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Updated: Sep 16, 2025

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一个可扩展的机器学习多局部回归框架,用于在各种化学环境中适应潜在能量表面

Kai-Le Jiang1, Huai-Qian Wang1,2, Hui-Fang Li2

  • 1College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China.

The Journal of chemical physics
|July 8, 2025
PubMed
概括

本研究介绍了CLRNet,这是一种用于准确构建潜在能量表面 (PES) 的新型机器学习框架. 它有效地建模了高能结构,并以化学反应的预测精度平衡了计算能力.

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