x 32 channel that consists of the Boltzmann factors (called the Boltzmann grid in this work) was added to existing three girds. Subsequently, inverse design was conducted both with and without the Boltzmann grid, and the generated samples were examined. As depicted in Figure , the generated samples with the supplementary Boltzmann grid exhibited more distinct distributions of the targeted Henry coefficient values. This can be attributed to the fact that the Boltzmann grid provides more straightforward information that enable the neural network to learn the correlation between the zeolites structures and their Henry coefficients. This suggests that, depending on the target property, users can enhance the overall quality of the inverse design process by integrating an additional channel that is closely relevant to the property of interest.