论文标题
计算化学家的预测不确定性验证
Prediction uncertainty validation for computational chemists
论文作者
论文摘要
预测不确定性（PU）的验证已成为现代计算化学的重要任务。旨在量化气象学预测的可靠性，校准框架（CS）框架现在被广泛用于优化和验证不确定性吸引机器学习（ML）方法。但是，它的应用不仅限于ML，并且可以作为任何PU验证的原则框架。本文旨在逐步介绍CS框架中PU验证的概念和技术，适合于计算化学的细节。根据本地校准统计，所提供的方法从基本图形检查到更复杂的方法。引入了紧密度的概念。这些方法在合成数据集上说明，并应用于从计算化学文献中提取的不确定性定量数据。
Validation of prediction uncertainty (PU) is becoming an essential task for modern computational chemistry. Designed to quantify the reliability of predictions in meteorology, the calibration-sharpness (CS) framework is now widely used to optimize and validate uncertainty-aware machine learning (ML) methods. However, its application is not limited to ML and it can serve as a principled framework for any PU validation. The present article is intended as a step-by-step introduction to the concepts and techniques of PU validation in the CS framework, adapted to the specifics of computational chemistry. The presented methods range from elementary graphical checks to more sophisticated ones based on local calibration statistics. The concept of tightness, is introduced. The methods are illustrated on synthetic datasets and applied to uncertainty quantification data extracted from the computational chemistry literature.
