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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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Release files for camd 2022.8.24

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