对于反应性机器学习的潜在能量表面的异常值检测
Luis Itza Vazquez-Salazar1, Silvan Käser1, Markus Meuwly1
1Department of Chemistry, University of Basel, Basel, Switzerland.
概括
测试了不确定性量化 (UQ) 方法,以识别分子潜在能量表面的错误. 合并模型在异常值检测方面被证明是最有效的,其表现优于高斯混合模型和深度证据回归.
科学领域:
- 计算化学是一种计算化学.
- 化学物理 化学物理
- 机器学习在科学中的应用.
背景情况:
- 准确的潜在能量表面 (PES) 对于模拟化学反应至关重要.
- 在 PES 中识别不可靠的预测对于高效的模型改进至关重要.
- 不确定性量化 (UQ) 为评估预测信心提供了一个框架.
研究的目的:
- 评估不同的UQ方法来检测反应分子PES中的异常值.
- 为了比较UQ的合集,深度证据回归 (DER) 和高斯混合模型 (GMM) 的性能.
- 确定提高 PES 开发准确性和效率的策略.
主要方法:
- 应用三个UQ方法:合奏,DER和GMM.
- 测试syn-Criegee和乙烯基氧化之间的H转移反应.
- 基于预测不确定性的异常结果检测性能分析.
主要成果:
- 集成模型在检测异常值方面表现出最高的准确性 (前25个结构的~90%).
- GMM表现中等 (在前1000个结构中约为50%).
- 德尔的业绩受到其统计假设的显著限制.
- 确定了与平均误差相关的基于结构的指标.
结论:
- 基于集体的UQ是一种强大的方法,用于识别PES计算中的不可靠数据点.
- 在这种情况下,GMM为UQ提供了一个可行的替代方案.
- 识别的基于结构的指标可以加快对PES的神经网络模型的改进.
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