Sanggyu Chong1, Filippo Bigi1, Federico Grasselli1

  • 1Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland. michele.ceriotti@epfl.ch.

Faraday discussions
|September 25, 2024
PubMed
概括

预测刚性增强了对化学中的机器学习 (ML) 模型的理解. 这些指标评估了全球和本地ML模型的稳定性,提高了化学结构-属性相关性培训效率和可解释性.

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