# Ontology-based knowledge graph infrastructure for interoperable atomistic simulation data

arXiv: https://arxiv.org/abs/2604.06230 · HTML: https://mlsanity.com/paper/2604.06230/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 63 references that are in the map.
- First posted on arXiv 31 Mar 2026.

## Builds on (most cited first, 8 of 8)

- [Large Language Models for Generative Information Extraction: A Survey](https://mlsanity.com/paper/2312.17617/) (2023)
- [Accelerating Scientific Discovery with Generative Knowledge Extraction, Graph-Based Representation, and Multimodal Intelligent Graph Reasoning](https://mlsanity.com/paper/2403.11996/) (2024)
- [AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance](https://mlsanity.com/paper/2003.12476/) (2020)
- [An Ontology for the Materials Design Domain](https://mlsanity.com/paper/2006.07712/) (2020)
- [A semantic approach to mapping the Provenance Ontology to Basic Formal Ontology](https://mlsanity.com/paper/2408.03866/) (2024)
- [Metadata practices for simulation workflows](https://mlsanity.com/paper/2408.17309/) (2024)
- [Semantic interoperability and characterization of data provenance in computational molecular engineering](https://mlsanity.com/paper/1908.02335/) (2019)
- [A Python workflow definition for computational materials design](https://mlsanity.com/paper/2505.20366/) (2025)

## Built on by (most cited first, 1 of 1)

- [An Ontology for Machine Learning Interatomic Potentials](https://mlsanity.com/paper/2607.23219/) (2026)

## Abstract (arXiv)

The reuse of atomistic simulation data is often limited by heterogeneous formats, incomplete metadata, and a lack of standardized representations of workflows and provenance. Here we present an ontology-based infrastructure for representing and integrating atomistic simulation data as a knowledge graph. The approach combines domain ontologies with a software framework that enables data capture both from existing datasets and directly from simulation workflows at the point of generation. Heterogeneous data from multiple sources are normalized into a common, ontology-aligned representation, enabling consistent querying and analysis across datasets. We demonstrate these capabilities through the integration of grain boundary data, cross-dataset analysis of material properties, and extraction of derived thermodynamic quantities from existing simulations. In addition, workflows are represented in a machine-readable form, enabling both forward provenance tracking and partial reconstruction of computational procedures. The resulting knowledge graph contains over 750,000 triples describing nearly 8,000 computational samples. This work provides a practical framework for improving the findability, interoperability, and reuse of atomistic simulation data.