in subsequent examples for brevity. Atlas supports parameter spaces consisting of continuous, discrete, and categorical parameters, and arbitrary combinations thereof, in sequential or batched optimization mode. The definition of vector-valued descriptors for categorical parameter options is also supported. BO strategies in Atlas primarily use GP surrogate models, and use the low-level infrastructure of the PyTorch, GpyTorch 92 and BoTorch 58 libraries. In the following section, the main capabilities of Atlas (Figure ) are explained. For more information on each concept, please visit the Atlas GitHub, documentation, and tutorial notebook. Importantly, Atlas allows users to combine its key capabilities to suit their specialized needs. Barring a few incompatibilities (described in detail in our documentation), capabilities can be combined and interchanged freely. For example, the a priori known and unknown constraints can be used for any parameter space, with any acquisition function, acquisition optimizer, and any planner. Multi-objective optimization via ASFs, robust optimization, and asynchronous optimization can also be used in this way.