期刊信息

AI & Materials

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出版商:
ELSP
ISSN:
3006-7588
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征稿

AI & Materials is an academic journal published by ELSP. (ISSN 3006-7588).

AI & Materials is an online multidisciplinary open access journal committed to the deep integration and common enhancement in materials science and artificial intelligence (AI) technology. The journal aims to build an open and fair platform to attract peer-reviewed research articles that report newest achievements with innovation related to joint developments of AI theory and technology and materials design, prediction and production. Moreover, the journal also welcomes the novel research papers that potentially motivate the Interdisciplinary progresses of materials science and AI. The scope of the journal includes but is not limited to: Novel AI algorithms that have potential applications to materials Computer-aided design of novel materials AI for science technique, especially for materials science Digital twin technology with AI for materials industry Modelling techniques for manufacturing processes and systems boosted with AI Materials theory assisted by AI technology High-Performance Computing (HPC) for modeling, simulation, and analysis applicable to materials and AI
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Special Issues

Special Issue on AI-Enhanced Multifunctional Dielectric Materials — From Design to Application 截稿日期: 2026-06-30 This Special Issue of AI & Materials (AIMAT) is launched in conjunction with the newly established ICAIM Workshop “AI-Enhanced Multifunctional Dielectric Materials: From Design to Application.” Both the workshop and the Special Issue aim to build a synergistic platform for advancing the integration of artificial intelligence with multifunctional dielectric materials research. Multifunctional dielectric materials exhibiting coupled mechanical, electrical, magnetic, thermal, and optical responses are at the core of innovation in electronics, energy storage, and energy conversion. However, their complex structure–property relationships under multiphysics coupling remain difficult to model and optimize. Artificial intelligence offers transformative capabilities to accelerate design, characterization, and performance prediction in this field. This Special Issue welcomes original research papers, reviews, and perspectives that address AI-driven methodologies for the design, characterization, and optimization of multifunctional dielectric materials. Topics of interest include, but are not limited to: AI-based modeling and prediction of dielectric material performance under multiphysics constraints Machine learning–assisted optimization of ferroelectric capacitors for memory and energy storage AI-guided design of ferroelectric materials for high-efficiency photovoltaic applications Intelligent design of electromagnetic wave–absorbing materials Data-driven discovery of novel dielectric systems with multifunctional responses Integration of computational and experimental AI frameworks for material innovation The goal of this Special Issue is to promote interdisciplinary collaboration among materials scientists, physicists, chemists, electrical engineers, and computer scientists, and to accelerate the development of next-generation smart dielectric materials empowered by artificial intelligence.
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