TY - JOUR
T1 - Artificial Intelligence for Advanced Functional Materials: Exploring Current and Future Directions
T2 - Article No. 021001
AU - Malica, Cristiano
AU - Novoselov, Kostya
AU - Barnard, Amanda
AU - Kalinin, Sergei
AU - Spurgeon, Steven
AU - Reuter, Karsten
AU - Alducin, Maite
AU - Deringer, Volker
AU - Csanyi, Gabor
AU - Marzari, Nicola
AU - Huang, Shirong
AU - Cuniberti, Gianaurelio
AU - Deng, Qiushi
AU - Ordejon, Pablo
AU - Cole, Ivan
AU - Choudhary, Kamal
AU - Hippalgaonkar, Kedar
AU - Hippalgaonkar, Kedar
AU - Zhu, Ruiming
AU - von Lilienfeld, O. Anatole
AU - Hibat-Allah, Mohamed
AU - Carrasquilla, Juan
AU - Cisotto, Giulia
AU - Zancanaro, Alberto
AU - Wenzel, Wolfgang
AU - Ferrari, Andrea
AU - Ustyuzhanin, Andrey
AU - Roche, Stephan
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - artificial intelligence (AI)
KW - machine learning (ML)
KW - materials science
U2 - 10.1088/2515-7639/adc29d
DO - 10.1088/2515-7639/adc29d
M3 - Article
SN - 2515-7639
VL - 8
JO - JPhys Materials
JF - JPhys Materials
IS - 2
ER -