Aims & Scope

Aims

AI Materials (ISSN 3042-6715) is an international, peer-reviewed, open access journal dedicated to advancing the interdisciplinary frontier of applications between artificial intelligence (AI) and materials science. It publishes reviews, regular research papers (articles), and short communications, but other article types will also be considered. We aim to establish a platform for disseminating high-impact outcomes on the integration of AI methodologies with materials science, with key focus areas including AI-driven material screening, predictive property optimization, autonomous experimental systems, and large language models.

We encourage authors to publish their experimental and theoretical research in as much detail as possible; therefore, this journal has no restriction on the maximum length of papers. The research methodology must be provided in sufficient detail so that results can be reproduced.

Scope

• AI-Enhanced Theory & Simulation: Machine learning’s potential, AI algorithms for accelerating electronic structure, molecular dynamics, and multiscale modeling.
• Generative Design & Discovery: Generative models for the inverse design of novel materials, explicitly addressing synthesizability, stability, and processing conditions.
• Autonomous Experimentation: Closed-loop workflows driven by AI agents, integrating automated synthesis and characterization (Self-Driving Labs) to accelerate material optimization.
• AI for Characterization: Advanced computer vision and signal processing techniques for the automated interpretation of microscopy, spectroscopy, and scattering data.
• LLMs & Agents: Large language models for knowledge extraction from the literature, and autonomous research agents capable of reasoning, planning, and executing complex scientific tasks.
• Data Infrastructure: Construction of benchmark datasets and databases with a focus on data quality, standardization, and uncertainty quantification.
• Open Science & Tools: Development of open-source software, platforms, and digital twins tailored to the materials science community.

Note: We welcome research on all classes of materials, including inorganic, organic, polymers, biomaterials, and hybrids. We encourage the publication of high-quality negative datasets valuable for model training.

MDPI Publication Ethics Statement

MDPI is a member of the Committee on Publication Ethics (COPE). MDPI takes the responsibility to enforce a rigorous peer-review together with strict ethical policies and standards to ensure to add high quality scientific works to the field of scholarly publication. Unfortunately, cases of plagiarism, data falsification, inappropriate authorship credit, and the like, do arise. MDPI takes such publishing ethics issues very seriously and our editors are trained to proceed in such cases with a zero tolerance policy. To verify the originality of content submitted to our journals, we use iThenticate to check submissions against previous publications.

Book Reviews

Authors and publishers are encouraged to send review copies of their recent related books to the following address. Received books will be listed as Books Received within the journal's News & Announcements section.

MDPI
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Copyright / Open Access

Articles published in AI Materials will be Open-Access articles distributed under the terms and conditions of the Creative Commons Attribution License (CC BY). The copyright is retained by the author(s). MDPI will insert the following note at the end of the published text:

Reprints

Reprints may be ordered. Please contact publisher@mdpi.com for more information on how to order reprints.

Announcement and Advertisement

Announcements regarding academic activities such as conferences are published for free in the News & Announcements section of the journal. Advertisement can be either published or placed on the pertinent website. Contact e-mail address is aimater@mdpi.com.