为过渡金属复合体的最佳调范围隔离混合功能器的机器学习参数.
Xiang-Yang Liu1, Qing-Xin Xiang2, Sheng-Rui Wang2
1College of Chemistry and Material Science, Sichuan Normal University, Chengdu 610068, China.
The journal of physical chemistry letters
|September 8, 2025
概括
本研究引入了一种机器学习 (ML) 模型,用于预测过渡金属复合体 (TMC) 中范围分离的混合函数的最佳参数. 这种方法显著降低了计算成本,同时保持了电子结构预测的准确性.
科学领域:
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 最佳调整的距离分离混合 (OT-RSH) 功能为电子结构计算提供了高精度.
- 确定这些函数的最佳参数可能是计算上昂贵的,特别是对于像过渡金属复合体 (TMC) 这样的大型系统.
- 开发有效的方法来预测这些参数对于加速材料发现至关重要.
研究的目的:
- 开发一种机器学习 (ML) 方法,用于预测TMC中最佳范围分离参数.
- 为了降低使用OT-RSH函数的电子结构计算的计算成本.
- 为了使光电子应用中TMC的高通量选能够准确和高效.
主要方法:
- 从tmQM数据库中编制了4380个TMC的数据集.
- 每个TMC是由一个62,087维的多个指纹特征 (MFF) 矢量表示的.
- 训练支持向量机 (SVM) 回归模型以预测最佳范围分离参数.
主要成果:
- 开发的机器学习功能,ML-LC-PBE0*,预测了与完全调整的计算可比的电离潜力.
- 与传统方法相比,这种ML方法显著降低了计算成本.
- 与DLPNO-CCSD (T) 参考值相比,ML-LC-PBE0*在传统的全局和固定参数RSH功能上显示出更高的性能.
结论:
- 机器学习方法为TMCs的RSH函数中预测最佳参数提供了一个有效和准确的框架.
- 这种方法有助于高通量选和电子结构预测.
- 这些发现为发现基于TMC的新型光电子材料铺平了道路.
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