Applied Artificial Intelligence in Materials Science and Material Design
Chávez Ángel, Emigdio (Institut Català de Nanociència i Nanotecnologia)
Eriksen, Martin (Institut de Física d'Altes Energies)
Castro-Alvarez, Alejandro (Universidad de La Frontera (Xile). Departamento de Ciencias Preclínicas)
Garcia, José H. (Institut Català de Nanociència i Nanotecnologia)
Botifoll, Marc (Institut Català de Nanociència i Nanotecnologia)
Avalos-Ovando, Oscar (Ohio University. Department of Physics and Astronomy)
Arbiol i Cobos, Jordi (Institut Català de Nanociència i Nanotecnologia)
Mugarza, Aitor (Institut Català de Nanociència i Nanotecnologia)

Date:	2025
Abstract:	Materials science has traditionally relied on a combination of experimental techniques and theoretical modeling to discover and develop new materials with desired properties. However, these processes can be time-consuming, resource-intensive, and often limited by the complexity of material systems. The advent of artificial intelligence (AI), particularly machine learning, has revolutionized materials science by offering powerful tools to accelerate the discovery, design, and characterization of novel materials. AI not only enhances the predictive modeling of material properties but also streamlines data analysis in techniques like X-Ray diffraction, Raman spectroscopy, scanning probe microscopy, and electron microscopy. By leveraging large datasets, AI algorithms can identify patterns, reduce noise, and predict material behavior with unprecedented accuracy. In this review, recent advancements in AI applications across various domains of materials science, including spectroscopy, synchrotron studies, scanning probe and electron microscopies, metamaterials, atomistic modeling, molecular design, and drug discovery, are highlighted. It is discussed how AI-driven methods are reshaping the field, making material discovery more efficient, and paving the way for breakthroughs in material design and real-time experimental analysis.
Grants:	European Commission 101094299 Agencia Estatal de Investigación TED2021-132388B-C41 Agencia Estatal de Investigación PRTR-C17.I1 Generalitat de Catalunya 2021/SGR-00457 Generalitat de Catalunya 2020/FI-00103
Note:	Altres ajuts: this study was supported by Generalitat de Catalunya (In-CAEM Project). ICN2 acknowledges funding from Grant IU16-014206 (METCAM FIB) funded by the European Union through the European Regional Development Fund (ERDF), with the support of the Ministry of Research and Universities, Generalitat de Catalunya. ICN2 is founding member of e-DREAM.
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article de revisió ; recerca ; Versió publicada
Subject:	Artificial intelligence ; Electron microscope ; Material design ; Pharma ; Scanning probe microscopy ; Spectroscopy
Published in:	Advanced Intelligent Systems, Vol. 7, Num. 8 (August 2025) , art. 2400986, ISSN 2640-4567

DOI: 10.1002/aisy.202400986

26 p, 3.7 MB

Preprint 42 p, 2.9 MB

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Research literature > UAB research groups literature > Research Centres and Groups (research output) > Experimental sciences > Catalan Institute of Nanoscience and Nanotechnology (ICN2)
Research literature > UAB research groups literature > Research Centres and Groups (research output) > Experimental sciences > Institut de Física d’Altes Energies (IFAE)
Articles > Research articles
Articles > Published articles