Announcements

MDPI is delighted to announce the establishment of the Michele Parrinello Award. Named in honor of Professor Michele Parrinello, the award celebrates his exceptional contributions and his profound impact on the field of computational physical science research.

The award will be presented biennially to distinguished scientists who have made outstanding achievements and contributions in the field of computational physical science—spanning physics, chemistry, and materials science.

About Professor Michele Parrinello

"Do not be afraid of new things. I see it many times when we discuss a new thing that young people are scared to go against the mainstream a little bit, thinking what is going to happen to me and so on. Be confident that what you do is meaningful, and do not be afraid, do not listen too much to what other people have to say.”

——Professor Michele Parrinello

Born in Messina in 1945, he received his degree from the University of Bologna and is currently affiliated with the Italian Institute of Technology. Professor Parrinello is known for his many technical innovations in the field of atomistic simulations and for a wealth of interdisciplinary applications ranging from materials science to chemistry and biology. Together with Roberto Car, he introduced ab initio molecular dynamics, also known as the Car–Parrinello method, marking the beginning of a new era both in the area of electronic structure calculations and in molecular dynamics simulations. He is also known for the Parrinello–Rahman method, which allows crystalline phase transitions to be studied by molecular dynamics. More recently, he has introduced metadynamics for the study of rare events and the calculation of free energies.

For his work, he has been awarded many prizes and honorary degrees. He is a member of numerous academies and learned societies, including the German Berlin-Brandenburgische Akademie der Wissenschaften, the British Royal Society, and the Italian Accademia Nazionale dei Lincei, which is the major academy in his home country of Italy.

Award Committee

The award committee will be chaired by Professor Xin-Gao Gong, a computational condensed matter physicist, academician of the Chinese Academy of Sciences, and professor at the Department of Physics, Fudan University. Professor Xin-Gao Gong will lead a panel of several senior experts in the field to oversee the evaluation and selection process. The Institute for Computational Physical Sciences at Fudan University (Shanghai, China), led by Professor Xin-Gao Gong, will serve as the supporting institute for the award.

"We hope the Michele Parrinello Award will recognize scientists who have made significant contributions to the field of computational condensed matter physics and at the same time set a benchmark for the younger generation, providing clear direction for their pursuit—this is precisely the original intention behind establishing the award."

——Professor Xin-Gao Gong

The first edition of the award was officially launched on 1 November 2025. Nominations will be accepted before the end of March 2026. For further details, please visit mparrinelloaward.org.

About the MDPI Sustainability Foundation and MDPI Awards

The Michele Parrinello Award is part of the MDPI Sustainability Foundation, which is dedicated to advancing sustainable development through scientific progress and global collaboration. The foundation also oversees the World Sustainability Award, the Emerging Sustainability Leader Award, and the Tu Youyou Award. The establishment of the Michele Parrinello Award will further enrich the existing award portfolio, providing continued and diversified financial support to outstanding professionals across various fields. 

In addition to these foundation-level awards, MDPI journals also recognize outstanding contributions through a range of honors, including Best Paper Awards, Outstanding Reviewer Awards, Young Investigator Awards, Travel Awards, Best PhD Thesis Awards, Editor of Distinction Awards, and others. These initiatives aim to recognize excellence across disciplines and career stages, contributing to the long-term vitality and sustainability of scientific research.

Find more information on awards here.

We are honored to recognize the 2024 MDPI Top 1000 Reviewers—scholars whose exemplary commitment to rigorous and constructive peer review is vital in upholding the highest standards of academic publishing.

Selected from a distinguished pool of 215,000 reviewers from 65 countries and regions worldwide, these honorees stand out for their exceptional expertise, diligence, and dedication to advancing research through timely and thoughtful reviews. Their constructive and impartial feedback ensures the publication of high-quality, impactful research, while their timely reviews facilitate swift revisions and faster publication of innovative work.

Peer review is the invisible foundation of academic progress. With gratitude and respect, we celebrate these 1000 scholars who made that foundation stronger in 2024. We respected all privacy preferences, with part of nominees opting for limited attribution.

