Artificial intelligence for advanced functional materials: exploring current and future directions
Abstract This 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.
Authors
- Qiushi Deng (ORCID: https://orcid.org/0000-0001-8118-1998)
- Nicola Marzari (ORCID: https://orcid.org/0000-0002-9764-0199)
- Ruiming Zhu (ORCID: https://orcid.org/0009-0000-2270-2669)
- Shirong Huang (ORCID: https://orcid.org/0000-0002-4349-793X)
- Gianaurelio Cuniberti (ORCID: https://orcid.org/0000-0002-6574-7848)
- Kamal Choudhary (ORCID: https://orcid.org/0000-0001-9737-8074)
- Pablo Ordejón (ORCID: https://orcid.org/0000-0002-2353-2793)
- Karsten Reuter (ORCID: https://orcid.org/0000-0001-8473-8659)
- Stephan Roche (ORCID: https://orcid.org/0000-0003-0323-4665)
- Cristiano Malica (ORCID: https://orcid.org/0000-0003-1159-4468)
- Steven R. Spurgeon (ORCID: https://orcid.org/0000-0003-1218-839X)
- Giulia Cisotto (ORCID: https://orcid.org/0000-0002-9554-9367)
- Kedar Hippalgaonkar (ORCID: https://orcid.org/0000-0002-1270-9047)
- Sergei V. Kalinin (ORCID: https://orcid.org/0000-0001-5354-6152)
- Alberto Zancanaro (ORCID: https://orcid.org/0000-0002-5276-7030)
- Mohamed Hibat-Allah (ORCID: https://orcid.org/0000-0002-5298-8589)
- Volker L. Deringer (ORCID: https://orcid.org/0000-0001-6873-0278)
- Wolfgang Wenzel (ORCID: https://orcid.org/0000-0001-9487-4689)
- M. Alducin (ORCID: https://orcid.org/0000-0002-1264-7034)
- Kostya S. Novoselov (ORCID: https://orcid.org/0000-0003-4972-5371)
- Ivan Cole (ORCID: https://orcid.org/0000-0001-6582-1457)
- O. Anatole von Lilienfeld (ORCID: https://orcid.org/0000-0001-7419-0466)
- Juan Carrasquilla
- A. Ustyuzhanin (ORCID: https://orcid.org/0000-0001-7865-2357)
- Amanda S. Barnard
- Andrea C. Ferrari
- Gábor Csányi
Institutions
- Karlsruhe Institute of Technology (DE)
- Agency for Science, Technology and Research (SG)
- Australian National University (AU)
- Institució Catalana de Recerca i Estudis Avançats (ES)
- National Laboratory of the Rockies (US)
- National Institute of Standards and Technology (US)
- University of Trieste (IT)
- Pacific Northwest National Laboratory (US)
- University of Waterloo (CA)
- National University of Singapore (SG)
- Nanyang Technological University (SG)
- University of Bremen (DE)
- University of Toronto (CA)
- University of Colorado Boulder (US)
- University of Cambridge (GB)
- Board of the Swiss Federal Institutes of Technology (CH)
- Fritz Haber Institute of the Max Planck Society (DE)
- Constructing Excellence (GB)
- ETH Zurich (CH)
- University of Oxford (GB)
- Institut Català de Nanociència i Nanotecnologia (ES)
- Vector Institute (CA)
- Material Physics Center (ES)
- RMIT Europe (ES)
- Technische Universität Berlin (DE)
- Donostia International Physics Center (ES)
- École Polytechnique Fédérale de Lausanne (CH)
- University of Tennessee at Knoxville (US)
- Technische Universität Dresden (DE)
Publication Details
- Journal
- Journal of Physics Materials
- Published
- 2025-03-19
- DOI
- https://doi.org/10.1088/2515-7639/adc29d
- Citations
- 8
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 4.14