工业场景下基于深度学习的时序预测方法及应用

以大模型在材料科学中的应用为着眼点，首先综述了大模型，介绍了大模型的基本概念、发展过程、技术分类与特点等内容；其次从通用领域大模型和垂直领域大模型两个角度，总结了大模型的应用，列举分析了不同种类大模型的应用场景和功能. 再次，结合材料科学领域中的具体需求研究现状，调研并综述了语言大模型、视觉大模型和多模态大模型在材料科学中的应用情况，以自然语言处理和计算机视觉中的具体任务为切入，参考典型应用案例，综合提示工程策略和零样本知识迁移学习，厘清了当前将大模型应用至材料科学的研究范式和制约因素，并利用改进SAM视觉大模型在四种材料显微图像数据上进行了验证性图像分割与关键结构提取实验，结果表明SAM带来的零样本分割能力对于材料微结构的精准高效表征具有巨大应用潜力. 最后，提出了大模型相关技术、方法在材料科学中的未来研究机遇，从单模态到综合性多模态的大模型研发与调优，评估了可行性及技术难点.

Deep learning

Representative large models and their related applications, such as Bidirectional encoder representations from transformers (BERT), Generative pretrained transformer (GPT), Segment anything model (SAM), ChatGPT, DALL-E, Wenxin, and Pangu, have made astounding strides and exerted considerable influence across various fields domestically and abroad. They constantly attract the attention and follow-up of diverse societal sectors, including enterprises, universities, and research institutions. Large model applications have been successfully applied in scenarios such as biology, medicine, law, and social governance. Designing, modifying, and constructing domain-specific large models are crucial for truly harnessing their application value. Therefore, this paper provides inspiration for the application of large models in materials science. First, it provides an overview of large models, introducing their basic concepts, developmental process, technical classification, and features. Second, from the perspectives of the general domain and specific large models, this paper summarizes the applications of large models and analyzes the application scenarios and functions of various types of large models. Subsequently, considering the specific needs and current state of research in the field of materials science, this paper reviews the application of large language models, large visual models, and large multimodal models. It integrates engineering strategies and zero-shot knowledge transfer learning from specific tasks in natural language processing and computer vision and referencing typical application cases, clarifying current research paradigms and limiting factors for applying large models to materials science. To verify the effectiveness and potential of the visual large model, basal experiments of image segmentation and key structure extraction are performed on the microscopic image data of four types of materials using improved SAM, including Ni-superalloy, superalloy, polycrystalline pure iron grain, and Inconel 939. The experimental results reveal that the zero-shot segmentation capability of SAM has enormous potential for accurate and efficient representation of material microstructures. With the help of tailored prompt engineering, precise masks of the precipitated phase, grain boundaries, and cracks can be outputted without any label. Finally, this paper proposes future research opportunities for technologies and methods related to large models in materials science. This paper assesses the feasibility and technical challenges for the development and tuning of unimodal to comprehensive multimodal large models. With continuous innovations and collaborations, the horizon for large models in materials science seems boundlessly promising. The integration of these models can produce a new era of advanced research, leading to advancements that were previously considered unattainable. The symbiosis between materials science and large models can pave the way for unforeseen discoveries, enriching our scientific prowess.