Summary of A Sober Look at Llms For Material Discovery: Are They Actually Good For Bayesian Optimization Over Molecules?, by Agustinus Kristiadi et al.
A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?
by Agustinus Kristiadi, Felix Strieth-Kalthoff, Marta Skreta, Pascal Poupart, Alán Aspuru-Guzik, Geoff Pleiss
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This research paper explores the application of large language models (LLMs) in accelerating principled Bayesian optimization (BO) in molecular space. The study focuses on leveraging LLMs as fixed feature extractors for standard BO surrogate models and using parameter-efficient finetuning methods to obtain the posterior of the LLM surrogate. The researchers conducted extensive experiments with real-world chemistry problems, showing that LLMs can be useful for BO over molecules when pre-trained or fine-tuned with domain-specific data. This work aims to provide a sober and dispassionate answer to the question of whether LLMs are suitable for accelerating principled BO in molecular space. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at using special computers called large language models (LLMs) to help find new materials more efficiently. The scientists want to know if these computers can be used with a method called Bayesian optimization (BO) to discover new molecules faster. They tested the LLMs by treating them like tools that provide information about the properties of molecules, and then using that information to make predictions. The results show that the LLMs are helpful for finding new molecules when they’re trained on data related to materials science. |
Keywords
* Artificial intelligence * Optimization * Parameter efficient