The names of these reviewers are listed below in alphabetical order by first name:

The MDPI webinar “AI-Powered Material Science and Engineering” brings together leading experts to explore how artificial intelligence is accelerating the discovery, characterization, and modeling of advanced materials across different scales. AI-driven tools now enable researchers to predict material behavior, interpret complex structural data, and significantly increase the speed of innovation compared to traditional experimental methods. This webinar features Prof. Dr. Jian Feng Wang from City University of Hong Kong, an internationally recognized expert in the micro–macro mechanics of granular materials; his work integrates X-ray CT, discrete element modeling, and machine learning-based pattern recognition to reveal the multiscale physics governing soil behavior. Also joining is Prof. Dr. Stefano Mariani from the Polytechnic University of Milan, whose research spans the reliability of MEMS, structural health monitoring using machine learning and deep learning, advanced fracture simulations, and multiscale modeling, supported by extensive experience across international research institutions. Together, they will demonstrate how AI enhances understanding from particle-scale mechanics to complex structural systems.
MDPI has 115 journals under the subject of "Chemistry & Materials Science"; please click here for further details.

Date: 1 December 2025
Time: 8:00 a.m. CET | 3:00 p.m. CST
Webinar ID: 826 5862 3549
Webinar Secretariat: journal.webinar@mdpi.com
Webinar webpage: https://sciforum.net/event/HTWAI-1

Register now for free!

Time in CET

After registering, you will receive a confirmation email containing information on how to join the webinar. Registrations with academic or institutional email addresses will be prioritized. 

Unable to attend? Feel free to still register; we will inform you when the recording is available.

Webinar Chair and Keynote Speaker:

• Prof. Dr. Jian Feng Wang, Department of Architecture and Civil Engineering, City University of Hong Kong;
• Prof. Dr. Stefano Mariani, Department of Civil and Environmental Engineering, Politecnico di Milano.

 Relevant Special Issue:
“Artificial Intelligence and Machine Learning for Material Design, Discovery, and Optimization”
Guest Editors: Dr. Craig Hamel and Dr. Devin J. Roach
Deadline for manuscript submissions: 20 May 2026

AI-powered material science and engineering is a rapidly growing and highly popular research field at the intersection of artificial intelligence and materials innovation. By leveraging machine learning algorithms, AI accelerates the discovery, design, and optimization of new materials, significantly reducing time and costs compared with traditional trial-and-error methods. Researchers use AI to predict material properties, screen vast databases, and simulate complex behaviors under various conditions. This transformative approach is revolutionizing industries such as energy, electronics, and healthcare. With increasing investments and breakthroughs, AI-driven materials science is now a hotspot in both academia and industry, offering immense potential for sustainable and high-performance material development.

To advance this transformative frontier, we invite you to explore a curated collection of cutting-edge research articles, journals, and Special Issues spanning diverse domains within AI-powered material sciences and engineering, including intelligent materials design, autonomous experimentation, multiscale modeling, and sustainable materials innovation. By disseminating these breakthroughs, we aim to inspire, accelerate, and champion innovation in materials research, translating scientific discovery into collaborative dialog and real-world applications that will shape a more resilient and sustainable future.

Keynote Speakers:

Prof. Dr. Stefano Mariani Polytechnic University of Milan, Italy	Prof. Dr. Jian Feng Wang City University of Hong Kong, China

 Free to register for this webinar here!

Prof. Michele Parrinello is an Italian physicist particularly known for his work in molecular dynamics, the computer simulation of physical movements of atoms and molecules. To honor his enduring legacy in advancing computational science, MDPI is proud to establish the Michele Parrinello Award through the initiative of his former student, Prof. Xin-Gao Gong. This biennial international award recognizes senior researchers who have made outstanding contributions to computational physical sciences, encompassing physics, chemistry, and materials science with particular emphasis on pioneering contributions to foundational science.

Nomination deadline: 31 March 2026.

Prize:

• EUR 50000;
• An award medal and a certificate.

For more details about the award, please visit here.

We are honored to present a series of thought-provoking interviews with pioneering experts at the forefront of AI-powered materials science and engineering, as they share their transformative journeys and visionary insights on accelerating material discovery, innovation, and sustainable development across diverse scientific and industrial landscapes.

