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Metrics for Benchmarking and Uncertainty Quantification: Quality, Applicability, and a Path to 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, i. e., statistical loss function metrics for the validation and benchmarking of data-derived models, and the uncertainty quantification of predictions made by them.
Advances of Machine Learning in Molecular Modeling and Simulation
In this review, we highlight recent developments in the application of machine learning for molecular modeling and simulation.
Data Analysis, Statistics and Probability Computational Physics
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