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通过机器学习增强分子图形与几何形状通过原子间潜力

Cong Fu, Yuchao Lin, Zachary Krueger

    ArXiv
    |September 29, 2025
    PubMed
    概括

    机器学习原子间潜力 (MLIP) 模型可以生成精确的3D分子几何形状,减少对密度函数理论 (DFT) 等昂贵的计算方法的依赖. 这些在大型数据集上训练的MLIP模型改善了分子性质预测.

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