{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:23:55Z","timestamp":1760142235647,"version":"build-2065373602"},"reference-count":37,"publisher":"IOP Publishing","issue":"4","license":[{"start":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T00:00:00Z","timestamp":1760054400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T00:00:00Z","timestamp":1760054400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. 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These workshops explored diverse research areas currently shaping the field, with participants engaging in comprehensive discussions that addressed the intersection\u2019s most significant challenges from scientific, technical, and commercial perspectives. Through this holistic approach, AI4Mat\u2019s 2024 workshops successfully illuminated the multifaceted nature of AI-driven materials research, highlighting both current achievements and future opportunities in this rapidly evolving field. In this article, the AI4Mat-2024 organizing committee presents key insights from our workshops and community discussions, outlining critical challenges in this emerging field while summarizing the latest advances in AI-accelerated materials design. We examine persistent challenges around data creation and reproducibility, alongside the growing commercial interest in developing new markets and optimization materials production processes at scale. The article also highlights significant research breakthroughs showcased at AI4Mat, including the application of large language models to accelerate materials science tasks, the development of sophisticated generative models for materials discovery, and the growing demand for interpretable AI methodologies that provide transparent insights into materials behavior.<\/jats:p>","DOI":"10.1088\/2632-2153\/ae0d5d","type":"journal-article","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T07:47:34Z","timestamp":1759218454000},"page":"040201","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Perspective on artificial intelligence for accelerated materials design (AI4Mat) workshops in 2024"],"prefix":"10.1088","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5121-3853","authenticated-orcid":true,"given":"Santiago","family":"Miret","sequence":"first","affiliation":[]},{"given":"Marta","family":"Skreta","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3772-6927","authenticated-orcid":true,"given":"Geemi","family":"Wellawatte","sequence":"additional","affiliation":[]},{"given":"Stefano","family":"Martiniani","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1500-4947","authenticated-orcid":true,"given":"N M","family":"Anoop Krishnan","sequence":"additional","affiliation":[]},{"given":"George","family":"Karypis","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4894-4660","authenticated-orcid":true,"given":"Kevin","family":"Maik Jablonka","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2025,10,10]]},"reference":[{"year":"2022","author":"Miret","key":"mlstae0d5dbib1","type":"conference-proceedings"},{"key":"mlstae0d5dbib2","doi-asserted-by":"publisher","first-page":"1081","DOI":"10.1039\/D4DD90010C","type":"journal-article","volume":"3","author":"Miret","year":"2024","journal-title":"Digit. 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