In this study, 494498 hard-sphere packing simulations were conducted using a two-step process of a force-biased algorithm followed by the Lubachevsky-Stillinger algorithm . The simulations were performed using mixtures of three different particle types, each characterized by two log-normal distribution parameters and three composition parameters. Two parameters (scale and shape) describe each of the three distributions, and three additional composition parameters describe the fractional share (e.g., in terms of volume) of each of the particle types. Additionally, the number of particles and an initial scaling factor were allowed to vary. With a greater number of particles, denser and more realistic packs can be generated at the expense of computational cost (i.e., the fidelity parameter). The initial scaling factor affects the computational stability of the simulation; with an adequate scaling factor, the simulation is more likely to be completed successfully. The quasi-random Sobol sampling technique was employed to generate parameter combinations, enabling a more uniform sampling of the allowable parameter space. Although it may serve other purposes, this dataset was primarily designed as a multi-fidelity benchmark dataset for constrained adaptive design experiments. To realistically capture the noise in this dataset, simulations were run multiple times for each quasirandom parameter combination. To increase throughput and reduce latency, simulation parameters (including repeats) were shuffled and divided into batches, which were then dispatched to a high-performance computing environment for asynchronous evaluation. The results were recorded in a free-tier MongoDB Atlas database and then consolidated and prepared as datasets suitable for machine learning applications. For further implementation details, see and . Instructions for model usage are available at .