Artificial Intelligence (AI) is revolutionizing the field of Materials Science and Engineering by accelerating the discovery, design, and optimization of new materials. By leveraging machine learning algorithms, AI can predict material properties, enabling researchers to identify novel materials with desired characteristics more efficiently than traditional methods. AI-driven simulations and data analysis tools are enhancing the understanding of complex material behaviors, reducing the need for costly and time-consuming experiments. This synergy between AI and materials science is particularly impactful in fields like renewable energy, electronics, and nanotechnology, where innovative materials are crucial. Ultimately, AI is paving the way for breakthroughs in material design, promising faster development of high-performance materials for diverse industrial applications.
Title : The effect of substitution of Mn by Pd on the structure and thermomagnetic properties of the Mn1−xPdxCoGe alloys (where x = 0.03, 0.05, 0.07 and 0.1)
Piotr Gebara, Politechnika Czestochowska, Poland
Title : Prospective study of copper sulfide nanofilms
Paulo Cesar De Morais, Catholic University of Brasilia, Brazil
Title : Modeling phase crystallization in Ge-rich Ge-Sb-Te PCRAMs
Alain Portavoce, IM2NP, France
Title : Application of vanadium and tantalum single-site zeolite catalysts in heterogeneous catalysis
Stanislaw Dzwigaj, Sorbonne University, France
Title : Process parameters optimization and mechanical properties of additively manufactured ankle-foot orthoses based on polypropylene
Mohamed Yousfi, INSA Lyon, IMP Laboratory, France
Title : Non-thermal microwave effects in isothermal materials processing: their origin, significance, and roles
Boon Wong, Retired Materials Scientist, United States