Materials and Design is a top-tier journal that welcomes high-quality submissions from diverse disciplines in the materials’ world. Our focus is on exploring the correlations among structure, property, and processing of inorganic and organic materials, through innovative and proactive design. With an interdisciplinary approach, the journal connects materials science, engineering, physics, chemistry, biology, and emerging fields like Artificial Intelligence and advanced data science.

We encourage submissions that offer ground-breaking insights into the multi-scale architecture and function of materials, showcase technological advancements, and demonstrate connections between synthesis and properties, experiment and simulation across the scales. By publishing outstanding research articles, reviews, and short communications, Materials and Design aims to advance the field of materials science and engineering, fostering collaboration and shaping the future of materials design.

Special Issue on In-line metrology, design optimization and material development in additive manufacturing
Día de Entrega: 2024-12-31

In the present SI, alongside laser-, electron- and arc-based manufacturing, contributions are sought on all 'flavours' of AM, including Fused Filament Fabrication (FFF), 3D bio-printing, ink-jetting, and stereolithography.

Guest editors:

J. P. Oliveira,
UNIDEMI, Departamento de Engenharia Mecânica e Industrial, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal

Xu Song,
The Chinese University of Hong Kong, Hong Kong / Editor, JMADE

Alexander M. Korsunsky,
University of Oxford, UK / Editor-in-Chief, JMADE

Special issue information:

Dear colleagues,

Given the high number of submissions and requests for deadline extension, the Editors of the Special Issue entitled “In-line metrology, design optimization and material development in additive manufacturing” have taken the decision to keep this SI open for submissions until Dec 31, 2024.

Additive Manufacturing (AM) remains a rich and rapidly developing theme in Materials & Design. From the initial focus in this field having been placed on achieving the desired optimal shape, the attention has now moved to controlling material microstructure and mechanical properties, as well as residual stress, in order to underpin structural integrity and performance. In the present SI, alongside laser-, electron- and arc-based manufacturing, contributions are sought on all 'flavours' of AM, including Fused Filament Fabrication (FFF), 3D bio-printing, ink-jetting, and stereolithography.

To date, 168 full papers have been published. The following three articles provide an indicative selection:

    “Selective laser melting of hybrid ex-situ/in-situ reinforced titanium matrix composites: Laser/powder interaction, reinforcement formation mechanism, and non-equilibrium microstructural evolutions” (DOI: 10.1016/j.matdes.2019.108185) by E. Fereiduni et al. Here, the authors produced Ti-6Al-4V parts reinforced with B4C particles through selective laser melting. B4C particle dissolution was found to be dependent on the energy density which resulted in different microstructures in the produced parts. A consistent increase in microhardness was observed upon the introduction of B4C particles.​
    “Obtaining large-size pyramidal lattice cell structures by pulse wire arc additive manufacturing” (DOI: 10.1016/j.matdes.2019.108401) by T. Xu et al. In this work, wire arc additive manufacturing was used to build unsupported large lattice-like cell structures. The authors showed that multiple process variables such as feed direction, heat input and droplet force greatly influence the quality of the produced parts. An optimized approach was demonstrated, opening new possibilities for the use of wire arc additive manufacturing in key engineering applications.
    “High-throughput synthesis of Mo-Nb-Ta-W high-entropy alloys via additive manufacturing” (DOI: 10.1016/j.matdes.2019.108358) by M. Moorehead. Two topics of major relevance were addressed in this paper: additive manufacturing and high entropy alloys. Directed energy deposition was used as a high-throughput method to evaluate multiple composition spaces. A comprehensive microstructural characterization supported by thermodynamic calculations stresses the importance of additive manufacturing as a potential tool for the development of novel alloy systems.

In view of the rich range of contributions attracted by this SI, the Editors invite all interested researchers in this field to contribute their further outstanding results to this special issue.

