Bachelor Thesis: Active Learning Strategies for Machine Learned Potentials
Author: Meret Unbehaun, 2022 (EMail, LinkedIn)
Project for the Active Learning Framework
- parallel running PL, oracle, candidate updater, query selector
- framework runs within one cluster
- communication between components: through databases and storage for SL model
- utilised Python 3.10
- python 3 is necessary, but other versions might work as well (e.g., Python 3.7)
- Packages:
- tqdm
- numpy
- dataclasses
- if using default databases: mysql and mysql-connector-python
Implement the concrete ML problem in a separate branch.
- Implement system_initiator -> will provide everything that is necessary
- Exemplary implementations on separate branches:
- Stream-based example, house price regression: https://github.com/aimat-lab/ActiveLearningFramework/tree/example_sbs
- Pool-based example, butene energy and force calculation: https://github.com/aimat-lab/ActiveLearningFramework/tree/Butene_energy_force_PbS
- Stream-based example, methanol energy and force calculation: https://github.com/aimat-lab/ActiveLearningFramework/tree/Methanol_SbS
| Abbreviation | Meaning |
|---|---|
| AL | Active Learner/Active Learning |
| PL | Passive Learner |
| ML | Machine Learning |
| SL | Supervised Learning |
| db | database |
| x, y | input, output of PL |