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ALchemist

ALchemist: Active Learning Toolkit for Chemical and Materials Research

ALchemist is a modular Python toolkit that brings active learning and Bayesian optimization to experimental design in chemical and materials research. It is designed for scientists and engineers who want to efficiently explore or optimize high-dimensional variable spaces—without writing code—using an intuitive graphical interface.

Key Features

  • Flexible variable space definition: Real, integer, and categorical variables with bounds or discrete values.
  • Probabilistic surrogate modeling: Gaussian process regression via BoTorch or scikit-optimize backends.
  • Advanced acquisition strategies: Efficient sampling using qEI, qPI, qUCB, and qNegIntegratedPosteriorVariance.
  • Intuitive GUI workflow: No coding required—define variables, generate initial experiments, load data, train models, and suggest new experiments.
  • Experiment tracking: CSV logging, reproducible random seeds, and error tracking.
  • Extensibility: Abstract interfaces for models and acquisition functions enable future backend and workflow expansion.

Installation

We recommend using Anaconda to manage your Python environments for ALchemist.

1. Create a new environment: 

conda create -n alchemist-env python=3.12
conda activate alchemist-env

2. Install ALchemist:

Option A: Install directly from GitHub: 

python -m pip install git+https://github.com/NREL/ALchemist.git

Option B: Clone and install (recommended for development): 

git clone https://github.com/NREL/ALchemist.git
cd ALchemist
python -m pip install -e .

All dependencies are specified in pyproject.toml and will be installed automatically.

After installation, launch the graphical user interface by running:

alchemist

Use the sidebar to navigate through the documentation. See Getting Started to define your variable space and generate initial experiments.