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Artificial intelligence (AI) is increasingly positioned as a design partner in materials optimization, enabling accelerated exploration of vast composition–processing–structure spaces under multiple, often conflicting, targets. Yet sustainability-centered materials design is not simply a larger version of multi-property optimization: it requires negotiating trade-offs across heterogeneous objective types such as performance, cost, safety, emissions, toxicity, circularity, and resource criticality, while accounting for lifecycle shifts and stakeholder-dependent priorities. Many current AI-enabled optimization workflows implicitly treat trade-offs as static Pareto-front problems with stable objective meanings and fixed feasibility boundaries. This conceptual manuscript argues that such assumptions are structurally incompatible with sustainable materials decisions, which involve trade-offs that are contextual, value-weighted, and regime-dependent. We introduce a novel theoretical framework—Trade-Off Sensitivity Theory (TOST)—which models sustainability optimization as a decision process governed by objective incompatibility geometry, lifecycle constraint migration, uncertainty-to-consequence coupling, and preference volatility. Rather than proposing algorithms or empirical evaluation, TOST provides a theoretical map linking Pareto efficiency to sustainability legitimacy through three layers: objective semantics, trade-off sensitivity, and action admissibility. The framework clarifies when AI outputs support responsible selection, when optimization is ill-posed, and how sustainable decisions can be justified under conflicting criteria.

In AI-driven materials design, the formulation of objective functions fundamentally shapes innovation trajectories by defining priorities across vast design spaces. This conceptual manuscript examines how optimization targets steer the discovery and refinement of materials, shaping emergent properties, scalability pathways, and integration into technological and societal systems. By synthesizing advancements in surrogate modeling, Bayesian optimization, generative architectures, and multi-objective strategies from recent literature, the analysis shows that single-objective formulations often constrain exploration to narrow performance peaks. In contrast, multi-objective configurations introduce intricate interaction dynamics, trade-offs, and feedback structures that diversify possible material outcomes. The proposed objective function nexus (OFN) framework conceptualizes objective functions as an interconnected system in which primary metrics, auxiliary constraints, weighting schemes, and iterative evaluations create steering logics that channel computational effort toward distinct horizons—ranging from high-performance specialized materials to scalable, sustainable alternatives. Analytical implications underscore nonlinear effects arising from objective interactions, such as the amplification of certain property clusters at the expense of others. At the same time, systems-level insights reveal how these choices encode epistemic priorities and value-laden selections. Trade-offs between competing goals, including performance versus manufacturability or cost versus environmental impact, manifest as dynamic tensions that reshape accessible design spaces over iterative cycles. By interpreting these dynamics interpretively, the framework illuminates how objective function design not only navigates but actively sculpts the futures of materials science, inviting reflective consideration of the priorities embedded in optimization practices.