Summary of Large Language Models, Physics-based Modeling, Experimental Measurements: the Trinity Of Data-scarce Learning Of Polymer Properties, by Ning Liu et al.
Large language models, physics-based modeling, experimental measurements: the trinity of data-scarce learning of polymer properties
by Ning Liu, Siavash Jafarzadeh, Brian Y. Lattimer, Shuna Ni, Jim Lua, Yue Yu
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent study explores the potential of large language models (LLMs) in material modeling for evaluation, analysis, and design. To overcome the limitations of data scarcity, which can hinder the accuracy and effectiveness of LLMs, the authors propose a novel physics-based training pipeline. This approach generates synthetic data to align the model with a physically consistent initial state before fine-tuning. The pipeline consists of two phases: supervised pretraining using abundant but less accurate synthetic data and finetuning with limited experimental data. Empirical results demonstrate that pretraining is crucial for achieving accurate LLMs, as exemplified by learning polymer flammability metrics using sparse cone calorimeter data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research investigates how to use large language models (LLMs) to better understand and predict the properties of materials. One challenge in using LLMs is that they need a lot of data to work well, but getting that data can be expensive or time-consuming. To solve this problem, the authors developed a new way to train LLMs using synthetic data that mimics real-world situations. This allows the model to learn from a much larger dataset than would be possible with limited experimental data alone. The results show that this approach is effective in learning important properties of materials, such as their flammability. |
Keywords
* Artificial intelligence * Fine tuning * Pretraining * Supervised * Synthetic data
