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Updated: May 21, 2025

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X2-PEC:基于原子对能量校正的神经网络模型

Minghong Jiang1, Zhanfeng Wang1, Yicheng Chen1

  • 1Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, MOE Key Laboratory of Computational Physical Sciences, Department of Chemistry, Fudan University, Shanghai, China.

Journal of computational chemistry
|March 18, 2025
PubMed
概括

新的X2-PEC深度学习方法提高了低密度函数理论 (DFT) 计算的高准确性. 这种人工神经网络 (ANN) 方法改善了对分子性质的预测,例如原子化能量和形成的度.

关键词:
B3LYPYP 在线播放BLYP BLYP 在线播放X1 X1 X1 X1 X1 X1 X1 X1 X1 X1 X1 X1 X1 X1 X1 X1 X1 X1 X1 X1XYGJ-OSOS 的时间.密度函数理论密度函数理论形成的凝聚力形成的凝聚力.神经网络的神经网络的神经网络

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