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

Special Issue on Intelligent Additive Manufacturing
截稿日期: 2026-03-01

Additive manufacturing (AM) is revolutionising how components are fabricated by enabling complex geometry and lightweight, topology‑optimised structures. Yet intrinsic process-induced defects—porosity, microcracks, residual stresses and mechanical anisotropy—can severely limit component performance and service life. Therefore, over the past few years, artificial intelligence and machine‑learning methods have been widely adopted to tackle these challenges:

    In‑situ defect detection via computer vision, infrared thermography, acoustic emission and other sensor readings, enabling real‑time identification of defects.
    Data‑driven parameter optimization uses supervised and reinforcement learning to tune layer height, scan speed, laser power and bead overlap, achieving target density, mechanical properties and surface finish.
    Closed‑loop feedback control—often implemented through digital‑twin frameworks and real‑time sensor fusion—allows adaptive adjustment of extrusion rates, melt‑pool dynamics or scan strategies to suppress defect nucleation and propagation.
    Al-powered process design, implementation and optimization greatly improve the intelligence level of high-end manufacturing systems via the support of Internet of Things, Big Data, Cloud Computing, etc.

This special issue welcomes original research articles, review papers and case studies addressing AI‑empowered solutions across diverse materials (polymers, metals, ceramics, composites) and AM processes (FFF, SLM, DED, SLA). The main goal is to extend fundamental knowledge, foster open data exchange, and highlight successful deployments of intelligent monitoring and control strategies that enhance structural integrity, reproducibility and sustainability in additive manufacturing for academia and industry.

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.