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, that is, 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.

当我们着手在化学和材料领域进行机器学习时，本综述旨在引起人们对两个令人关注的问题的关注，即用于数据衍生模型的验证和基准测试的统计损失函数度量以及对数据衍生模型的不确定性量化。他们做出的预测。由于化学家通常只接受有限的统计学培训，因此它们通常被忽略或未被重视的话题。除了帮助评估给定模型的质量，可靠性和适用性之外，这些度量标准对于比较不同模型的性能，从而为成功地将机器学习应用于化学领域，开发指南和最佳实践也是至关重要的。