表现最好的DFAs相比,ML模型在预测T1/2方面表现出更高的准确性.
- 射频-RFA射频模型实现了强烈的相关性 (皮尔森的r=0.82) 与测试组的估计T1/2值,显著优于DFAs.
结论:
- 机器学习模型为预测SCO过渡温度提供了一个有希望的,更准确的DFA替代方案.
- 该研究强调了ML在加速新型SCO材料的设计和发现方面的潜力.
- 预计随着更大的SCO复杂数据集的策划,ML模型性能将进一步改善.
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