Beyond Multiscale: A Neural-Network-Unified Molecular Model for Asymmetry-Free Dynamics Simulation

Adaptive multiscale simulations, such as adaptive quantum mechanics (QM)/molecular mechanics (MM), are indispensable for investigating complex solution dynamics but historically suffer from a fundamental dilemma: the dynamic exchange of identical solvent molecules across different model resolutions introduces an energetic asymmetry under solvent exchange (specifically, a permutation asymmetry where physically identical molecules are treated indistinguishably yet subjected to different force fields depending on their instantaneous positions). This asymmetry forces a prohibitive trade-off between severe structural distortions and the violation of Hamiltonian conservation (continuous energy drift). To resolve this issue, we propose the “Integrated Molecular Model,” a paradigm-shifting framework that unifies multiple resolutions into a single, dynamically consistent potential. By employing a neural network that takes only the multiscale weight functions as input descriptors, our model learns to systematically cancel the nonphysical transition forces originating from spatial discontinuities. As a proof-of-concept, we validated this unified framework on bulk water, aqueous ionic solutions, and reactive hydronium ions. Our approach not only faithfully restores artifact-free solvation structures but also strictly conserves the Hamiltonian, enabling stable, long-term simulations under the microcanonical (NVE) ensemble. Consequently, the model successfully eliminates artificial thermostat-induced breathing modes, allowing for the rigorous evaluation of intrinsic dynamical properties such as diffusion coefficients. Furthermore, owing to its physically informed network design, the correction potential introduces less than 2% additional computational overhead and demonstrates remarkable zero-shot transferability across chemically similar ionic species. By eradicating the inherent asymmetry of adaptive multiscale modeling, this highly scalable framework establishes a robust foundation for high-fidelity simulations of complex condensed-phase phenomena, including reactive dynamics at solid–liquid interfaces and ion transport in battery electrolytes.