Data-Driven Artificial Intelligence, Optimization and Related Applications in Materials Science
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".
Deadline for manuscript submissions: 31 October 2026 | Viewed by 381
Special Issue Editors
Interests: simulation; optimization; mechanical testing; finite element modeling; microstructure; materials; finite element analysis; mechanical properties; mechanics of materials; mechanical behavior of materials
Special Issues, Collections and Topics in MDPI journals
Interests: optimization; numerical methods; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: predictive maintenance; prognostics and health management; machine learning; Industry 4.0
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We are pleased to invite researchers and practitioners to submit their latest high-quality research to the special session Data-Driven Artificial Intelligence, Optimization and Related Applications which will be published in Journal of Mathematics, Materials Science, MDPI. This special session aims to showcase innovative mathematical methodologies and impactful applications that are shaping the future of materials science.
The integration of data-driven artificial intelligence with advanced optimization techniques is revolutionizing how materials are discovered, modeled, and optimized. From accelerating materials design and reducing experimental costs to enabling accurate prediction of complex material behaviors, these approaches are rapidly becoming essential tools across academia and industry. This special session provides a unique and timely forum to disseminate breakthrough results at the intersection of mathematics, AI, and materials science.
We particularly encourage submissions that emphasize strong mathematical foundations combined with real-world relevance. Topics of interest include, but are not limited to, machine learning and deep learning methods for materials modeling, physics-informed and data-driven approaches, optimization and control strategies for materials design, inverse and ill-posed problems, uncertainty quantification, surrogate and reduced-order modeling, and AI-assisted multiscale or multiphysics simulations.
Authors will benefit from the wide visibility and rapid publication offered by MDPI, as well as from a rigorous peer-review process led by experts in mathematics and applied sciences. The special session is intended to attract a broad interdisciplinary audience, offering contributors an excellent opportunity to highlight the novelty, mathematical depth, and practical impact of their work.
We warmly invite researchers, industry professionals, and early-career scientists to contribute original research articles or comprehensive review papers. By submitting to this special session, authors will help define emerging research directions and advance the development of data-driven, mathematically sound solutions for materials science challenges.
Submit your work and join us in shaping the future of data-driven mathematics for materials science.
Prof. Dr. David Bassir
Prof. Dr. Jüri Majak
Dr. Hai-Canh Vu
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- data-driven artificial intelligence
- machine learning in materials science
- mathematical optimization
- physics-informed learning
- materials design and discovery
- inverse problems
- uncertainty quantification
- multiscale modeling
- surrogate modeling
- reduced-order models
- numerical methods
- computational materials science
- AI-assisted simulation
- data-driven modeling
- digital twin model
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
