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Updated: Jan 8, 2026

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增强采样,以有效地学习粗粒度机器学习潜力

Weilong Chen1, Franz Görlich1, Paul Fuchs1

  • 1Professorship of Multiscale Modeling of Fluid Materials, Department of Engineering Physics and Computation, TUM School of Engineering and Design, Technical University of Munich, Munich 80333, Germany.

Journal of chemical theory and computation
|December 24, 2025
PubMed
概括

增强采样通过改进用于粗粒度机器学习潜力 (MLP) 的数据生成来加速分子动力学 (MD) 模拟. 这种方法克服了传统力量匹配的局限性,导致更准确,更可靠的CG-MLPs.

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Hebbian LTP
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