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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.

Computational materials engineering has undergone a transformative shift with the integration of data-driven methodologies and artificial intelligence, enabling accelerated discovery and design of novel materials. Uncertainty quantification (UQ) plays a pivotal role in this paradigm, addressing inherent variabilities in simulations, experimental data, and model predictions to ensure reliable decision-making in materials development. This review synthesizes recent advancements in UQ methods within computational and data-driven materials engineering, focusing on probabilistic modeling, sensitivity analysis, and Bayesian inference techniques deployed across multiscale simulations and machine learning frameworks. We examine deployment contexts ranging from molecular dynamics to additive manufacturing, highlighting how UQ enhances robustness in property prediction, process optimization, and autonomous discovery systems. By integrating insights from high-impact studies the review delineates a systems-level perspective on UQ infrastructures, emphasizing their role in bridging computational predictions with experimental validation. Key challenges such as computational efficiency and data scarcity are contextualized, alongside opportunities for multimodal integration. Ultimately, this synthesis positions UQ as an essential infrastructure for advancing materials informatics toward industrial applicability, offering a forward-looking outlook on scalable, uncertainty-aware workflows in materials engineering.