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.

翻译：力学与热力学性质（包括晶体缺陷的影响）是工程应用中评估材料性能的关键指标。分子动力学模拟为在原子尺度理解这些机制提供了重要见解。然而，当前实践通常依赖于零散的脚本，其元数据不一致且溯源信息有限，这阻碍了研究的可重复性、互操作性与可重用性。FAIR数据原则与基于工作流的方法为解决这些局限性提供了路径。本文提出可复用的原子模拟工作流，其中整合了与应用本体对齐的元数据标注，能够自动捕获溯源信息并生成符合FAIR标准的数据输出。该工作流涵盖关键的力学与热力学量，包括状态方程、弹性张量、机械加载、热学性质、缺陷形成能以及纳米压痕。我们验证了霍尔-佩奇效应等结构-性能关系，并证明该工作流可在一致的语义框架内跨不同原子间势函数和材料体系实现复用。该方法提供了AI就绪的模拟数据，支持新兴的智能体AI工作流，并为基于知识的力学与热力学模拟建立了可推广的蓝图。