Dataset Curation. The designs in AircraftVerse are battery-powered, where the propulsion comes from the electric motors that power the propellers. Our design corpus includes a range of propellers, motors, batteries, and fixed wings. The list of components is provided in Appendix F. We use the propulsion subsystem design as an example to describe the curation of the dataset. We use a combination of wings and propellers (in the right topology) provides the thrust and lift needed for efficient horizontal flight and vertical take-off and landing. Our components are diverse, such as the propellers have a different number of rotor blades with different diameters and pitches, the component motors have different  $K_{v}$ ,  $K_{m}$ ,  $K_{t}$  ratings, and the batteries have different peak and continuous current ratings and different voltage ratings. Consequently, the design choices allow significant diversity in structure and composition for similar performance. For example, the thrust produced by a propeller-motor pair can be related approximately to the parameters by the following formula: Thrust  $\propto (K_v \times \text{Voltage}) / (\text{Pitch} \times \text{Diameter})$ , where  $K_{v}$  is the motor's "Kilovolt" rating, which represents the number of revolutions per minute (RPM) that the motor will produce per volt, Voltage is the voltage being applied to the motor, Pitch is the distance that the propeller would move forward in one rotation, and Diameter is the distance across the propeller. Our design used more detailed models [31,24], but this dependency illustrates how the same thrust can be produced by different combinations of propellers and motors. High  $K_{v}$  rating motors that rotate much faster can produce the same thrust with smaller propellers when compared to lower  $K_{v}$  (slower) motors with larger diameter propellers. These combinations can have different continuous and peak current requirements that would influence the battery choice. In addition to these propulsion choices, connectors such as parametric joints, hubs, and arms can be put together in flexible ways to build interesting design geometry and topologies. These can have an impact on the experienced drag, which would then influence the requirements for propulsion. Thus, the design of aircraft requires making a number of tightly coupled design choices. The performance of each design can be assessed using a pipeline comprising commercial tools such as Creo [24] and custom flight dynamics model (FDM) [31,2]. Each aircraft is also assessed on controllability (existence of trim states - where the aircraft remains stable in the absence of environment perturbations) at different speeds using the FDM. In particular, the FDM evaluates whether the aerial vehicle can fly at a specific velocity through optimization schemes that check if the translational and rotational accelerations can be driven to zero for a given design during vertical take-off and horizontal flight (more details in Appendix C). Manually finding a large number of feasible designs is very challenging and prohibitively time-consuming. Instead, we use a learning-based design approach [6]. We create a number of designs using a procedural aircraft generator that uses heuristics provided by domain experts, such as limiting the complexity of produced designs to have at most 16 propellers and at most 12 wings, using design motifs and symmetries that help with controllability of the aircraft. A transformer-based model is trained on designs with good flight characteristics to act as a filter in the future generation process (Appendix E). Each of the final 27,714 designs included in the dataset is run through the detailed scientific and engineering models [31,2,24] to generate the metadata describing the design performance and characteristics.