The evolving role of artificial intelligence in materials science: From data-driven discovery to future prospects

Bendaoud Mebarek; Joan-Josep  Suñol; daoud moh amin amin; Yassine  El Guerri; alem abdelkader; keddam mourad

doi:10.62638/ZasMat1837

Authors

Bendaoud Mebarek Research Laboratory in Artificial Intelligence and Systems, University of Tiaret, Algeria Author https://orcid.org/0000-0002-6838-3867
Joan-Josep Suñol Departmento de Fisica, Universitat de Girona, Spain Author https://orcid.org/0000-0002-7653-3415
Mohamed Amine Daoud Research Laboratory in Artificial Intelligence and Systems, University of Tiaret, Algeria Author https://orcid.org/0000-0001-8486-8541
Yassine El Guerri Research Laboratory of Industrial Technologies, Mechanical Engineering Department, University of Tiaret, Algeria Author https://orcid.org/0009-0008-9664-2361
Abdelkader Alem Research Laboratory in Artificial Intelligence and Systems, University of Tiaret, Algeria Author https://orcid.org/0000-0002-5110-2256
Mourad Keddam Laboratory of Materials Technology, USTHB, Algiers, Algeria Author https://orcid.org/0000-0002-7721-5830

DOI:

https://doi.org/10.62638/ZasMat1837

Abstract

The incorporation of Artificial Intelligence (AI) into materials science has initiated a transformative shift, facilitating significant advancements in the discovery, optimization, and production of novel materials. This review provides a comprehensive overview of recent developments, uses, and problems that come with using AI in this area. It looks at cutting-edge methods like machine learning, neural networks, and genetic algorithms, and how they can help speed up the development of new materials. The main areas of use, such as finding new materials, improving the properties of existing ones, modelling and simulation, and improving manufacturing processes, are all discussed in depth. Even though a lot of progress has been made, there are still a lot of problems to solve, especially when it comes to data quality and availability, computational complexity, model interpretability, and ethics. This review includes case studies to show how AI-driven methods can be useful and effective in materials science. It also points out new trends and future directions, stressing the possibilities for collaboration between different fields and new research opportunities.

Keywords:

Artificial intelligence, materials science, machine learning, optimization, innovation

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