深度思维21的功能性并没有推断到过渡金属化学
Heng Zhao1, Tim Gould2, Stefan Vuckovic1
1Department of Chemistry, University of Fribourg, Fribourg, Switzerland. stefan.vuckovic@unifr.ch.
Physical chemistry chemical physics : PCCP
|April 10, 2024
概括
像DM21这样的机器学习功能表现有希望,但难以推断到过渡金属化学. 建议制定策略,以提高它们对这些复杂分子的性能.
科学领域:
- 计算化学是一种计算化学.
- 量子化学是一种量子化学.
- 材料科学是一种材料科学.
背景情况:
- 密度函数近似 (DFAs) 对于预测分子性质至关重要.
- 机器学习函数 (MLF) 提供了与传统DFA相比的潜在改进.
- 将MLF推断到新的化学系统,如过渡金属化学,仍然是一个挑战.
研究的目的:
- 评估深度思维21 (DM21) 机器学习的功能性到过渡金属化学 (TMC) 的推断能力.
- 将DM21的性能与TMC的标准功能 (B3LYP) 进行比较.
- 确定TMC中的MLF的局限性,并提出解决方案.
主要方法:
- 使用已建立的基准来评估TMC的DM21准确性.
- 在TMC分子上分析DM21的自一致场 (SCF) 趋同.
- 在主组和TMC化学空间之间比较特征表示.
主要成果:
- 对于TMC,DM21的准确性与B3LYP相当或高于B3LYP.
- DM21始终遇到SCF与TMC分子的融合问题.
- 分析显示,主要组和TMC之间的DM21特征存在差异.
结论:
- 虽然DM21在TMC准确性方面表现良好,但其融合问题限制了实际应用.
- 了解特征差异是改善MLF推断的关键.
- 需要进一步开发,以提高MLF的稳定性,用于过渡金属化学.
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