Malica, CristianoNovoselov, Kostya S.Barnard, Amanda S.Kalinin, Sergei V.Spurgeon, Steven R.Reuter, KarstenAlducin, MaiteDeringer, Volker L.Csányi, GáborMarzari, NicolaHuang, ShirongCuniberti, GianaurelioDeng, QiushiOrdejón, PabloCole, IvanChoudhary, KamalHippalgaonkar, KedarZhu, Ruimingvon Lilienfeld, O. AnatoleHibat-Allah, MohamedCarrasquilla, JuanCisotto, GiuliaZancanaro, AlbertoWenzel, WolfgangFerrari, Andrea C.Ustyuzhanin, AndreyRoche, Stephan2025-06-112025-06-11WOS:001473720000001ORCID:/0000-0002-4784-2382/work/185262556http://www.scopus.com/inward/record.url?scp=105003766969&partnerID=8YFLogxKhttps://hdl.handle.net/1885/733758484This perspective addresses the topic of harnessing the tools of artificial intelligence (AI) for boosting innovation in functional materials design and engineering as well as discovering new materials for targeted applications in energy storage, biomedicine, composites, nanoelectronics or quantum technologies. It gives a current view of experts in the field, insisting on challenges and opportunities provided by the development of large materials databases, novel schemes for implementing AI into materials production and characterization as well as progress in the quest of simulating physical and chemical properties of realistic atomic models reaching the trillion atoms scale and with near ab initio accuracy.C M acknowledges the support by the European Commission through the MaX Centre of Excellence for supercomputing applications (grant number 101093374). C M and N M acknowledge support from the Deutsche Forschungsgemeinschaft (DFG) under Germany's Excellence Strategy (EXC 2077, No. 390741603, University Allowance, University of Bremen) and Lucio Colombi Ciacchi, the host of the 'U Bremen Excellence Chair Program'. S R acknowledges funding from 2021 SGR 00997, funded by Generalitat de Catalunya and Grant PID2022-138283NB-I00 funded by MICIU/AEI/ 10.13039/501100011033 and by 'ERDF/EU'. ICN2 is funded by the CERCA Programme/Generalitat de Catalunya and supported by the Severo Ochoa Centres of Excellence programme, Grant CEX2021-001214-S, funded by MCIN/AEI/10.13039.501100011033. K S N and A U acknowledge support by the Ministry of Education, Singapore, under its Research Centre of Excellence award to the Institute for Functional Intelligent Materials (I-FIM, project No. EDUNC-33-18-279-V12) and National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG3-RP-2022-028). M A acknowledges financial support from the Spanish MCIN/AEI/10.13039/501100011033 (Grant No. PID2022-140163NB-I00), Gobierno Vasco-UPV/EHU (Project No. IT1569-22), and the Basque Government Education Departments' IKUR program, also co-funded by the European NextGenerationEU action through the Spanish Plan de Recuperacion, Transformacion y Resiliencia (PRTR). V L D acknowledges support from UK Research and Innovation [grant number EP/X016188/1]. O A v L acknowledges the support by the Natural Sciences and Engineering Research Council of Canada (NSERC), [funding reference number RGPIN-2023-04853], the University of Toronto's Acceleration Consortium via the Canada First Research Excellence Fund, grant number: CFREF-2022-00042, the Ed Clark Chair of Advanced Materials, a Canada CIFAR AI Chair, the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 772834). W W acknowledges the support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy for the Excellence Cluster '3D Matter Made to Order' (Grant No. EXC-2082/1-390761711) and by the Carl Zeiss Foundation.20en© 2025 The Author(s).artificial intelligence (AI)machine learning (ML)materials science2025-03-1910.1088/2515-7639/adc29d105003766969