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Scientific path abandonment has emerged as a critical yet underrecognized failure mode in AI-guided materials research, in which promising research directions—such as novel compositional families, structural motifs, or synthesis routes—are terminated prematurely due to insufficient evidence, narrow optimization signals, or algorithmic impatience. This failure mode is defined as the termination of a research direction before sufficient evidence has been gathered to determine its true promise, distinguishing it from rational stopping grounded in conclusive data. The mechanisms driving this abandonment include algorithmic impatience that halts exploration upon short-term metric plateaus, overconfidence in negative predictions, narrow optimization that sacrifices multi-objective potential, and exploration decay inherent in active learning loops. Four distinct types of path abandonment—compositional, structural, synthesis, and property—each generate specific failure modes, such as local optima traps, false-negative cascades, exploration starvation, and regret amplification. Detection principles center on systematic audits, counterfactual reasoning, diversity monitoring, and regret tracking. In contrast, mitigation principles emphasize extended exploration, resource reserves, delayed abandonment thresholds, path revisitation, and regret-aware stopping rules. By articulating this failure mode and offering a comprehensive framework for recognition and remedy, the analysis identifies scientific path abandonment as a systemic risk that undermines the very autonomy and discovery potential that AI promises to deliver in materials science.