Aims

Smart Materials and Devices (SMD) is an international, peer-reviewed, open-access journal dedicated to advancing the frontier of intelligent materials and their integration with cutting-edge technologies. Published quarterly by Science Exploration Press, SMD provides a premier platform for research that spans the development and application of smart materials, with a strong emphasis on the transformative role of artificial intelligence (AI).
Our goal is to publish groundbreaking research that not only covers the spectrum of smart materials, but also highlights how AI and machine learning are revolutionizing material science. We aim to bridge fundamental research with practical applications, encouraging interdisciplinary collaboration and driving innovation in both material science and engineering. By focusing on how AI can reshape the design, optimization, and application of smart materials, we strive to foster advancements that push the boundaries of what these materials can achieve.

Scope

1. Smart Materials and Applications
● Stimuli-responsive and adaptive materials.
● Shape memory alloys and polymers for biomedical, aerospace, etc.
● Piezoelectric, ferroelectric, and magnetoelectric materials for sensing, actuation, and energy harvesting.
● Nanomaterials and nanocomposites for electronics, energy, environment, and medical applications.
● Biomimetic and self-healing materials.

2. Smart Devices and Applications
● Energy devices, including optoelectronics, thermoelectrics, supercapacitors, batteries, etc.
● Wearable devices and smart textiles.
● Biomedical devices, such as implants, drug delivery systems, and health monitoring.
● Functional devices and systems with smart sensors and actuators.
● Environmental monitoring and intelligent systems.

3. Advanced Manufacturing
● 3D/4D printing technologies for smart material and device manufacturing.
● Advanced fabrication techniques like self-assembly and micro/nano-fabrication.
● Materials synthesis and characterization.

4. AI in Materials Science
● AI-driven materials design and discovery.
● Neural networks and data analytics.
● Machine learning for materials science.
● AI integration with computational modeling.