Boltzmann priors for Implicit Transfer Operators
Juan Viguera Diez Mathias Jacob Schreiner Ola Engkvist Simon Olsson
Accurate prediction of thermodynamic properties is essential in drug discovery and materials science. Molecular dynamics (MD) simulations provide a principled approach to this task, yet they typically rely on prohibitively long sequential simulations. Implicit Transfer Operator (ITO) Learning offers a promising approach to address this limitation by enabling stable simulation with time steps orders of magnitude larger than MD. However, to train ITOs, we need extensive, unbiased MD data, limiting the scope of this framework. Here, we introduce Boltzmann Priors for ITO (BoPITO) to enhance ITO learning in two ways. First, BoPITO enables more efficient data generation, and second, it embeds inductive biases for long-term dynamical behavior, simultaneously improving sample efficiency by one order of magnitude and guaranteeing asymptotically unbiased equilibrium statistics. Furthermore, we showcase the use of BoPITO in a new tunable sampling protocol interpolating between ITOs trained on off-equilibrium simulations and an equilibrium model by incorporating unbiased correlation functions. Code is available at https://github.com/olsson-group/bopito.
Molecular Dynamics, Generative Models, Transfer Operators, Diffusion Models, Boltzmann Generators
We integrate Boltzmann Generators and Implicit Transfer Operator Learning to enable data-efficient training and interpolation between off-equilibrium and equilibrium models of Molecular Dynamics.