关于"通过解决小数电子问题来推进密度函数的边界"的评论
James Kirkpatrick1, Brendan McMorrow1, David H P Turban1
1DeepMind, 6 Pancras Square, London N1C 4AG, UK.
概括
这项研究针对DM21模型的说法
科学领域:
- 计算化学
- 量子力学
- 科学中的机器学习
背景情况:
- 对于DM21模型的适用性而言,其超出训练数据的概括能力至关重要.
- 格拉西莫夫等人 在我们之前的工作中对DM21的微分电荷 (FC) 和微分旋转 (FS) 能力的证明提出了质疑.
- 他们的批评集中在我们的训练集和基准数据集之间的重叠,以及我们概括示例的有效性.
研究的目的:
- 为了反驳格拉西莫夫等人所提出的说法. 关于DM21模型的概括能力.
- 澄清我们的基准结果和概括示例的准确性和相关性.
- 重新确认DM21模型的整体质量和预测能力.
主要方法:
- 培训套件的重新评估与破约基准 (BBB) 相重叠.
- 在论文中介绍的概括例的详细分析.
- 直接反驳格拉西莫夫等人提出的具体问题.
主要成果:
- 训练组和BBB之间所说的~50%的重叠是不准确的.
- 一般化示例表明DM21在训练场外的强大表现.
- 格拉西莫夫和其他人的批评. 是基于有缺陷的前提,与核心调查结果无关.
结论:
- 格拉西莫夫和其他人的说法. 没有证据表明DM21缺乏FC/FS泛化.
- 我们的论文为DM21的概括能力提供了有效的证据.
- DM21模型保持了量子化学计算的高质量和预测准确性.
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