Name: Dr. Fernando Gomes de Souza Junior Affiliation: Universidade Federal do Rio de Janeiro, Brazil “Perhaps the work I am most proud of is the development of a unique, unprecedented scale for assessing the hazard of micro- and nanoplastics. No standardized global metric existed. We aggregated data from hundreds of articles—toxicity, size, shape, surface charge, chemical composition, environmental behavior—and trained an AI model to classify the relative risk of each particle type. This would have been impossible without AI.” Please read the full interview here.

Name: Dr. Pedro Morouço Affiliation: Polytechnic University of Leiria, Portugal “In my own work, AI has become the “glue” between biomechanics and biomaterials. Wearable-sensor and imaging data inform digital twins of tissues; surrogate models then explore scaffold designs that best support anticipated loads, healing profiles, or athlete-specific movement patterns. This has shortened iteration cycles (from weeks to days) when tuning lattice density, pore geometry, or printing paths to meet simultaneous targets like strength, compliance, and nutrient diffusion.” Please read the full interview here.

 “A Comprehensive Review of Machine-Learning Approaches for Crystal Structure/Property Prediction”
by Mostafa Sadeghian, Arvydas Palevicius and Giedrius Janusas
Crystals 2025, 15(11), 925; https://doi.org/10.3390/cryst15110925

“Synthetic Rebalancing of Imbalanced Macro Etch Testing Data for Deep Learning Image Classification”
by Yann Niklas Schöbel, Martin Müller and Frank Mücklich
Metals 2025, 15(11), 1172; https://doi.org/10.3390/met15111172

“Enhancing Biomedical Metal 3D Printing with AI and Nanomaterials Integration”
by Jackie Liu, Jaison Jeevanandam and Michael K. Danquah
Metals 2025, 15(10), 1163; https://doi.org/10.3390/met15101163

“Machine Learning in the Design and Performance Prediction of Organic Framework Membranes: Methodologies, Applications, and Industrial Prospects”
by Tong Wu, Jiawei Zhang, Qinghao Yan, Jingxiang Wang and Hao Yang
Membranes 2025, 15(6), 178; https://doi.org/10.3390/membranes15060178

“Interpretable Machine Learning Prediction of Polyimide Dielectric Constants: A Feature-Engineered Approach with Experimental Validation”
by Xiaojie He, Jiachen Wan, Songyang Zhang, Chenggang Zhang, Peng Xiao, Feng Zheng and Qinghua Lu
Polymers 2025, 17(12), 1622; https://doi.org/10.3390/polym17121622

“Integrating Machine Learning into Additive Manufacturing of Metallic Biomaterials: A Comprehensive Review”
by Shangyan Zhao, Yixuan Shi, Chengcong Huang, Xuan Li, Yuchen Lu, Yuzhi Wu, Yageng Li and Luning Wang
J. Funct. Biomater. 2025, 16(3), 77; https://doi.org/10.3390/jfb16030077

“Influence of Processing Parameters on Additively Manufactured Architected Cellular Metals: Emphasis on Biomedical Applications”
by Yixuan Shi, Yuzhe Zheng, Chengcong Huang, Shangyan Zhao, Xuan Li, Yuchen Lu, Yuzhi Wu, Peipei Li, Luning Wang and Yageng Li
J. Funct. Biomater. 2025, 16(2), 53; https://doi.org/10.3390/jfb16020053

“Prediction of Mechanical Properties of 3D Printed Particle-Reinforced Resin Composites”
by K. Rooney, Y. Dong, A. K. Basak and A. Pramanik
J. Compos. Sci. 2024, 8(10), 416; https://doi.org/10.3390/jcs8100416

“Data-Driven Optimization of Plasma Electrolytic Oxidation (PEO) Coatings with Explainable Artificial Intelligence Insights”
by Patricia Fernández-López, Sofia A. Alves, Aleksey Rogov, Aleksey Yerokhin, Iban Quintana, Aitor Duo and Aitor Aguirre-Ortuzar
Coatings 2024, 14(8), 979; https://doi.org/10.3390/coatings14080979

