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arXiv · 2510.09784

Combined Representation and Generation with Diffusive State Predictive Information Bottleneck

Richard John·Yunrui Qiu·Lukas Herron·Pratyush Tiwary
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Abstract

Generative modeling becomes increasingly data-intensive in high-dimensional spaces. In molecular science, where data collection is expensive and important events are rare, compression to lower-dimensional manifolds is especially important for various downstream tasks, including generation. We combine a time-lagged information bottleneck designed to characterize molecular important representations and a diffusion model in one joint training objective. The resulting protocol, which we term Diffusive State Predictive Information Bottleneck (D-SPIB), enables the balancing of representation learning and generation aims in one flexible architecture. Additionally, the model is capable of combining temperature information from different molecular simulation trajectories to learn a coherent and useful internal representation of thermodynamics. We benchmark D-SPIB on multiple molecular tasks and showcase its potential for exploring physical conditions outside the training set.

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Richard John, Yunrui Qiu, Lukas Herron, Pratyush Tiwary. 2025-10-10. Combined Representation and Generation with Diffusive State Predictive Information Bottleneck. https://arxiv.org/abs/2510.09784

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