AMLRO

Active Machine Learning Reaction Optimizer (AMLRO) for data-efficient reaction process condition discovery and optimization.

AMLRO is an open-source framework designed to accelerate chemical reaction optimization using active learning with classical machine learning regression models. AMLRO integrates space-filling sampling strategies (e.g., Sobol and Latin Hypercube sampling) with iterative model training, prediction, and experiment selection to efficiently navigate complex reaction spaces. The platform supports multiple regression models, flexible multi-objective definitions, and user-defined parameter bounds, enabling data-efficient optimization from small initial datasets. AMLRO is designed for ease of use by experimentalists and can operate as a standalone decision-support tool or be integrated into closed-loop automated experimentation workflows.

AMLRO follows a three-step workflow:

1. Reaction space generation
2. Training set generation with experimental feedback
3. Active learning-loop -> prediction of optimal reaction conditions.

For tutorials and interactive notebooks, see the documentation. 📘 Documentation

Click the badge below to open AMLRO Interactive notebook in Google Colab:

Citation

If you use AMLRO in your research, please cite:

Kulathunga, D. P. et al. RxnRover/amlro. Computer Software. USDOE Office of Energy Efficiency and Renewable Energy (EERE), Advanced Materials & Manufacturing Technologies Office (AMMTO), 2026. DOI: https://doi.org/10.11578/dc.20260205.1

BibTeX:

@misc{doecode_174798,
  title        = {RxnRover/amlro},
  author       = {Kulathunga, Dulitha Prasanna and Crandall, Zachery},
  abstractNote = {AMLRO (Active Machine Learning Reaction Optimizer)...},
  doi          = {10.11578/dc.20260205.1},
  url          = {https://doi.org/10.11578/dc.20260205.1},
  howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20260205.1}},
  year         = {2026},
  month        = {feb}
}

Quick Installation

Create a virtual environment (recommended):

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Clone and install:

git clone https://github.com/RxnRover/amlro.git
cd amlro
pip install -e .