“Feature-Assisted Machine Learning for Predicting Band Gaps of Binary Semiconductors”
by Sitong Huo, Shuqing Zhang, Qilin Wu and Xinping Zhang
Nanomaterials 2024, 14(5), 445; https://doi.org/10.3390/nano14050445

“Silicon Solar Cells: Trends, Manufacturing Challenges, and AI Perspectives”
by Marisa Di Sabatino, Rania Hendawi and Alfredo Sanchez Garcia
Crystals 2024, 14(2), 167; https://doi.org/10.3390/cryst14020167

“Synergizing Machine Learning Algorithm with Triboelectric Nanogenerators for Advanced Self-Powered Sensing Systems”
by Roujuan Li, Di Wei and Zhonglin Wang
Nanomaterials 2024, 14(2), 165; https://doi.org/10.3390/nano14020165

“Predicting the Performance of Functional Materials Composed of Polymeric Multicomponent Systems Using Artificial Intelligence—Formulations of Cleansing Foams as an Example”
by Masugu Hamaguchi, Hideki Miwake, Ryoichi Nakatake and Noriyoshi Arai
Polymers 2023, 15(21), 4216; https://doi.org/10.3390/polym15214216

“Unleashing the Power of Artificial Intelligence in Materials Design”
by Silvia Badini, Stefano Regondi and Raffaele Pugliese
Materials 2023, 16(17), 5927; https://doi.org/10.3390/ma16175927

“Determination of Particle Size Distributions of Bulk Samples Using Micro-Computed Tomography and Artificial Intelligence”
by Stefan Höving, Laura Neuendorf, Timo Betting and Norbert Kockmann
Materials 2023, 16(3), 1002; https://doi.org/10.3390/ma16031002

“Insight on Corrosion Prevention of C1018 in 1.0 M Hydrochloric Acid Using Liquid Smoke of Rice Husk Ash: Electrochemical, Surface Analysis, and Deep Learning Studies”
by Agus Paul Setiawan Kaban, Johny Wahyuadi Soedarsono, Wahyu Mayangsari, Mochammad Syaiful Anwar, Ahmad Maksum, Aga Ridhova and Rini Riastuti
Coatings 2023, 13(1), 136; https://doi.org/10.3390/coatings13010136

“Machine Learning and Artificial Intelligence for Polymer Processing” Guest Editors: Dr. Davide Masato, Dr. Saeed Farahani and Dr. Peng Gao Deadline for manuscript submissions: 26 February 2026	“Advances of Machine Learning in Nanoscale Materials Science” Guest Editor: Dr. Gang Tang Deadline for manuscript submissions: 10 February 2026
“Machine Learning for Material and Process Optimization in Additive Manufacturing” Guest Editors: Dr. Haining Zhang, Dr. Joon Phil Choi and Dr. Xingchen Liu Deadline for manuscript submissions: 26 February 2026	“Smart Sensing and Artificial Intelligence in Metal Processing and Machining” Guest Editor: Prof. Dr. Simon Klančnik Deadline for manuscript submissions: 20 March 2026
“Simulation and Artificial Intelligence Method Development for Complex Membrane Transport” Guest Editor: Dr. Christian Jorgensen Deadline for manuscript submissions: 10 May 2026	“Artificial Intelligence and Machine Learning for Material Design, Discovery, and Optimization” Guest Editors: Dr. Craig Hamel and Dr. Devin J. Roach Deadline for manuscript submissions: 20 May 2026

The integration of artificial intelligence (AI) with materials science and engineering has become one of the most dynamic and transformative frontiers in contemporary research. By leveraging AI techniques such as machine learning, deep learning, and data-driven modeling, scientists can now accelerate materials discovery, optimize material properties, and predict performance with unprecedented efficiency. Recognizing its immense potential, MDPI has launched the “AI-Powered Material Science and Engineering” event. We were sincerely honored to interview Dr. Fernando Gomes de Souza Junior, an Editorial Board Member of Materials (ISSN: 1996-1944).

