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The term “forgetting” appears throughout the materials artificial intelligence literature in multiple, often contradictory senses: as a catastrophic failure that destroys previously acquired knowledge of structure–property relations, as an unexamined side effect of data deletion or replay buffer limits, and occasionally as an implicit consequence of model capacity constraints. This conceptual ambiguity impedes precise communication, obscures design decisions, and prevents the field from treating forgetting as a controllable parameter rather than an inevitable defect. The present boundary/definitional paper proposes a precise definition of algorithmic forgetting as a deliberate design choice, distinct from both catastrophic forgetting and passive capacity limits. It distinguishes algorithmic forgetting from five nearby concepts—catastrophic forgetting, data deletion, privacy preservation, capacity saturation, and regularization-induced compression—by clarifying intent, mechanism, epistemic consequences, and reversibility. The paper further articulates the conditions under which forgetting becomes beneficial (adaptation to distribution shift in experimental data streams, selective retention under resource constraints, and controlled deletion for intellectual property or safety) versus harmful (loss of rare but physically valid examples). Finally, it supplies a materials-specific conceptual framework for deciding what to forget and what to retain, grounded in rarity, recency of validation, and relevance to the current search space. By reframing forgetting as an explicit design lever, this analysis offers materials AI practitioners a shared vocabulary and a systematic approach to engineering memory policies that enhance rather than undermine long-term scientific utility.

Sequential learning is increasingly invoked in compositionally complex alloy modeling because the combinatorial scale of alloy design space, coupled with the cost of density-functional theory calculations and the staged nature of data acquisition, makes exhaustive one-shot training impractical. Yet under these conditions, neural networks are prone to catastrophic forgetting, whereby adaptation to newly introduced compositions, phases, or thermodynamic regimes degrades performance on previously learned ones. This article examines the conditions under which such failure emerges in compositionally complex alloys and shows that forgetting is not incidental but structurally induced by the interaction of shared representations, non-linear property landscapes, high compositional dimensionality, and sparse task-specific data. The analysis identifies four recurrent failure modes—elemental forgetting, concentration range collapse, phase space amnesia, and temperature-induced overwrite—and links them to underlying mechanisms of parameter interference, representation drift, output distribution shift, and gradient conflict. It further delineates the conditions that intensify these dynamics, including high compositional similarity, abrupt task transitions, rehearsal-free updates, and unstable optimization settings. By integrating detection criteria with mitigation strategies such as elastic weight consolidation, memory replay, modular architectures, and curriculum sequencing, the study provides a practical and conceptual framework for continual learning in alloy discovery. The central argument is that robust sequential learning in compositionally complex alloys requires forgetting-aware design at the levels of evaluation, architecture, and training protocol if materials machine learning is to support reliable lifelong modeling across expanding compositional spaces.

Lifelong learning, also known as continual or sequential fine-tuning, promises machine-learning models for materials science that continuously improve as new data streams arrive. In theory, a model trained on binary-alloy formation energies should seamlessly incorporate new knowledge about ternary systems, experimental band gaps, or updated DFT databases without losing prior accuracy. Yet in practice, lifelong learning systematically fails for materials property prediction. Sequential fine-tuning triggers catastrophic interference: the model rapidly forgets earlier tasks while attempting to master new ones. This failure mode analysis identifies four core failure modes that are especially destructive in the materials domain. Catastrophic forgetting occurs when shared parameters encoding old task knowledge are overwritten during fine-tuning on new tasks. Negative interference arises when conflicting signals from different data sources force the model into an impossible compromise. Representation overwriting destroys generic feature detectors in early layers that were useful across multiple materials tasks. Task imbalance allows later, larger, or easier datasets to dominate the optimization process, marginalizing earlier knowledge. The root causes are intrinsic to materials science: massive domain shifts between compositional spaces, overlapping but non-identical feature distributions, and the optimizer’s inability to preserve multi-task optima in a shared parameter space. These problems are documented across recent studies of machine-learning interatomic potentials, continual learning in mechanical networks, and sequential task learning on evolving materials databases. This paper provides precise detection signatures for each failure mode and outlines mitigation principles that can be applied immediately by practitioners. By exposing why sequential fine-tuning collapses in materials property prediction, the analysis lays the foundation for genuinely lifelong materials models that accumulate knowledge rather than repeatedly erasing it.