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Materials acceleration—the compression of materials discovery timelines through automated experimentation and data-driven decision loops in self-driving laboratories (SDLs) and materials acceleration platforms (MAPs)—is reshaping contemporary materials science. While widely promoted for its efficiency and sustainability potential, accelerated discovery also introduces ethical tensions that remain insufficiently theorized. This conceptual paper develops a novel framework to analyze how acceleration restructures ethical challenges across five interdependent dimensions: sustainability, labor, dual-use risk, inequity, and governance. Drawing exclusively on peer-reviewed literature published, the analysis shows that compressed timelines and autonomous decision loops function as ethical multipliers, intensifying trade-offs rather than resolving them. Claimed computational and infrastructural burdens often offset sustainability gains; automation reconfigures scientific labor and risks epistemic deskilling; accelerated optimization amplifies dual-use vulnerabilities; access asymmetries widen global research inequities; and existing governance mechanisms lag behind acceleration velocity. To integrate these dynamics, the paper introduces the ethical acceleration tension matrix. This multidimensional framework models feedback interactions and identifies leverage points for ethical steering under conditions of speed and autonomy. By foregrounding interdependence, feedback velocity, and equilibrium steering—without recourse to empirical data—this work provides a foundational conceptual logic for responsible acceleration in applied artificial intelligence for materials science. Implications are outlined for platform design, governance, and education to align innovation velocity with societal safeguards.