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

2026-02-01

AI Summary

This work addresses the limited reproducibility and data reusability in atomistic simulations, which often stem from fragmented scripting practices, inconsistent metadata, and inadequate provenance tracking. To overcome these challenges, we propose the first reusable simulation framework that integrates semantic workflows with a materials application ontology. By structuring metadata for key mechanical and thermodynamic properties, our approach enables automated provenance capture and generates outputs compliant with FAIR (Findable, Accessible, Interoperable, Reusable) principles. The framework supports workflow reuse across different interatomic potentials and material systems, successfully validating structure–property relationships such as the Hall–Petch effect. It produces standardized, interoperable, high-quality datasets, establishing a knowledge-driven paradigm for AI-ready atomistic simulations.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Knowledge Representation and Reasoning: Ontologies

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Web data generation and simulation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• atomistic simulations
• FAIR data
• workflow reproducibility
• metadata inconsistency
• provenance tracking

Innovation

• semantic workflows
• FAIR data
• atomistic simulations
• ontology-based metadata
• AI-ready data