Name: Dr. Fernando Gomes de Souza Junior Affiliation: Biopolymers & Sensors Lab., Macromolecules Institute, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil Interests: natural resources; polymerization; nanocomposites; characterization; imaging; environmental recovery; nanomedicine; sensors; machine learning; data mining

The following is a short interview with Dr. Fernando Gomes de Souza Junior:

1. Could you introduce yourself and provide a concise overview of your research field?
Hi and it’s a pleasure to be here. My name is Fernando G. de Souza Jr. I am a Professor at the Federal University of Rio de Janeiro (UFRJ), and my work sits at the intersection of materials science and engineering, with a specialized focus on biopolymers, nanocomposites, data analysis, experimental design, biofuels, artificial intelligence, and machine learning.
My journey began in 1994, when I enrolled in chemistry at the Federal University of Espírito Santo. Back then, we were still using Windows 3.11—the first encounters with computers felt almost magical. It was during this era that I sent my first email, near the end of the 1990s, and began to realize how profoundly technology could transform scientific research. Throughout my undergraduate studies, my master’s degree (at UENF in materials science and engineering), and my doctorate (at the Institute of Macromolecules, UFRJ, working with conductive polymers), I consistently faced one recurring challenge: the explosion of scientific data generated by instruments such as electrometers, spectrometers, and sensors. This compelled me to learn programming—first in BASIC, later in more advanced languages—to automate measurements, process results, and extract meaning from numerical chaos. My postdoctoral work led me into data analysis and experimental design, where I began constructing statistical models capable of precisely describing the formation and performance processes of the materials we study.
Today, my research group focuses on biopolymers and nanocomposites, particularly in addressing their economic and technical challenge: they are, on average, 25% more expensive than their petrochemical analogs. Overcoming this barrier requires more than simply substituting raw materials—it demands functional innovation, which in turn necessitates nanomodification strategies guided by data-driven optimization. And this is where artificial intelligence entered as a catalyst—not merely as a tool but as a new scientific paradigm of thinking.

2. What has been the greatest challenge you have faced in your research career?
This is a very interesting question—and I believe it doesn’t have a single answer. When I reflect on the evolution of my career since 1994, I see that the greatest challenge wasn’t merely technical—it was cultural and systemic: learning to adapt to the accelerating pace of technological change while simultaneously fighting for investments—both public and private—that can translate this change into real scientific advancement. Universities are fundamental institutions for training qualified personnel, and this became clear to me during my undergraduate research, master’s, doctorate, and, ultimately, through my professorship selection process. But the true leap came when I confronted the absurd volume of data produced by high-precision instruments—data that, without adequate tools, was useless. That’s when I began writing my first code, realizing the importance of programming, multivariate statistics, and factorial experimental design. But the most recent—and perhaps the deepest—challenge is different: text mining of scientific and patent literature.
Today, what challenges me most is extracting hidden knowledge from the literature: articles, patents, technical reports. It’s not just about reading more—it’s about understanding what is not being said, identifying unexplored gaps, and detecting invisible connections between seemingly unrelated fields. For example, while scientific literature emphasizes new nanoparticles, novel synthesis techniques, or thermal properties, patents focus on durability, flexibility, lifespan, and industrial scalability. This discrepancy is rich—yet invisible without AI. This is precisely where we are now focusing: developing machine learning and generative AI models to mine these texts, extract patterns, identify trends, and—most importantly—generate novel hypotheses from data that already exists but remains unread. This is our current challenge: transforming information into strategic knowledge. And this requires more than algorithms—it demands scientific vision, critical curiosity, and persistence.

