# Automatically Building and Updating a Knowledge Graph of MLIP Models

arXiv: https://arxiv.org/abs/2610.09644 · HTML: https://mlsanity.com/paper/2610.09644/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 7 Oct 2026.

## Builds on (most cited first, 5 of 5)

- [A practical guide to machine learning interatomic potentials -- Status and future](https://mlsanity.com/paper/2503.09814/) (2025)
- [ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies](https://mlsanity.com/paper/1807.05351/) (2018)
- [Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model](https://mlsanity.com/paper/2404.03080/) (2024)
- [SHACL Constraints with Inference Rules](https://mlsanity.com/paper/1911.00598/) (2019)
- [The landscape of ontologies in materials science and engineering: A survey and evaluation](https://mlsanity.com/paper/2408.06034/) (2024)

## Abstract (arXiv)

Complementing the many efforts in providing semantic representations of concepts, notions, and entities in materials science, we report and illustrate a process by which we can automatically build a knowledge graph of the fast evolving field of machine learning applied to the prediction of material properties, focusing on MLIP (Machine Learning Interatomic Potential). This LLM-based process relies on multiple steps, from information extraction in documents and articles to a validation loop using SHACL constraints to detect and correct errors. It is carried out on a model-by-model basis, focusing on the consistency of representation, therefore enabling an iterative construction where the addition of new models is facilitated. We illustrate the process by showing a few interesting aspects that can be queried from a knowledge graph built from the models listed in the Matbench Discovery leaderboard.