Project LlamaPlastics: LLM-Powered Materials Discovery Platform

Overview

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Architecture

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Setup

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Usage

Running the Agent

```bash

Run the autonomous discovery agent

python main_agent.py --config config.yaml --output-dir agent_results --num-iterations 20

Run the active learning loop

python main_active_learning.py --config config.yaml --output-dir results

Run multi-objective optimization

python main_optimize.py --config config.yaml --output-dir optimization_results ```

Using the MLX Deployment

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Repository Structure

``` llamaplastics_project/ │ ├── README.md ├── requirements.txt ├── config.yaml │ ├── data/ ├── llm_interface/ ├── composition_encoding/ ├── property_predictor/ ├── active_learning/ ├── robotics_interface/ ├── simulation_interface/ ├── optimization/ ├── mlx_deployment/ ├── training/ ├── evaluation/ ├── agent/ │ ├── main_active_learning.py ├── main_optimize.py ├── main_agent.py │ └── notebooks/ ```

Contributing

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License

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