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利用大型语言模型来学习聚合物性质的稀缺数据
Ning Liu1, Siavash Jafarzadeh2, Brian Y Lattimer3
1Global Engineering and Materials Inc., Princeton, NJ, USA.
Nature computational science
|February 10, 2025
概括
本研究介绍了一个基于物理的培训管道,以解决大语言模型 (LLM) 的材料建模中的数据稀缺问题. 该方法使用合成数据进行预训练,提高了LLM准确性,用于诸如聚合物可燃性预测等任务.
科学领域:
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
- 计算建模 计算建模
背景情况:
- 大型语言模型 (LLM) 显示了材料建模的潜力,但需要广泛的数据才能准确.
- 获得足够的实验数据用于LLM微调通常是有限的和昂贵的.
- 数据稀缺性对开发精确的基于LLM的材料模型构成重大挑战.
研究的目的:
- 开发基于物理的培训管道,以克服LLM材料建模中的数据稀缺性.
- 为了实现准确的LLM培训和微调,即使在有限的实验数据集.
- 提高LLM在材料评估,分析和设计中的可靠性和适用性.
主要方法:
- 基于物理的建模框架产生了大量的合成数据,用于初始的LLM对齐.
- 采用了两阶段的培训策略:使用合成数据进行监督预训,然后使用实验数据进行微调.
- 该管道使用聚合物可燃性指标进行了验证,这是一个缺乏实验数据的领域.
主要成果:
- 使用合成数据进行监督的预培训对于实现精准微调的LLMs至关重要.
- 基于物理的管道有效地减轻了数据稀缺的负面影响.
- 该方法在学习聚合物可燃性指标方面取得了成功.
结论:
- 基于物理的数据生成是一种可行的策略,以解决LLM材料建模中的数据稀缺问题.
- 拟议的两阶段培训管道提高了LLM的准确性和可靠性.
- 这种方法提供了一条更高效和有效的材料设计和分析的途径,使用LLMs.
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