# Error Assessment of Computational Models in Chemistry

## TL;DR

This paper discusses how to evaluate and quantify the uncertainty in computational chemistry models, emphasizing the importance of distinguishing systematic errors from random errors for better model assessment.

## Contribution

It introduces methods to assess and decompose uncertainties in computational chemistry models, improving the reliability of performance evaluation.

## Key findings

- Identifies sources of uncertainty in computational models
- Proposes methods to separate systematic and random errors
- Provides examples from quantum chemistry literature

## Abstract

Computational models in chemistry rely on a number of approximations. The effect of such approximations on observables derived from them is often unpredictable. Therefore, it is challenging to quantify the uncertainty of a computational result, which, however, is necessary to assess the suitability of a computational model. Common performance statistics such as the mean absolute error are prone to failure as they do not distinguish the explainable (systematic) part of the errors from their unexplainable (random) part. In this paper, we discuss problems and solutions for performance assessment of computational models based on several examples from the quantum chemistry literature. For this purpose, we elucidate the different sources of uncertainty, the elimination of systematic errors, and the combination of individual uncertainty components to the uncertainty of a prediction.