First-principles energetics of water: a many-body analysis

TL;DR

This paper analyzes the limitations of density-functional theory in modeling water by decomposing the total energy into many-body components and demonstrates how machine learning can aid in understanding these errors.

Contribution

It introduces a many-body energy decomposition approach combined with machine learning to evaluate DFT inaccuracies in water systems.

Findings

2B and B2B errors significantly affect water energy predictions

Machine learning enables detailed analysis of water energetics

Both 2B and B2B errors are crucial for accurate water modeling

Abstract

Standard forms of density-functional theory (DFT) have good predictive power for many materials, but are not yet fully satisfactory for solid, liquid and cluster forms of water. We use a many-body separation of the total energy into its 1-body, 2-body (2B) and beyond-2-body (B2B) components to analyze the deficiencies of two popular DFT approximations. We show how machine-learning methods make this analysis possible for ice structures as well as for water clusters. We find that the crucial energy balance between compact and extended geometries can be distorted by 2B and B2B errors, and that both types of first-principles error are important.