Search papers, labs, and topics across Lattice.
This study introduces Self-Consistent Ultra-Coarse-Graining (SC-UCG), a novel approach that enhances molecular dynamics simulations by directly assigning internal states through graph message passing, eliminating the need for user-defined collective variables. By integrating the Bethe approximation and AI-based inference, SC-UCG effectively captures the correlations between coarse-grained molecules, leading to a more accurate representation of multistate phenomena. The method successfully models a tetramer's phase transition from a supercritical racemic fluid to distinct subcritical phases, demonstrating its robustness even when trained on limited data.
SC-UCG reveals that accurate modeling of phase transitions in complex biomolecular systems can be achieved without predefined collective variables, challenging traditional coarse-graining methods.
Bottom-up coarse-graining expands the length and time scales accessible to molecular dynamics (MD) simulations, but information loss can hinder accurate representation of multistate phenomena in complex biomolecular dynamics. Ultra-Coarse-Graining (UCG) projects discrete"quantum-like"extended degrees of freedom, or"internal states,"onto coarse-grained (CG) molecules, extending CG model expressiveness. The rapid-local-equilibrium (RLE) approximation in UCG depends on user-defined collective variables (CVs, e.g., local density) and neglects correlations between internal states within and between CG molecules. We present Self-Consistent UCG (SC-UCG), which uses the underlying UCG interactions directly to assign internal states without designing CVs in the CG ensemble. During simulation, internal state probabilities are determined self-consistently through graph message passing. We enhance the RLE Hamiltonian with the Bethe approximation and AI-based inference to represent explicit correlations between UCG beads. For force-field training, we develop Multilayer Internal State Consistency (MISC), a machine-learning method derived from relative entropy minimization that avoids iterative sampling of intermediate force fields. We apply SC-UCG to a tetramer exhibiting a second-order symmetry-breaking phase transition from a supercritical racemic fluid to subcritical D-rich and L-rich fluids. SC-UCG captures collective switching of internal states in the subcritical region and recapitulates the phase transition across temperatures, despite being trained on a single-temperature dataset.
