Artificial intelligence for advanced functional materials: exploring current and future directions

Cristiano Malica; Kostya S. Novoselov; Amanda S. Barnard; Sergei V. Kalinin; Steven R. Spurgeon; Karsten Reuter; Maite Alducin; Volker L. Deringer; Gábor Csányi; Nicola Marzari; Shirong Huang; Gianaurelio Cuniberti; Qiushi Deng; Pablo Ordejón; Ivan Cole; Kamal Choudhary; Kedar Hippalgaonkar; Ruiming Zhu; O. Anatole von Lilienfeld; Mohamed Hibat-Allah; Juan Carrasquilla; Giulia Cisotto; Alberto Zancanaro; Wolfgang Wenzel; Andrea C. Ferrari; Andrey Ustyuzhanin; Stephan Roche

doi:10.1088/2515-7639/adc29d

Artificial intelligence for advanced functional materials: exploring current and future directions

Cristiano Malica^*, Kostya S. Novoselov, Amanda S. Barnard, Sergei V. Kalinin, Steven R. Spurgeon, Karsten Reuter, Maite Alducin, Volker L. Deringer, Gábor Csányi, Nicola Marzari, Shirong Huang, Gianaurelio Cuniberti, Qiushi Deng, Pablo Ordejón, Ivan Cole, Kamal Choudhary, Kedar Hippalgaonkar, Ruiming Zhu, O. Anatole von Lilienfeld, Mohamed Hibat-AllahJuan Carrasquilla, Giulia Cisotto, Alberto Zancanaro, Wolfgang Wenzel, Andrea C. Ferrari, Andrey Ustyuzhanin, Stephan Roche^*

^*Corresponding author for this work

The Australian National University

Research output: Contribution to journal › Editorial › peer-review

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.

Original language	English
Article number	021001
Number of pages	20
Journal	JPhys Materials
Volume	8
Issue number	2
DOIs	https://doi.org/10.1088/2515-7639/adc29d
Publication status	Published - 23 Apr 2025

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Malica, C., Novoselov, K. S., Barnard, A. S., Kalinin, S. V., Spurgeon, S. R., Reuter, K., Alducin, M., Deringer, V. L., Csányi, G., Marzari, N., Huang, S., Cuniberti, G., Deng, Q., Ordejón, P., Cole, I., Choudhary, K., Hippalgaonkar, K., Zhu, R., von Lilienfeld, O. A., ... Roche, S. (2025). Artificial intelligence for advanced functional materials: exploring current and future directions. JPhys Materials, 8(2), Article 021001. https://doi.org/10.1088/2515-7639/adc29d

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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.",

keywords = "artificial intelligence (AI), machine learning (ML), materials science",

author = "Cristiano Malica and Novoselov, {Kostya S.} and Barnard, {Amanda S.} and Kalinin, {Sergei V.} and Spurgeon, {Steven R.} and Karsten Reuter and Maite Alducin and Deringer, {Volker L.} and G{\'a}bor Cs{\'a}nyi and Nicola Marzari and Shirong Huang and Gianaurelio Cuniberti and Qiushi Deng and Pablo Ordej{\'o}n and Ivan Cole and Kamal Choudhary and Kedar Hippalgaonkar and Ruiming Zhu and {von Lilienfeld}, {O. Anatole} and Mohamed Hibat-Allah and Juan Carrasquilla and Giulia Cisotto and Alberto Zancanaro and Wolfgang Wenzel and Ferrari, {Andrea C.} and Andrey Ustyuzhanin and Stephan Roche",

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doi = "10.1088/2515-7639/adc29d",

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Malica, C, Novoselov, KS, Barnard, AS, Kalinin, SV, Spurgeon, SR, Reuter, K, Alducin, M, Deringer, VL, Csányi, G, Marzari, N, Huang, S, Cuniberti, G, Deng, Q, Ordejón, P, Cole, I, Choudhary, K, Hippalgaonkar, K, Zhu, R, von Lilienfeld, OA, Hibat-Allah, M, Carrasquilla, J, Cisotto, G, Zancanaro, A, Wenzel, W, Ferrari, AC, Ustyuzhanin, A & Roche, S 2025, 'Artificial intelligence for advanced functional materials: exploring current and future directions', JPhys Materials, vol. 8, no. 2, 021001. https://doi.org/10.1088/2515-7639/adc29d

TY - JOUR

T1 - Artificial intelligence for advanced functional materials: exploring current and future directions

AU - Malica, Cristiano

AU - Novoselov, Kostya S.

AU - Barnard, Amanda S.

AU - Kalinin, Sergei V.

AU - Spurgeon, Steven R.

AU - Reuter, Karsten

AU - Alducin, Maite

AU - Deringer, Volker L.

AU - Csányi, Gábor

AU - Marzari, Nicola

AU - Huang, Shirong

AU - Cuniberti, Gianaurelio

AU - Deng, Qiushi

AU - Ordejón, Pablo

AU - Cole, Ivan

AU - Choudhary, Kamal

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 C.

AU - Ustyuzhanin, Andrey

AU - Roche, Stephan

PY - 2025/4/23

Y1 - 2025/4/23

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

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U2 - 10.1088/2515-7639/adc29d

DO - 10.1088/2515-7639/adc29d

M3 - Editorial

AN - SCOPUS:105003766969

SN - 2515-7639

VL - 8

JO - JPhys Materials

JF - JPhys Materials

IS - 2

M1 - 021001

ER -

Artificial intelligence for advanced functional materials: exploring current and future directions

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