Bayesian Optimization of Catalysts With In-context Learning
- URL: http://arxiv.org/abs/2304.05341v1
- Date: Tue, 11 Apr 2023 17:00:35 GMT
- Title: Bayesian Optimization of Catalysts With In-context Learning
- Authors: Mayk Caldas Ramos, Shane S. Michtavy, Marc D. Porosoff, Andrew D.
White
- Abstract summary: Large language models (LLMs) are able to do accurate classification with zero or only a few examples.
We show a prompting system that enables regression with uncertainty for in-context learning with frozen LLMs.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large language models (LLMs) are able to do accurate classification with zero
or only a few examples (in-context learning). We show a prompting system that
enables regression with uncertainty for in-context learning with frozen LLM
(GPT-3, GPT-3.5, and GPT-4) models, allowing predictions without features or
architecture tuning. By incorporating uncertainty, our approach enables
Bayesian optimization for catalyst or molecule optimization using natural
language, eliminating the need for training or simulation. Here, we performed
the optimization using the synthesis procedure of catalysts to predict
properties. Working with natural language mitigates difficulty synthesizability
since the literal synthesis procedure is the model's input. We showed that
in-context learning could improve past a model context window (maximum number
of tokens the model can process at once) as data is gathered via example
selection, allowing the model to scale better. Although our method does not
outperform all baselines, it requires zero training, feature selection, and
minimal computing while maintaining satisfactory performance. We also find
Gaussian Process Regression on text embeddings is strong at Bayesian
optimization. The code is available in our GitHub repository:
https://github.com/ur-whitelab/BO-LIFT
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