https://github.com/wiseodd/llm-bayesopt-exps
Official experiment code for the "Sober Look at LLMs for Material Discovery" paper.
https://github.com/wiseodd/llm-bayesopt-exps
Last synced: 5 months ago
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Official experiment code for the "Sober Look at LLMs for Material Discovery" paper.
- Host: GitHub
- URL: https://github.com/wiseodd/llm-bayesopt-exps
- Owner: wiseodd
- Created: 2024-07-30T13:45:39.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-22T14:57:31.000Z (over 1 year ago)
- Last Synced: 2025-02-09T03:11:17.684Z (about 1 year ago)
- Language: Python
- Size: 30.7 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# A Sober Look at LLMs for Material Discovery
Official experiment repo for the "A Sober Look at LLMs for Material Discovery" paper (ICML 2024).
> [!TIP]
> If you just want to use the method as a library, check out the sister repo: .
## Setup
> [!IMPORTANT]
> Note that the ordering is important.
1. Install PyTorch (with CUDA):
2. Install Huggingface libraries and others: `pip install transformers datasets peft tqdm`
3. Install laplace-torch: `pip install laplace-torch`
## Fixed-Feature Experiments
Cache molecules in $\mathcal{D}_{\mathrm{cand}}$ (see full parameters in the Python file):
```
python cache_features.py --feature_type {FEATURE_TYPE} --problem {PROBLEM} --prompt_type {PROMPT_TYPE}
```
Then, do BO:
```
python run_fixed_features.py --feature_type {FEATURE_TYPE} --method {METHOD} --randseed {RANDSEED} --problem {PROBLEM}
```
Similarly for the multiobjective experiments (`cache_features_multiobj.py` and `run_multiobj.py`).
## Finetuning Experiments
Simply run the following.
```
python run_finetuning.py --foundation_model {FOUNDATION_MODEL} --randseed {RANDSEED} --problem {PROBLEM}
```
See the Python file for the full arguments.
## BO-LIFT In Context Learning Baseline
The script is in `baselines/run_bolift.py`. It has similar options as the fixed-feature script.
## Citation
```bib
@inproceedings{kristiadi2024sober,
title={A Sober Look at {LLMs} for Material Discovery: {A}re They Actually Good for {B}ayesian Optimization Over Molecules?},
author={Kristiadi, Agustinus and Strieth-Kalthoff, Felix and Skreta, Marta and Poupart, Pascal and Aspuru-Guzik, Al\'{a}n and Pleiss, Geoff},
booktitle={ICML},
year={2024}
}
```