Harnessing large language models for data-scarce learning of polymer properties

IF 12 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Ning Liu, Siavash Jafarzadeh, Brian Y. Lattimer, Shuna Ni, Jim Lua, Yue Yu
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引用次数: 0

Abstract

Large language models (LLMs) bear promise as a fast and accurate material modeling paradigm for evaluation, analysis and design. Their vast number of trainable parameters necessitates a wealth of data to achieve accuracy and mitigate overfitting. However, experimental measurements are often limited and costly to obtain in sufficient quantities for fine-tuning. To this end, here we present a physics-based training pipeline that tackles the pathology of data scarcity. The core enabler is a physics-based modeling framework that generates a multitude of synthetic data to align the LLM to a physically consistent initial state before fine-tuning. Our framework features a two-phase training strategy: utilizing the large-in-amount but less accurate synthetic data for supervised pretraining, and fine-tuning the phase-1 model with limited experimental data. We empirically demonstrate that supervised pretraining is vital to obtaining accurate fine-tuned LLMs, via the lens of learning polymer flammability metrics where cone calorimeter data are sparse. A physics-based training pipeline is developed to help tackle the challenges of data scarcity. The framework aligns large language models to a physically consistent initial state that is fine-tuned for learning polymer properties.

Abstract Image

利用大型语言模型进行数据稀缺的聚合物性质学习。
大型语言模型(llm)有望成为一种快速、准确的材料建模范式,用于评估、分析和设计。它们的大量可训练参数需要大量的数据来实现准确性和减轻过拟合。然而,实验测量往往是有限的和昂贵的,以获得足够数量的微调。为此,我们提出了一个基于物理的培训管道来解决数据稀缺的问题。核心推动者是一个基于物理的建模框架,该框架生成大量合成数据,以便在微调之前将LLM对齐到物理一致的初始状态。我们的框架具有两阶段训练策略:利用大量但不太准确的合成数据进行监督预训练,并使用有限的实验数据对第一阶段模型进行微调。我们的经验表明,通过学习聚合物可燃性指标(锥体量热计数据稀疏),监督预训练对于获得精确的微调llm至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
11.70
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