用生成式AI自动分析半导体材料电镜图像，效果媲美专家。
Sparks of Artificial General Intelligence(AGI) in Semiconductor Material Science: Early Explorations into the Next Frontier of Generative AI-Assisted Electron Micrograph Analysis
- 结合大模型与文本生成图像，实现端到端自动化分析
- 在少量样本下准确识别纳米材料，精度超越传统方法
- 为材料科学中的通用人工智能探索提供新路径
电子显微镜表征材料面临自动化标注难题，主要源于纳米结构的复杂性。为此，本文提出一个完全自动化的端到端流程，利用生成式AI最新进展，实现对半导体材料微观结构的有效分析，其性能可媲美人类专家，助力推动纳米材料识别中的通用人工智能（AGI）发展。该方法融合GPT-4V等大型多模态模型（LMMs）与DALL-E 3等文本到图像生成模型，通过GPT-4引导的视觉问答（VQA）分析图像，借助DALL-E 3生成合成纳米材料图像，并采用少样本提示的上下文学习策略，在GPT-4V中实现高精度纳米材料识别。实验表明，该方法在精度上优于传统技术，且支持高通量筛选。
原文摘要 · Abstract (English)
Characterizing materials with electron micrographs poses significant challenges for automated labeling due to the complex nature of nanomaterial structures. To address this, we introduce a fully automated, end-to-end pipeline that leverages recent advances in Generative AI. It is designed for analyzing and understanding the microstructures of semiconductor materials with effectiveness comparable to that of human experts, contributing to the pursuit of Artificial General Intelligence (AGI) in nanomaterial identification. Our approach utilizes Large MultiModal Models (LMMs) such as GPT-4V, alongside text-to-image models like DALLE-3. We integrate a GPT-4 guided Visual Question Answering (VQA) method to analyze nanomaterial images, generate synthetic nanomaterial images via DALLE-3, and employ in-context learning with few-shot prompting in GPT-4V for accurate nanomaterial identification. Our method surpasses traditional techniques by enhancing the precision of nanomaterial identification and optimizing the process for high-throughput screening.
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