# Towards knowledge-based workflows: a semantic approach to atomistic simulations for mechanical and thermodynamic properties

arXiv: https://arxiv.org/abs/2602.01358 · HTML: https://mlsanity.com/paper/2602.01358/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 68 references that are in the map.
- First posted on arXiv 1 Feb 2026.

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

- [AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance](https://mlsanity.com/paper/2003.12476/) (2020)
- [34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery](https://mlsanity.com/paper/2505.03049/) (2025)
- [Methods Included: Standardizing Computational Reuse and Portability with the Common Workflow Language](https://mlsanity.com/paper/2105.07028/) (2021)
- [A semantic approach to mapping the Provenance Ontology to Basic Formal Ontology](https://mlsanity.com/paper/2408.03866/) (2024)
- [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)

Mechanical and thermodynamic properties, including the influence of crystal defects, are critical for evaluating materials in engineering applications. Molecular dynamics simulations provide valuable insight into these mechanisms at the atomic scale. However, current practice often relies on fragmented scripts with inconsistent metadata and limited provenance, which hinders reproducibility, interoperability, and reuse. FAIR data principles and workflow-based approaches offer a path to address these limitations. We present reusable atomistic workflows that incorporate metadata annotation aligned with application ontologies, enabling automatic provenance capture and FAIR-compliant data outputs. The workflows cover key mechanical and thermodynamic quantities, including equation of state, elastic tensors, mechanical loading, thermal properties, defect formation energies, and nanoindentation. We demonstrate validation of structure-property relations such as the Hall-Petch effect and show that the workflows can be reused across different interatomic potentials and materials within a coherent semantic framework. The approach provides AI-ready simulation data, supports emerging agentic AI workflows, and establishes a generalizable blueprint for knowledge-based mechanical and thermodynamic simulations.