Metrics for Benchmarking and Uncertainty Quantification: Quality, Applicability, and Best Practices for Machine Learning in Chemistry

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.

Automated summary

This review highlights two important issues to consider when applying machine learning in the chemical and materials domain: statistical loss function metrics for model validation and benchmarking, and uncertainty quantification of predictions. These topics are often overlooked by chemists, but are crucial for comparing model performance and developing successful machine learning applications in chemistry.