3. In your view, what are the main advantages of integrating artificial intelligence into materials science and engineering? How has AI transformed your research methods or outcomes?
This is an excellent question—because it touches the core of the revolution we are living through. The integration of AI into materials science is not an enhancement—it is a redefinition of the scientific methodology. Many of the problems we face—complex, multivariate, involving hundreds of interacting variables—would be impossible to solve without these tools.
One concrete example: We developed a butylene succinate oligomer for use as a bio-phase changing material (bio-PCM)—a material that stores and releases heat to regulate temperature in environments. Optimizing its thermal properties involves dozens of parameters: monomer-to-catalyst ratio, reaction temperature, time, pressure, additives, etc. With traditional methods, testing all combinations would take years. With machine learning, we built predictive models that identified optimal conditions for maximum thermal efficiency and cyclic stability—in weeks. And this has enormous social impact: residential climate control consumes staggering amounts of energy. If we can develop materials that reduce this demand, we contribute to energy justice and resilience amid severe climate change.
Another example: in the field of biofuels, we used machine learning to discover novel catalysts. Instead of randomly testing hundreds of compounds, we trained models using molecular structures and catalytic performance data—and the models pointed us toward promising candidates we would never have considered.
We also developed a text classification system to understand how science and industry perceive the same material differently. We used Scopus (scientific literature) and patent databases (WIPO, USPTO). Result? In science, the focus is on new techniques, new nanoparticles, new properties. In patents, the focus is on lifespan, flexibility, production cost, scalability. This divergence reveals a critical gap between what science produces and what industry needs. And AI allows us to visualize, quantify, and—ultimately—bridge it.
But perhaps the work I am most proud of is the development of a unique, unprecedented scale for assessing the hazard of micro- and nanoplastics. No standardized global metric existed. We aggregated data from hundreds of articles—toxicity, size, shape, surface charge, chemical composition, environmental behavior—and trained an AI model to classify the relative risk of each particle type. This would have been impossible without AI. Only through the capacity to process, correlate, and generalize such vast data at scale could we create a tool now being used by research groups worldwide. AI doesn’t merely accelerate research—it redefines what is possible to investigate.

4. Looking ahead to the next decade, what do you see as the main opportunities and potential advances in materials science and engineering driven by AI?
This is another excellent question—and I believe that, above all, we must focus on more efficient methods for extracting scientific data. Much of what we seek to discover is already written—but hidden within thousands of articles, theses, patents, and technical reports. The next great leap will come from intelligent web scraping, semantic extraction, and the use of Large Language Models (LLMs) to uncover connections between concepts, disciplines, and fields. It’s not just about keyword searches. It’s about understanding:

• What are the most critical gaps in biopolymer nanocomposites?
• Which material combinations have been tested and failed—but never documented as “failures”?
• Which patents are blocking innovation due to overly aggressive protection strategies? 

These are the new problems of science—and AI is the only tool capable of solving them.
Moreover, property optimization will remain a pillar—but no longer in isolation. The ideal strategy now integrates four pillars:

1. Data analysis (to understand what already exists);
2. Experimental design (to define next steps efficiently);
3. Computational simulation (Monte Carlo, molecular dynamics);
4. Machine learning (to predict, generalize, and suggest). 

We have already succeeded in predicting properties of nanocomposites—such as thermal conductivity, mechanical strength, or degradation rate—based solely on chemical composition. This eliminates hundreds of experiments. And what’s even more powerful: these models are reusable. A model trained on biopolymers can, with minimal adjustments, be applied to synthetic polymers, ceramics, or even hydrogels.
The next decade will be defined by generative models—not just to predict, but to invent. Imagine a model that, given a functional objective—“a material that is biodegradable, lightweight, highly impact-resistant, and degrades within 6 months in moist soil”—generates hundreds of plausible compositions, suggests molecular structures, viable synthesis routes, and even cost estimates. This is already possible. In ten years, it will be routine. Materials science will cease to be a science of trial and error—and become a science of data-guided computational design.

We are excited to announce that MDPI will be attending the MRS Fall Meeting and Exhibit, taking place from 30 November to 5 December 2025, in Boston, Massachusetts, USA.

Join us at the world’s foremost international scientific gathering for materials research, the MRS Meeting showcases leading interdisciplinary research in both fundamental and applied areas presented by scientists from around the world.

Why visit MDPI’s booth?

• Explore our open access journals covering coloring matters, electronic materials, technology, materials degradation, and more;
• Meet our team and learn how to publish your research with MDPI;
• Discover partnership opportunities and how MDPI supports the scientific community;
• Get exclusive conference materials and gifts.

The following MDPI journals will be represented at the conference:

• Batteries;
• Coatings;
• Electronic Materials;
• Spectroscopy Journal;
• Physics;
• Journal of Functional Biomaterials;
• Energies;
• Chemosensors;
• Methane;
• Corrosion and Materials Degradation;
• Colorants;
• Microplastics;
• Materials.

The 4th Materials Meeting 2025 of MRM will be held from 8 to 13 December 2025 in Yokohama, Japan. The conference will be hosted by the Japan Institute of Metals and Materials.

