camd is software designed to support autonomous materials research and sequential learning
camd is software designed to support Computational Autonomy for Materials Discovery based on ongoing work led by the Toyota Research Institute.
camd enables the construction of sequential learning pipelines using a set of abstractions that include
- Agents - decision making entities which select experiments to run from pre-determined candidate sets
- Experiments - experimental procedures which augment candidate data in a way that facilitates further experiment selection
- Analyzers - Post-processing procedures which frame experimental results in the context of candidate or seed datasets
In addition to these abstractions, camd provides a loop construct which executes the sequence of hypothesize-experiment-analyze by the Agent, Experiment, and Analyzer, respectively. Simulations of agent performance can also be conducted using after the fact sampling of known data.
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