Tutorials

Learning-oriented, step-by-step guides that take you from zero to a working active-learning experiment. The notebooks live on GitHub (with committed outputs); this page curates them into a learning path.

🧪 Start with an experiment

Run the full ask/tell loop end to end:

• Offline Design Tutorial: the loop driven by labels from a held-out, pre-scored pool. The best entry point.

• Online Design Tutorial: the same loop, with labels from a live scorer.

🪶 Lightweight (alf-core only)

If you want to explore ALF’s core abstractions with minimal dependencies, start here:

• ALF Core Quickstart: an active learning loop on MNIST digit classification, built entirely on alf-core using numpy and scipy. Implements a softmax-regression surrogate and an uncertainty-sampling acquisition function — both custom, neither available in alf-tools — to show how to bring your own components.

🤖 Go deeper on models

Each tutorial focuses on a specific surrogate model type—when to use it, how to configure it, and what its uncertainty estimates look like.

• GP Tutorial: Gaussian Process surrogate, and its kernel cheat-sheet. Good default for small datasets where uncertainty calibration matters.

• CNN Tutorial: convolutional sequence surrogate. Scales better than GPs for larger candidate pools.

• Ensemble Tutorial: seed ensembles, MC dropout, and combined ensembles for uncertainty-aware prediction. Useful when you need calibrated uncertainty without a full Bayesian model.

• ESM-2 Tutorial: using a protein language model as a surrogate or zero-shot scorer. Useful when sequence context matters and you have limited labelled data.

• Chemprop MPNN Tutorial: active learning for small molecules using SMILES inputs and the Chemprop message-passing neural network. The right choice when working with molecular graphs rather than sequences.

• MLIP Tutorial: a machine learning interatomic potential model (MACE force field) as a surrogate over atomistic systems, predicting per-structure energies from structure data. The right choice when designing over 3D molecular or crystal structures.

🗄️ Go deeper on datasets

• GuacaMol Tutorial: exploring the GuacaMol drug-like molecule corpus—download, property analysis, and SMILES querying. A good starting point for small-molecule experiments.

• ProteinGym Tutorial: the offline design loop applied to a ProteinGym deep-mutational-scanning (DMS) fitness benchmark. Best read after the Offline Design Tutorial, which it mirrors on a real protein-fitness dataset.

To add your own model, dataset, acquisition or search function, see the How-to / Recipes.