Search

Machine learning interatomic potentials now enable molecular dynamics simulations with near-density-functional-theory accuracy at scales inaccessible to conventional quantum methods. Yet most remain fundamentally static: once trained, they are deployed without adaptation, even as simulations enter configurations outside the original training distribution. Although self-consistency ensures that predicted forces remain exact derivatives of the learned energy surface, it guarantees only internal coherence, not fidelity to the true reference landscape. As a result, small local errors can accumulate into substantial long-timescale drift. This paper proposes a conceptual framework for error-correcting machine learning potentials based on on-the-fly residual learning. The architecture combines a self-consistent base predictor with an error detector, a lightweight residual corrector, an online updater, and a memory manager. Embedded directly within the molecular dynamics loop, these components enable the system to identify unreliable predictions, apply immediate corrections, selectively request sparse density-functional-theory labels, and retain corrective knowledge during continuous adaptation. By shifting from static deployment to simulation-aware error correction, the framework addresses the central limitations of extrapolation failure and accumulated drift. It therefore outlines a path toward adaptive machine learning potentials capable of sustaining reliable long-timescale materials simulations with controlled computational overhead.

Machine learning interatomic potentials achieve high accuracy at molecular-dynamics scales but rely on local descriptors that truncate long-range interactions. In charge-transfer-sensitive materials, where electrostatics decay as 1/r, this assumption fails. The resulting mismatch produces systematic errors, including non-convergent energies, force discontinuities, unstable charge distributions, oxidation-state ambiguity, suppressed ionic transport, and collapsed dielectric response. These effects are not incidental but arise directly from the absence of global electrostatic coupling. This work presents a failure-mode analysis that links these breakdowns to their physical origin and introduces practical diagnostics based on standard simulation outputs. It further outlines mitigation strategies that restore long-range physics through electrostatic solvers, charge equilibration, multipole representations, and hybrid architectures. By establishing when and why locality fails, the study defines the minimal requirements for charge-transfer-aware machine learning potentials and provides a pathway toward reliable simulation of ionic and polar materials.