基于大型语言模型的声学元材料的声音吸收数据驱动的设计
Yongfeng Jiang1,2, Siyang Cao1,2, Han Meng3,4
1State Key Laboratory for Mechanics and Control for Aerospace Structures, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, People's Republic of China.
Scientific reports
|December 4, 2025
概括
大型语言模型 (LLM) 提供了用户友好的声学元材料设计,消除了复杂的机器学习 (ML) 专业知识. 新的策略可以通过对话或微调实现快速,准确的设计,推进数据驱动的元材料开发.
科学领域:
- 声学元材料是一种声学元材料.
- 数据驱动的设计
- 人工智能的人工智能
背景情况:
- 机器学习 (ML) 广泛用于声学元材料设计,但需要专业技能,缺乏通用性.
- 现有的ML模型通常是结构特定的,限制了广泛的实际应用.
- 在声学超材料研究中,需要可访问和多功能设计策略.
研究的目的:
- 基于大语言模型 (LLM) 的声学元材料引入两种新的数据驱动的设计策略.
- 消除在声学元材料设计中需要先进的ML知识和编码技能.
- 为前向和反向声学元材料设计提供通用,用户友好的方法.
主要方法:
- 代理互动策略:使用像ChatGPT这样的LLM作为通过文本进行参数到性能映射的代理.
- 法学士微调策略:重新训练像DeepSeek这样的模型在声学超材料数据集上,以提高预测和反向设计.
- 这两种策略都侧重于简化设计过程和提高可访问性.
主要成果:
- 代理互动策略通过简单的对话在不到一分钟的时间内实现了声学超材料设计.
- 与传统的ML模型相比,微调的LLM策略在设计结果中表现出更高的准确性.
- 通过不断的微调,LLM显示出在元材料领域的专业化潜力.
结论:
- 基于LLM的策略为声学元材料设计提供了传统ML的可行,用户友好的替代方案.
- 这些方法显著扩大了数据驱动设计在现场的可访问性.
- 该研究强调了LLMs在推进元材料科学和工程方面的变革潜力.
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