Abstract
Reproducibility of computational results remains a challenge in materials science, as simulation workflows and parameters are often reported only in unstructured text and tables. While literature data are valuable for validation and reuse, the lack of machine-readable workflow descriptions prevents large-scale curation and systematic comparison. Existing text-mining approaches are insufficient to extract complete computational workflows with their associated parameters. An ontology-driven, large language model (LLM)-assisted framework is introduced for the automated extraction and structuring of computational workflows from the literature. The approach focuses on density functional theory-based stacking fault energy (SFE) calculations in hexagonal close-packed magnesium and its binary alloys, and uses a multi-stage filtering strategy together with prompt-engineered LLM extraction applied to method sections and tables. Extracted information is unified into a canonical schema and aligned with established materials ontologies (CMSO, ASMO, and PLDO), enabling the construction of a knowledge graph using atomRDF. The resulting knowledge graph enables systematic comparison of reported SFE values and supports the structured reuse of computational protocols. While full computational reproducibility is still constrained by missing or implicit metadata, the framework provides a foundation for organizing and contextualizing published results in a semantically interoperable form, thereby improving transparency and reusability of computational materials data.
Abstract (translated)
计算结果的可重复性在材料科学中仍是一个挑战，因为模拟工作流程和参数通常仅以非结构化的文本和表格形式报告。尽管文献数据对于验证和重用非常重要，但缺乏机器可读的工作流描述阻碍了大规模的数据整理与系统的比较研究。现有的基于文本挖掘的方法不足以提取完整包含相关参数的计算工作流。 本文介绍了一种新的框架，该框架采用本体驱动、大型语言模型（LLM）辅助的方式，从文献中自动抽取和结构化计算工作流。该方法专注于以密度泛函理论为基础的堆积层错能（SFE）在六方紧密堆叠镁及其二元合金中的计算，并采用了多阶段过滤策略以及针对方法部分和表格进行了提示工程化的LLM提取技术。所提取的信息被统一为一个规范模式，并与现有材料本体论（CMSO、ASMO 和 PLDO）对齐，从而可以使用atomRDF构建知识图谱。 这种知识图谱允许系统性地比较已报告的SFE值，并支持计算协议的结构化重用。尽管完全重现计算结果仍然受限于缺失或隐含的元数据，该框架为组织和解释发表成果提供了一个语义互操作的基础形式，从而提高了计算材料数据的透明度和可重用性。
URL
https://arxiv.org/abs/2601.12582