Manuscript submission information:

The journal’s submission platform (Editorial Manager®) is available for receiving submissions to this Special Issue. Please refer to the Guide for Authors to prepare your manuscript, and select the article type of "VSI: Additive Manufacturing" when submitting your manuscript online. Both the Guide for Authors and the submission portal could be found on the Journal Homepage https://www.sciencedirect.com/journal/materials-and-design

All the submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers. Upon its editorial acceptance, your article will go into production immediately. It will be published in the latest regular issue, while be presented on the specific Special Issue webpage simultaneously. In regular issues, Special Issue articles will be clearly marked and branded.

Submission Deadline: Dec 31, 2024

Special Issue on Artificial Intelligence-Enabled Material Innovations
Día de Entrega: 2025-05-31

The introduction and integration of Artificial Intelligence (AI) within materials discovery, development, and deployment have opened new frontiers in modeling, measuring, and mastering materials. This Special Issue of Materials & Design is entitled “AI-Enabled Material Innovations” and aims to define and explore the transformative role of AI across various materials science domains. It charts how these approaches revolutionize materials discovery, characterization, microstructural analysis, multiscale modeling, and data-driven insights.

Guest editors:

Enrico Salvati, University of Udine, Polytechnic Department of Engineering and Architecture (DPIA)
Email: enrico.salvati@uniud.it

Edoardo Rossi, Università degli Studi Roma Tré, Department of Civil, Computer Science and Aeronautical Technologies Engineering
Email: edoardo.rossi@uniroma3.it

Alexander M. Korsunsky, University of Oxford, Trinity College
Email: alexander.korsunsky@eng.ox.ac.uk

Special issue information:

This special issue focuses on the intersection of Artificial Intelligence (AI) and Material Science, inviting submissions that leverage AI to push the field's boundaries. We seek studies that use AI, whether through innovative or established tools, to uncover new insights and methodologies in Material Science, advancing the state of the art. The goal is to highlight research where AI supports and significantly enhances our understanding and development of materials. Materials Science is hallmarked by several peculiar concepts and interconnections, such as the concept of Representative Volume Element (RVE), typical microstructure, the combination of highly disparate but complementary experimental characterization tools that generate big data volumes, the need for multi-physics, multiscale hierarchical analysis of the structure and internal fields: amorphous and crystal atomic arrangements and electron density fields, short to long-range order and/or disorder, valence and stress-strain states, phase transitions, etc. Therefore, the infusion of AI across these various facets has engendered a paradigm shift in how materials can be designed, characterized, and modeled. This perspective offers novel methodologies for understanding, predicting, and optimizing material properties and behavior. The following summary delineates Machine Learning (ML) and Artificial Intelligence (AI) state-of-the-art impact and applications within three critical categories. It outlines the guidelines for the research to be included in the special issue: (i) Materials Design and Synthesis, (ii) Materials Characterization, and (iii) Materials Simulation and Modeling.

(i) Materials Design and Synthesis

Artificial Intelligence (AI) in materials design and synthesis could facilitate the tailored optimization of materials with desired properties by efficiently exploring vast compositional and configurational spaces to identify materials that might have remained uncharted using traditional methods. AI is expected to predict properties and suggest new structures for a more efficient, data-driven approach to materials design and synthesis.

Protocols, including neural and graph networks, decision trees, and evolutionary algorithms, play a central role in this transformation by significantly reducing the time, data size, and computational effort needed to identify promising material candidates. These algorithms can accurately predict material properties by leveraging existing datasets.

A hallmark of AI's transformative power in this field is its success in predicting the multiscale properties–performance paradigm for materials, such as the strength, stiffness, fracture toughness, fatigue, design of thermo-chemical-mechanical processing, etc. Deep learning, synergistically trained on extensive material datasets and physics-based models, predicts mechanical behaviors, optimizing the design process for novel materials.

The application of AI, notably in the design of High Entropy Alloys (HEAs) and High Entropy Ceramics (HECs), exemplifies the scopes of the proposed category. Machine Learning (ML) techniques - a sub-family of AI - such as Artificial Neural Networks (ANN) and Support Vector Machines (SVM) are pivotal in predicting phase formations in HEAs, leveraging descriptors like atomic size difference, mixing entropy, and valence electron concentration (VEC). For HECs, the extrapolation of single-phase formations utilizes parameters such as enthalpy of mixing and electronegativity, broadening the possibilities for engineering materials with tailored properties. Integrating Genetic Algorithms (GAs) with ML models furthers the ability to pinpoint alloys with superior hardness.

