构建可直接用于AI训练的真实二维材料与界面设计框架
AI-ready design of realistic 2D materials and interfaces with Mat3ra-2D
- 用可复现的配置流水线生成含缺陷和无序的真实二维结构
- 支持定向晶面、应变匹配界面等典型结构的自动化构建
- 开源网页版工具，无需本地环境即可快速上手使用
人工智能与机器学习在材料科学中主要基于理想体相晶体训练，难以迁移至实际应用中以表面、界面和缺陷为主的情形。本文提出Mat3ra-2D，一个开源框架，可快速设计包含晶面、异质界面及非理想特征的真实二维材料结构。该框架结合标准化数据存储规范与模块化核心概念，并通过配置构建流水线实现结构生成全过程的溯源与元数据保留。典型任务如定向晶面、应变匹配界面的构建已封装为可交互的Jupyter笔记本，既作为文档又可用于复现计算。所有示例支持浏览器运行，便于集成至Web应用。该框架实现了面向AI/ML应用的真实二维材料与界面数据集的系统性构建与组织。
原文摘要 · Abstract (English)
Artificial intelligence (AI) and machine learning (ML) models in materials science are predominantly trained on ideal bulk crystals, limiting their transferability to real-world applications where surfaces, interfaces, and defects dominate. We present Mat3ra-2D, an open-source framework for the rapid design of realistic two-dimensional materials and related structures, including slabs and heterogeneous interfaces, with support for disorder and defect-driven complexity. The approach combines: (1) well-defined standards for storing and exchanging materials data with a modular implementation of core concepts and (2) transformation workflows expressed as configuration-builder pipelines that preserve provenance and metadata. We implement typical structure generation tasks, such as constructing orientation-specific slabs or strain-matching interfaces, in reusable Jupyter notebooks that serve as both interactive documentation and templates for reproducible runs. To lower the barrier to adoption, we design the examples to run in any web browser and demonstrate how to incorporate these developments into a web application. Mat3ra-2D enables systematic creation and organization of realistic 2D- and interface-aware datasets for AI/ML-ready applications.
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