Institute for Advanced Materials Research Press
Journals
Search
Submit
Contact
Login
Register
Institute for Advanced Materials Research Press
Search for journals and articles
Fast access to articles, journals, authors & more
Browse journals
Explore journals and articles by subject
Search and explore the millions of quality, peer-reviewed journal articles published under the Taylor & Francis, Routledge and Dove Medical Press imprints.
All Journals
Trending research
Review | Open access
Representation Learning for Materials Microstructures — Conceptual Advances and Interpretability Challenges: A Review Study
Interpretability, Inverse design, Representation learning, Materials microstructures, Deep learning, Structure-property relationships
The field of materials science has witnessed a transformative shift with the advent of representation learning techniques, particularly for analyzing complex microstructures. This review synthesizes recent conceptual advances in representation learning, including deep neural networks, autoencoders, and vision transformers, applied to microstructure data for tasks such as property prediction, inverse design, and evolution modeling. We explore how these methods extract latent features from high-dimensional microstructure images, enabling efficient computation and discovery of structure-property relationships. However, interpretability remains a significant challenge, as black-box models often obscure the physical meaning of learned representations, hindering trust and scientific insight. We discuss strategies for enhancing interpretability, such as attention mechanisms, heat maps, and post-hoc explanations, drawing from recent studies in alloy microstructures and additive manufacturing. The review highlights the integration of domain knowledge to disentangle representations and address data scarcity issues. By examining case studies in metals, ceramics, and composites, we identify gaps in current approaches, including bias in learned features and limited generalizability across materials classes. Ultimately, this review aims to guide future research toward interpretable representation-learning frameworks that accelerate materials design and foster a deeper understanding of microstructural phenomena.
Published: 18 January 2026
Original Research | Open access
The Problem of Normative Assumptions in Materials AI Objective Functions
Materials AI, Objective functions, Normative assumptions, Value-laden optimization, Hidden normativity, Value monoculture
Objective functions in materials artificial intelligence are routinely presented as neutral computational devices that merely minimize formation energy or maximize ionic conductivity. Yet, they quietly embed normative assumptions about what constitutes a “good” material, thereby encoding ethical, social, and scientific value judgments that shape downstream discovery pathways. These functions operate as value vehicles by translating ostensibly descriptive metrics into prescriptive targets that privilege certain outcomes—stability over metastability, efficiency over sustainability—while rendering competing priorities invisible. This critique identifies four interlocking problems: the hidden normativity concealed within technical loss functions, the resulting value monoculture that narrows the space of desirable materials, the measurability trap that biases discovery toward easily quantified properties, and the democratic deficit that excludes affected stakeholders from objective formulation. The consequences of these unexamined assumptions include narrow and path-dependent discovery trajectories, unresolved value conflicts, and a systematic exclusion of societal considerations from materials innovation. Alternative approaches are therefore proposed that treat objective design as an explicit exercise in value articulation, multi-objective negotiation, and participatory governance, thereby transforming materials AI from a value-blind optimizer into a reflexive, value-aware sociotechnical practice.
Published: 18 July 2025
Original Research | Open access
Benchmarking Without Illusion: A Conceptual Critique of Performance Comparisons in Materials AI
Materials AI, Interaction dynamics, Epistemic reasoning, Conceptual frameworks, Benchmarking illusions, Performance critique
In the rapidly evolving field of applied artificial intelligence (AI) for materials science, benchmarking serves as a cornerstone for evaluating model performance and guiding research trajectories. However, this paper advances a conceptual critique that unveils the inherent illusions embedded within conventional performance comparisons, which often obscure the nuanced realities of materials discovery and prediction. By synthesizing recent literature, we highlight how benchmarking practices can perpetuate misconceptions about model efficacy, generalizability, and alignment with real-world materials challenges. The critique centers on the interaction dynamics among data representations, evaluation metrics, and contextual factors, revealing feedback structures that amplify epistemic distortions. We propose a novel conceptual framework that reinterprets benchmarking as a multi-layered system of steering logics, in which trade-offs among precision, robustness, and interpretability shape the interpretive landscape of AI-driven insights into materials. This framework emphasizes systems-level insights into how illusory superiority emerges from mismatched expectations and overlooked interdependencies. Through analytical implications, we explore how recalibrating these dynamics could foster more transparent and ethically grounded performance assessments. Ultimately, the paper advocates for an integrative approach that prioritizes conceptual interpretations over superficial metrics, offering epistemic reasoning to navigate the complexities of materials AI without succumbing to benchmarking illusions. This conceptual reevaluation has the potential to refine the field's theoretical underpinnings, promoting advancements that are both innovative and reliable.
Published: 18 July 2025
Review | Open access
Artificial Intelligence as a Co-Scientist in Materials Science: From Pattern Recognition to Self-Driving Laboratories
Artificial intelligence, Materials science, Self-driving laboratories, Co-scientist, Pattern recognition, Generative AI
Artificial intelligence is rapidly moving beyond its early role as a pattern-recognition and predictive-modelling tool in materials science. What began as an acceleration strategy for screening known datasets is now becoming a broader transformation of how materials hypotheses are generated, tested, and refined. The central problem is that this transformation is often described in fragments: predictive models in one literature, generative design in another, physics-informed learning in another, and autonomous laboratories in yet another. A unified conceptual synthesis is needed to explain how these streams collectively move AI from passive assistant to active scientific collaborator. This integrative review traces the evolution of AI in materials science from 2017 to 2026. It frames the field through the idea of the AI co-scientist: an intelligent system that can recognise patterns, propose candidates, incorporate physical constraints, select experiments, and learn from feedback. The review integrates 31 peer-reviewed articles spanning materials informatics, machine learning, generative AI, inverse design, physics-informed modelling, active learning, autonomous experimentation, and self-driving laboratories. It does not present new empirical data, meta-analysis, or bibliometric mapping. The synthesis identifies four major evolutionary stages: pattern recognition, generative design, physics-integrated AI, and autonomous experimentation. These stages are not isolated phases but mutually reinforcing capabilities that increasingly connect computation, synthesis, characterisation, and human judgement. The review concludes that AI is becoming a genuine partner in materials discovery, but this transition depends on trustworthy data infrastructure, interpretable models, robust experimental integration, and new norms for human–AI collaboration. The co-scientist paradigm offers a forward-looking framework for understanding how materials science may be reorganised around closed-loop intelligence.
Published: 18 January 2026
Support and guidelines
Authors
Essential guidance and support for authors.
For authors
Editors
Essential guidance and support for editors.
For editors
Reviewers
Essential guidance and support for reviewers.
For reviewers
Librarians
Essential guidance and support for librarians.
For librarians
Open access journals and publishing
Featured journals
Institute for Advanced Materials Research Press