Deep learning, mainly through Convolutional Neural Networks (CNNs), extends to optimizing service behavior, facilitating multi-objective optimizations, and the intricate task of inverse design, where desired outcomes guide the creation of new material systems (metamaterials) through topology optimization and the interpolation of improved mechanical properties.

(ii) Materials Characterization

In materials characterization, AI technologies have shown exceptional promise in automating and enhancing the analysis of complex material structures and compositions, allowing researchers to sieve through large volumes of complex experimental data and reveal the underlying trends and interrelations, providing insights that might otherwise remain obscured. At the core of the applications, AI enables the interpretation of complex data generated from various characterization techniques, ranging from microscopy imaging to mechanical testing, such as nanoindentation. Techniques such as CNNs for advanced image analysis, unsupervised learning for pattern discovery in unlabeled datasets, and image segmentation techniques have become essential for in situ high-throughput characterization, enabling detailed real-time analysis of microstructures. Transfer learning, hybrid models that blend physical theories with ML algorithms, and generative models like Generative Adversarial Networks (GANs) and Variational autoencoders (VAEs) for synthetic data generation collectively streamline the characterization process, offering significant advancements over traditional methods.

The potential of ML in automating and refining microstructure characterization has been notably demonstrated, with methodologies (e.g., feature extraction, dimensionality reduction, etc.) capable of accurately classifying complex microstructures and reducing the need for extensive expert intervention. Unsupervised ML techniques have further advanced the capability for comprehensive 3D microstructural characterization, employing a blend of topology classification, image processing, and clustering algorithms to elucidate the internal structures of materials without prior knowledge.

Integrating ML with nanoindentation techniques signifies a leap toward developing hybrid models that combine physical theories with ML algorithms. This approach, aiming at the inception of Scientific Machine Learning (SciML), promises to elevate the precision of materials characterization and accelerate the discovery of new materials.

Deep neural networks have been applied for microscopy image analysis, enhancing the generalization of models and leading to more targeted and efficient data generation for materials analysis.

Gaussian Process Regression is another ML technique that captures complex dataset structures and provides uncertainty estimates and predictions. It is beneficial for regularizing experimental data and inference purposes, even when dealing with small datasets.

Micro-scale characterization and the study of phase transformations have particularly benefited in this regard, as AI/ML models have been able to learn from vast complex datasets to predict outcomes at an unprecedented scale and speed. This is further exemplified by computational tools developed for predicting grain boundary structures, uncovering previously unrecognized polymorphism in materials.

(iii) Materials Simulation and Modeling

Integrating AI into materials simulation and modeling introduces advanced techniques for navigating the complexities of atomistic, molecular dynamics, and thermodynamical simulations. It is worth noting that, at the present stage, the application of AI/ML in the field has been predominantly confined to the realm of first principle calculations using DFT, particularly the development of ML interatomic potentials. At the same time, other areas have seen more limited advances. However, the potential use of AI could enhance computational efficiency, facilitating the simulation of intricate phenomena across broader temporal and spatial scales at a lower cost, offering a solution to the gap between the need for quantum mechanical precision and the demand for large-scale, rapid calculations. A crucial element of this integration is the development of advanced machine-learned potentials and deep-learning models, which surpass conventional computational strategies by harmonizing accuracy with computational speed. To this scope, one of the potential growing roles of ML in atomic-scale modeling is represented by the promotion of research automation, where machine-learned models propose new experiments or simulations, fostering a cycle of data generation and model refinement. A notable outlook is given by the recent application of ML in computational alloy modeling: from constructing model-Hamiltonians to a data-centric approach, addressing queries on single material systems and broad inquiries across extensive datasets.

Another field of development foresees the creation of algorithms capable of predicting properties, classifying materials, and aiding in the creation of phase diagrams with an efficiency previously unattainable. In this field, Bayesian optimization effectively optimizes the experimental process by combining uncertainty sampling with active learning to expedite discovery in multi-component phase diagrams.

