Prediction uncertainty validation for computational chemists.

Area of Science:

• Computational Chemistry
• Machine Learning
• Scientific Computing

Background:

• Validation of prediction uncertainty (PU) is crucial for modern computational chemistry.
• The calibration-sharpness (CS) framework, originally from meteorology, is increasingly used for validating uncertainty-aware machine learning (ML) methods.
• The CS framework offers a principled approach for any PU validation, extending beyond ML applications.

Purpose of the Study:

• To provide a step-by-step introduction to PU validation using the CS framework, tailored for computational chemistry.
• To present a range of methods, from basic graphical checks to advanced local calibration statistics.
• To introduce the concept of tightness within the CS framework.

Main Methods:

• Adaptation of the calibration-sharpness (CS) framework for computational chemistry.
• Implementation of elementary graphical checks for PU validation.
• Application of sophisticated local calibration statistics and the concept of tightness.

Main Results:

• The study illustrates CS framework methods on synthetic datasets.
• Methods are applied to real-world uncertainty quantification data from computational chemistry literature.
• Demonstrates the applicability and utility of the CS framework for computational chemistry PU validation.

Conclusions:

• The CS framework provides a robust and versatile tool for validating prediction uncertainty in computational chemistry.
• The presented methods enable rigorous assessment of prediction reliability, enhancing trust in computational results.
• This work facilitates the adoption of advanced PU validation techniques in the computational chemistry community.