量子化学数据生成作为填充,以提高机器学习反应和逆合成规划的可靠性
Alessandra Toniato1,2,3,4, Jan P Unsleber1,2, Alain C Vaucher3,4
1Laboratory of Physical Chemistry, ETH Zurich Vladimir-Prelog-Weg 2 8093 Zurich Switzerland markus.reiher@phys.chem.ethz.ch.
概括
人工智能增强化学合成计划使用实验数据. 第一原则计算可以提供缺失的数据来改善AI预测,证明自主数据生成的可行方法.
科学领域:
- 计算化学是一种计算化学.
- 化学领域的人工智能
- 化学合成规划 化学合成规划
背景情况:
- 数据驱动的AI模型通过利用大型实验反应数据库,在化学合成规划方面取得了成功.
- 这些人工智能模型的性能受到现有实验数据的可用性和完整性的限制.
- 对反应级联的AI预测的不确定性可能来自缺失的数据,阻碍可靠的合成设计.
研究的目的:
- 证明使用自主第一原则计算来生成AI驱动合成计划中缺失的数据的可行性.
- 调查与按需第一原则计算相关的资源需求.
- 增强人工智能预测的信心,并通过补充计算数据实现模型再培训.
主要方法:
- 实现自主第一原则计算,以生成缺失的实验数据.
- 将计算衍生数据集成到AI模型中,用于合成预测.
- 分析计算资源需求以进行按需计算.
主要成果:
- 证明了使用第一原则计算自主生成缺失数据的可行性.
- 量化了进行这些按需计算的资源需求.
- 展示了提高人工智能模型准确性和可靠性的潜力.
结论:
- 自主第一原则计算提供了一种可行的解决方案,可以在人工智能驱动的合成规划中增加有限的实验数据.
- 这种方法提高了预测的信心,并使模型重新训练成为可能,解决了当前数据驱动方法的关键局限性.
- 了解资源需求对于这种混合计算-实验策略的实际实施至关重要.
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