The potential topics of interest are (but not limited to):
Integrating AI/ML approaches into Materials Science and Engineering is causing a major disruptive transformation that can be understood by visualizing a tetragonal framework encompassing the space between four fundamental nodes: experimental characterization, numerical modeling, engineering design, and technological application. Today, one may witness significant activity around each of these nodes that we wish to capture in the Virtual Special Issue:

A. Artificial Intelligence Applied to the Design of New Materials:

· AI for predictive By-design Materials: using AI for predictive analytics (e.g., high-entropy alloys novel formulations) by deciphering intricate data patterns linking thermodynamics and microstructures to mechanical properties, streamlining the extrapolation of new materials with tailored functionalities.

· Generative Design and Topology Optimization through Deep Learning: employing deep learning for generative design processes through the intricate tasks of inverse design and topology optimization. This encapsulates multi-objective optimizations and the creation of new material systems with improved mechanical, electrical, and thermal properties, guiding the development of novel materials through a data-driven approach.

B. Artificial Intelligence Applied to Materials for New Technologies:

· AI-Driven Development of Smart Materials: AI algorithms to design and develop smart materials that can adapt their properties in response to external stimuli (e.g., environmental), applicable in sectors like aerospace, automotive, and wearable technology, enhancing performance and sustainability.

· AI-Driven Material Selection for Technological Innovation: AI and ML tools streamline the selection of materials for new technologies by analyzing data to identify materials that meet specific application requirements.

· AI identification of new functional/mechanical property combinations for novel applications, such as nano-energy, triboelectric generators, and thermoelectric and photovoltaic systems.

· Optimizing Material Properties for Specific Applications with ML: determine the desired property combinations for a given application, which can be achieved by composite or material-by-design route.

C. Artificial Intelligence Applied to Materials Characterization

· Material Property Correlations from location-based data visualization: examples as image segmentation and deep learning approaches to analyze material microstructural and morphological features to deduce the overall material mechanical properties from combined areal datasets.

· AI for Microstructural Characterization: Employing AI by leveraging statistically significant datasets for detailed characterization of material microstructures, how they impact the overall material response, and for the identification of crucial features (i.e., statistical machine learning: unsupervised and supervised clustering, etc.) and multiscale effects.

· AI for Uncertainty Quantification: enriching experimental evaluations with detailed uncertainty quantification, including errors originating from regularization processes.

· ML for beyond-analytical-limits in Material Characterization: extraction of localized non-readily accessible material properties through data-driven insights (e.g., deep learning for phase transformation during nanoindentation, elastic constants for crystal planes, crystal plasticity).

D. Artificial Intelligence Applied to Materials Modelling

· Predictive Modeling for Materials Properties: applying supervised learning techniques to predict various materials properties based on historical data.

· AI in Multiscale Materials Modeling: demonstrating the use of AI in modeling material behavior across different scales, from atomic to macroscopic levels.

· Reduced order modeling: for fast evaluation of material performance and optimization purposes.

· AI probabilistic methodologies: to account for uncertainties and other errors in material modeling.

The Guest Editors seek contributions highlighting AI and ML's unique capabilities in handling large datasets, uncovering complex patterns, and efficiently exploring vast compositional and configurational spaces in materials science. Moreover, articles that particularly seek to encompass synergies between the four nodes ultimately demonstrate how AI/ML solutions can realize a comprehensive (beyond the state-of-the-art) breakthrough in materials science.

Manuscript submission information:

All manuscripts will be peer-reviewed following the standard practices of Materials & Design. For more information on Materials & Design, including the journal ranking and scores, publication policies, author guidelines, and publication charges, please refer to the journal website at https://www.sciencedirect.com/journal/materials-and-design.Please submit your manuscripts using the Editorial Manager web portal: https://www.editorialmanager.com/jmade and select the appropriate option VSI: AI Materials in the website pull-down menu at the time of submission.

Submission deadline: 31st May 2025

Keywords:

Artificial Intelligence; Machine Learning; Materials Design; Materials Characterization; Materials Modeling; Data-Driven Science; Multiscale Modeling

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