The symposiums of focus for the conference include the following:

• Cross-disciplinary research in fundamental materials science;
• Frontiers in data-driven materials development;
• Next-generation advanced materials through nanostructure control technology;
• New trends in battery science and application;
• Advancing sustainable materials, energy, and recycling technologies;
• Sustainable futures through advanced materials and water science;
• Advanced materials and emerging technologies for device development;
• High-performance functional materials: preparation, processing, and characterization;
• Innovative soft materials for life, food, and health sciences materials frontier.

The following MDPI journals will be presented at the conference:

• Materials;
• Sustainable Chemistry;
• Coatings.

If you are planning to attend the above conference, please feel free to start an online conversation with us. Our delegates also look forward to meeting you in person and answering any questions that you may have. For more information about the conference, please visit the following link: https://mrm2025.mrs-j.org/.

We are pleased to announce that a new scholar has been appointed as an Editorial Board Member (EBM) for Materials (ISSN: 1996-144), effective October 2025. We wish our new member every success in both their research and their efforts to develop the journal.

Name: Dr. Gaetano Giunta
Affiliation: Luxembourg Institute of Science and Technology, Luxembourg
Interests: beam, plate, and shell structural models; multi-field and multi-scale problems; non-linear mechanics; composites; smart; functionally graded and advanced materials; lattice materials; variable-stiffness materials; finite element and meshless methods

Publications in Materials:
1. “A FEM Free Vibration Analysis of Variable Stiffness Composite Plates through Hierarchical Modeling”
by Gaetano Giunta, Domenico Andrea Iannotta and Marco Montemurro
Materials 2023, 16(13), 4643; https://doi.org/10.3390/ma16134643

The journal is currently still recruiting Editorial Board Members and Guest Editors. Please contact the Editorial Office if you are interested in these positions.

Materials Editorial Office

We would like to acknowledge the following Editorial Board Members and Guest Editors of Materials (ISSN: 1996-1944), who introduced our journal and our Special Issues at their recent conferences.

1. The 8th International Conference on Ionic Liquid-Based Materials—ILMAT 2025
Conference date: 8–12 September 2025
Conference location: Rome, Italy
Editorial Board Member: Prof. Dr. Olga Russina

Relevant Special Issue:
“Ionic Liquid-Based Materials: Fundamentals and Applications”
Guest Editors: Prof. Dr. Olga Russina and Dr. Alessandro Triolo
Deadline for manuscript submissions: 10 May 2026

2. Durability and Sustainability of Concrete Structures (DSCS 2025)
Conference date: 16–18 September 2025
Conference location: Naples, Italy
Editorial Board Member: Prof. Jean-Marc Tulliani

3. Materials Evolution 2025 Conference
Conference date: 18–19 September 2025
Conference location: Krakow, Poland
Guest Editor: Dr. Oleksandr Tkach

Relevant Special Issue:
“Polycrystalline Ferroelectrics: Novel Fabrication Techniques and Applications”
Guest Editors: Dr. Oleksandr Tkach and Dr. Olena Okhay
Deadline for manuscript submissions: 20 December 2025

4. IEEE NAP 2025—15th International Conference on Nanomaterials: Applications & Properties
Conference date: 7–13 September 2025
Conference location: Bratislava, Slovakia
Guest Editor: Dr. Martina Lenzuni

The Special Issues above are open for submissions. For more information, you may access the Special Issues’ website at the following link: https://www.mdpi.com/journal/materials/special_issues.

We look forward to showcasing your research in Materials.

Materials Editorial Office

The Materials Best PhD Thesis Award recognizes young scholars who have produced outstanding PhD theses in the field of materials science and engineering, hoping to further encourage the continuation of their outstanding work and contribution to their field.

Prize:

• CHF 800;
• A certificate;
• A voucher to waive the article processing charges (APCs) for one submission in the journal (subject to peer review), valid for one year.

Number of winners: 1.

To find out more information about the award and how to nominate candidates, please click here: https://www.mdpi.com/journal/materials/awards/3686.

To request further information, please contact the Materials Editorial Office.

Materials Editorial Office