Chemistry inherently involves a wide range of stochastic processes, yet chemists do not typically explore stochastic processes at the macroscale due to the difficulty in gathering data. We wondered whether it was possible to explore such processes, in this case crystallization, in a systematic way using an autonomous robotic platform. By performing inorganic reactions in an automated system, and observing the resultant occupied macrostate (crystallization images), we developed a powerful entropy source for generation of true random numbers. Randomness was confirmed using tests described by the National Institute for Standards and Technology (puniformity >> 0.0001). Deficit from maximum approximate entropy was found be different between compounds (pANOVA << 0.01), and encryption security of these numbers was found to be greater than that of a frequently used pseudorandom number generator. This means that we can now use random number generation as a probe of the stochastic process, as well as explore potential real-world applications.