Search

The integration of multi-model and hybrid artificial intelligence (AI) systems has revolutionized materials research by enabling the efficient analysis of complex datasets, the prediction of material properties, and the optimization of design processes. This narrative review examines the architectures of these systems, including ensemble methods, multimodal data fusion, and physics-informed neural networks. It evaluates their applications in areas such as alloy design, nanomaterial synthesis, and battery management. Key trade-offs are discussed, encompassing computational efficiency versus predictive accuracy, data scarcity versus model generalizability, and interpretability versus performance in black-box models. Drawing on recent peer-reviewed literature, the review highlights how these AI approaches accelerate materials discovery while addressing challenges such as uncertainty quantification and scalability. By synthesizing current advancements, this work underscores the potential of hybrid AI to drive sustainable innovation in materials science, with implications for future interdisciplinary research.

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.

Multi-fidelity modeling has become an indispensable paradigm in artificial intelligence for materials science, offering a structured way to integrate data from simulations of varying computational expense and accuracy to accelerate the discovery and optimization of novel materials while mitigating the prohibitive costs associated with high-fidelity methods alone. This review systematically examines the conceptual foundations, underlying assumptions, and inherent trade-offs of multi-fidelity approaches through a targeted analysis of 30 peer-reviewed publications published between 2017 and 2023, identified via a rigorous literature search across databases such as Web of Science and Scopus that employed the exact search strings specified in the reference discovery protocol. The conceptual foundations rest on the hierarchical organization of fidelity levels, wherein low-fidelity models deliver rapid, broad-coverage approximations that serve as scaffolds for correction and refinement by higher-fidelity calculations through surrogate-based information transfer, thereby enabling efficient navigation of high-dimensional material design spaces. Key assumptions—such as the presence of meaningful correlation and smoothness between fidelity outputs, as well as linearity in the mapping between them—are scrutinized alongside the trade-offs they impose between computational cost, predictive accuracy, generalization capacity, and uncertainty handling. Methods ranging from Gaussian process co-kriging to neural network transfer learning are conceptually surveyed for their role in bridging fidelity gaps. At the same time, materials-specific applications in alloys, polymers, and interfaces illustrate both demonstrated successes and context-dependent limitations. Significant gaps persist in the literature, notably the infrequent validation of core assumptions and the absence of standardized benchmarks for multi-fidelity tasks, prompting recommendations for explicit assumption testing, quantitative trade-off reporting, and community-driven development of open benchmarks and reporting standards to elevate the rigor of multi-fidelity materials AI. Through this structured examination, the review underscores that while multi-fidelity frameworks hold transformative potential, their conceptual maturity requires sustained critical attention to assumptions and trade-offs if they are to support next-generation materials innovation reliably.

The field of computational and data-driven materials engineering has witnessed a paradigm shift toward accelerated discovery pipelines, leveraging machine learning and high-throughput computations to navigate vast materials spaces. However, this emphasis on speed often comes at the expense of epistemic depth, where understanding of underlying mechanisms is sidelined by predictive efficiency. This manuscript introduces a conceptual framework that examines the inherent trade-offs between discovery acceleration and epistemic comprehension in computational design ecosystems. By integrating insights from materials informatics, representation learning, and uncertainty quantification, we propose a systems-level architecture that balances rapid iteration with interpretive rigor. The framework delineates how data infrastructures, model architectures, and feedback loops influence the speed–understanding continuum, highlighting computational steering logics that mitigate epistemic risks without compromising efficiency. Implications extend to autonomous discovery systems, inverse design strategies, and multimodal datasets, fostering more resilient AI-guided materials engineering. Ultimately, this approach advocates for hybrid paradigms where acceleration serves as a scaffold for deeper mechanistic insights, potentially transforming how computational tools are deployed in materials research.