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phases with the fidelity required for quantitative predictions. For example, although total energy estimates
for surface geometries may achieve moderate accuracy, they frequently fail to resolve surface specific
energetics (for instance, cleavage or adsorption energies), underscoring a shortfall in generalization across
[48]
disparate datasets and property domains .

One avenue to redress dataset imbalance is data augmentation, in which underrepresented regions of
chemical and structural space are populated by additional configurations generated through high-
throughput simulations . Concurrently, tailored IAPs designed for structurally complex materials have
[49]
demonstrated markedly improved performance. For example, DefiNet augments the host graph with
vacancy markers and employs defect-aware message passing to capture interactions between vacancies,
substitutions and pristine atoms, while fine-tuned MLFFs for defected crystals revise local‐environment
descriptors and network capacity to accommodate disrupted periodicity . Through systematic refinement
[50]
of descriptor schemes or the integration of higher-capacity, symmetry-preserving architectures, these
bespoke models extend the applicability of ML-IAPs to materials with intricate defect landscapes.

ML-IAPs models: balance between complexity and efficiency
Although advanced network architectures and sophisticated optimization schemes have propelled ML-IAPs
to unprecedented accuracy, they also introduce notable challenges. As models become more complex, they
may overfit the training data, while simpler models may lose important details . Iterative optimization
[51]
routines further complicate matters by permitting error accumulation across successive parameter updates,
which can undermine predictive consistency. A compelling strategy to mitigate these issues may be the
development of fully differentiable, high-precision end-to-end frameworks, in which the final loss can be
backpropagated through every component of the modeling pipeline, ensuring uniform gradient flow and
reducing the potential for both overfitting and numerical drift. Fully differentiable architectures have
become foundational to modern GNN‐based IAPs, replacing fixed structural descriptors with highly
parameterized functions that are optimized end-to-end via stochastic gradient descent. By enabling gradient
signals to propagate through every model component, this paradigm avoids arbitrary handcrafted features
and ensures that learned representations adapt dynamically to diverse atomic environments. The DeepMind
Allegro framework exemplifies this strategy, leveraging fully differentiable message‐passing layers and
[52]
learned radial functions to accelerate convergence and enhance both energy and force accuracy . End-to-
end differentiability thus underpins improved training efficiency, model adaptability and generalizability
across complex materials systems.

A recent paradigm, termed effective MD, offers a compelling route to reconcile the trade‐off between
fidelity and throughput in atomistic simulations . In this framework, high‐accuracy AIMD data are used to
[53]
parameterize streamlined force‐evaluation kernels via tailored optimization routines. By distilling complex
force‐field representations into efficient surrogate models, effective MD delivers robust dynamical
trajectories with significantly lower computational cost than full AIMD, while keeping predictive accuracy.
This approach thus exemplifies how judicious integration of high‐fidelity training data and optimized
model architectures can extend the reach of IAPs to time‐ and length‐scales previously accessible only to
classical MD.

In all cases, a careful balance between computational expense and predictive accuracy is essential. SOTA
equivariant and multi-fidelity GNN IAPs have pushed the limits of energy and force prediction accuracy
across a breadth of materials and molecular systems. NequIP demonstrates exceptional data efficiency,
achieving sub-meV energy errors and force errors on the order of tens of meV/Å in a complex reactive
environment . CHGNet, pretrained on over 1.5 M DFT trajectories, reaches energy MAEs below 30 meV/
[14]