BIT: Boltzmann-Inspired Training for One-Step Molecular Generation

Molecular forces provide rich local information about the Boltzmann distribution. To harness this information for direct one-step molecular generation, we introduce Boltzmann-Inspired Training (BIT), an attraction–repulsion framework with adaptive training targets. BIT projects reference forces onto atom-pair directions and uses their alignment with differences in pairwise distances between generated and reference conformations to reweight the local attraction. It combines the resulting target with a data-only target using a weight that varies across generated conformations, and trains the generator by regression. On seven MD17 molecules, BIT reduces mean pooled-distance total variation distance (TVD) by 55% and mean atom-pair TVD by 24% relative to the same generator trained without forces. On average across molecules, BIT also achieves lower pooled-distance TVD and distance-histogram mean absolute error (MAE) than AvgFlow and ET-Flow. At inference, the trained generator produces each conformation with a single network evaluation, while force information is used only during training.