Metamaterials, engineered materials with architected structures across multiple length scales, offer unprecedented and tunable mechanical properties that surpass those of conventional materials. However, leveraging advanced machine learning (ML) for metamaterial discovery is hindered by three fundamental challenges: (C1) Data Heterogeneity Challenge arises from heterogeneous data sources, heterogeneous composition scales, and heterogeneous structure categories; (C2) Model Complexity Challenge stems from the intricate geometric constraints of ML models, which complicate their adaptation to metamaterial structures; and (C3) Human-AI Collaboration Challenge comes from the ''dual black-box'' nature of sophisticated ML models and the need for intuitive user interfaces. To tackle these challenges, we introduce a unified framework, named MetamatBench, that operates on three levels. (1) At the data level, we integrate and standardize 5 heterogeneous, multi-modal metamaterial datasets. (2) The ML level provides a comprehensive toolkit that adapts 17 state-of-the-art ML methods for metamaterial discovery. It also includes a comprehensive evaluation suite with 12 novel performance metrics plus a finite element-based assessment to ensure accurate and reliable model validation. (3) The user level features a visual-interactive interface that bridges the gap between complex ML techniques and non-ML researchers, advancing property prediction and inverse design of metamaterials for research and applications. MetamatBench offers a unified platform that enables machine learning researchers and practitioners to develop and evaluate new methodologies in metamaterial discovery. For accessibility and reproducibility, we open-source our benchmark and the codebase at https://github.com/cjpcool/Metamaterial-Benchmark.

In this paper, we introduce a new Multimodal Entity Linking (MEL) task on the multimodal data. The MEL task discovers entities in multiple modalities and various forms within large-scale multimodal data and maps multimodal mentions in a document to entities in a structured knowledge base such as Wikipedia. Different from the conventional Neural Entity Linking (NEL) task that focuses on textual information solely, MEL aims at achieving human-level disambiguation among entities in images, texts, and knowledge bases. Due to the lack of sufficient labeled data for the MEL task, we release a large-scale multimodal entity linking dataset M3EL (abbreviated for MultiModal Movie Entity Linking). Specifically, we collect reviews and images of 1,100 movies, extract textual and visual mentions, and label them with entities registered in Wikipedia. In addition, we construct a new baseline method to solve the MEL problem, which models the alignment of textual and visual mentions as a bipartite graph matching problem and solves it with an optimal-transportation-based linking method. Extensive experiments on the M3EL dataset verify the quality of the dataset and the effectiveness of the proposed method. We envision this work to be helpful for soliciting more research effort and applications regarding multimodal computing and inference in the future. We make the dataset and the baseline algorithm publicly available at https://jingrug.github.io/research/M3EL.