User Guide

AMLRO provides a Python framework for iterative optimization of reaction conditions. The framework is organized into three main steps, each exposed as a dedicated function that can be called by the user:

1. Reaction Space Generation → get_reaction_scope()

2. Training Set Generation → generate_training_data()

3. Active Learning Prediction → get_optimized_parameters()

Overview of the AMLRO workflow.

The user guide explains these steps, configuration options, and file formats. Users do not need to modify internal ML models, Pareto front calculations, or optimization algorithms. AMLRO handles these internally based on the configuration provided.

• Configurations
• Reaction Space Generation
• Generate Training Data
• Active Learning Prediction