Abstract
<jats:p>Adaptive multiscale simulations, such as adaptive QM/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 a physical asymmetry. This asymmetry forces a prohibitive trade-off between severe structural distortions and the violation of Hamiltonian conservation (continuous energy drift). To definitively resolve this issue, we propose the ”Integrated Molecular Model,” a paradigm-shifting framework that mathematically 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 non-physical 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 unprecedentedly 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 fundamentally 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.</jats:p>