AI 中文总结
研究机器学习原子间势元数据分散问题，提出MLIPs本体，涵盖方法、训练数据和基准三个模块，连接相关现有本体，通过示例展示和评估，增强数据完整性与一致性。
AI 中文摘要
机器学习原子间势（MLIPs）能以低成本近似量子力学能量和力，传统上由密度泛函理论（DFT）或波函数方法计算。该领域算法、训练数据集等元数据分散。本文提出MLIPs本体，它是OWL 2 DL本体，涵盖描述MLIP方法等所需概念，分为三个模块，连接材料科学和机器学习现有本体。通过示例展示并评估，证明其可增强数据完整性和一致性。
英文摘要
Machine learning interatomic potentials (MLIPs) approximate quantum-mechanical energies and forces---conventionally computed by density functional theory (DFT) or wave-function methods---at a fraction of the cost. The field encompasses a growing ecosystem of algorithms, training datasets, hyperparameters, and target materials, yet the metadata needed to systematically compare, reproduce, and build upon MLIP studies remains scattered across papers, scripts, and ad-hoc file formats. We present the MLIPs ontology, an OWL 2 DL ontology that captures the concepts needed to describe MLIP methods, their hyperparameters, training datasets with DFT provenance, and published benchmarks. The ontology is organized into three modules---Method, Training Data, and Benchmark---and connects existing ontologies in materials science (MDO, CMSO/ASMO) and machine learning (ML-Schema), complementing dataset-side schemas such as Croissant. It declares 27 formal axioms enforcing data completeness and consistency, including property chains that link trained models to their methods and training data. We demonstrate the ontology through a running example based on Moment Tensor Potentials and evaluate it through competency-question execution on a 20-paper seeded knowledge graph, OWL reasoning, and comparison with existing ontologies.
