标题： 超越材料科学中的原子几何表示：一种人机协同的多模态框架 显示英文标题

标题： Beyond Atomic Geometry Representations in Materials Science: A Human-in-the-Loop Multimodal Framework

摘要： 大多数材料科学数据集仅限于原子几何结构（例如，XYZ文件），限制了它们在多模态学习和全面以数据为中心的分析中的实用性。 这些约束历史上阻碍了该领域采用先进的机器学习技术。 本研究介绍了一个经过整理的框架 MultiCrystalSpectrumSet (MCS-Set)，通过将原子结构与二维投影和结构化文本注释（包括晶格参数和配位度量）相结合，扩展了材料数据集。 MCS-Set 支持两项关键任务：(1) 多模态属性和摘要预测，以及 (2) 带有部分聚类监督的受限晶体生成。 利用人机循环管道，MCS-Set 结合了领域专业知识与标准化描述符，以实现高质量注释。 使用最先进的语言模型和视觉-语言模型的评估揭示了显著的模态特定性能差距，并强调了注释质量对泛化的重要性。 MCS-Set 为基准测试多模态模型、改进注释实践以及推广可访问且多功能的材料科学数据集奠定了基础。 数据集和实现代码可在 https://github.com/KurbanIntelligenceLab/MultiCrystalSpectrumSet 获取。 显示英文摘要

摘要： Most materials science datasets are limited to atomic geometries (e.g., XYZ files), restricting their utility for multimodal learning and comprehensive data-centric analysis. These constraints have historically impeded the adoption of advanced machine learning techniques in the field. This work introduces MultiCrystalSpectrumSet (MCS-Set), a curated framework that expands materials datasets by integrating atomic structures with 2D projections and structured textual annotations, including lattice parameters and coordination metrics. MCS-Set enables two key tasks: (1) multimodal property and summary prediction, and (2) constrained crystal generation with partial cluster supervision. Leveraging a human-in-the-loop pipeline, MCS-Set combines domain expertise with standardized descriptors for high-quality annotation. Evaluations using state-of-the-art language and vision-language models reveal substantial modality-specific performance gaps and highlight the importance of annotation quality for generalization. MCS-Set offers a foundation for benchmarking multimodal models, advancing annotation practices, and promoting accessible, versatile materials science datasets. The dataset and implementations are available at https://github.com/KurbanIntelligenceLab/MultiCrystalSpectrumSet.