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Updated: Jun 16, 2025

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对分子性质预测的知识蒸:一个可扩展性分析.

Rahul Sheshanarayana1, Fengqi You1,2,3,4

  • 1College of Engineering, Cornell University, Ithaca, NY, 14853, USA.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|April 9, 2025
PubMed
概括

知识蒸 (KD) 有效地压缩了用于分子性质预测的复杂模型. 这种技术提高了效率和准确性,使得较小的模型能够在化学信息学和材料科学中实现卓越的性能.

关键词:
图形神经网络的神经网络知识的蒸知识的蒸.材料信息学 材料信息学可扩展性可扩展性.

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