论文标题
基准测试和不确定性量化的指标：质量，适用性以及化学机器学习最佳实践的途径
Metrics for Benchmarking and Uncertainty Quantification: Quality, Applicability, and a Path to Best Practices for Machine Learning in Chemistry
论文作者
论文摘要
当我们着手将机器学习在化学和材料领域（即数据衍生模型的验证和基准测试）以及对其做出的预测的不确定性量化时，这篇评论旨在引起人们对两个问题的关注。由于化学家通常只接受统计培训有限，因此他们经常被忽视或不足。除了帮助评估给定模型的质量，可靠性和适用性外，这些指标也是比较不同模型的性能的关键，从而为成功地应用机器学习在化学中成功应用化学方面的指南和最佳实践。
This review aims to draw attention to two issues of concern when we set out to make machine learning work in the chemical and materials domain, i.e., statistical loss function metrics for the validation and benchmarking of data-derived models, and the uncertainty quantification of predictions made by them. They are often overlooked or underappreciated topics as chemists typically only have limited training in statistics. Aside from helping to assess the quality, reliability, and applicability of a given model, these metrics are also key to comparing the performance of different models and thus for developing guidelines and best practices for the successful application of machine learning in chemistry.
