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# Prediction of grain boundary energy and understanding of grain boundary structure-energy relationship by machine learning

## Details

**Authors** Lu et al.

**Year** 2026

**Publisher** Acta Mechanica Sinica

**Discipline** Chemistry

[Read it at the publisher](https://doi.org/10.1007/s10409-026-51015-x) 
10.1007/s10409-026-51015-x

## In authors' words

### What they found (results)

Machine learning models trained on atomistic simulations of about 100,000 aluminum grain boundaries predicted grain-boundary energy 10.6-12.9% more accurately than established theoretical models based on the boundary's structural parameters.

## Commentary

### In short

The paper treats the grain boundary itself as the fundamental distinction-line separating two crystal orientation domains, and shows that the geometry of that distinction alone determines a measurable physical property.

**Patterns it shows** D

**Added** 2026-08-01

**How to cite this** Lu et al. (2026). Prediction of grain boundary energy and understanding of grain boundary structure-energy relationship by machine learning. Acta Mechanica Sinica.