Role of Artificial Intelligence in Quantum Materials Research

Year : 2026 | Volume : 16 | Issue : 02 | Page : 13 27
By

Ravuri Hema Krishna,

  1. Professor, Department of Chemistry, Amrita Sai Institute of Science and Technology, Bathinapadu, Andhra Pradesh,

Abstract

Quantum materials have emerged as a transformative class of advanced materials due to their extraordinary electronic, magnetic, optical, and topological properties governed by quantum mechanical phenomena. These materials are expected to revolutionize next-generation technologies such as quantum computing, spintronics, superconducting electronics, nanoelectronics, intelligent sensing systems, and energy-efficient devices. However, conventional methods for discovering and optimizing quantum materials are often expensive, time-consuming, and computationally intensive because of the enormous complexity of quantum interactions and the vast compositional design space involved. In recent years, artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has become a powerful tool for accelerating quantum materials research. AI-driven approaches enable rapid prediction of material properties, identification of stable crystal structures, optimization of synthesis parameters, and discovery of novel quantum phases with significantly reduced experimental effort. Furthermore, AI-assisted computational techniques improve quantum simulations, enhance reproducibility, and support autonomous experimental systems capable of intelligent decision-making. This review article provides a comprehensive overview of the role of AI in quantum materials science, including AI-assisted materials discovery, superconductivity prediction, topological materials classification, fabrication optimization, and quantum device engineering. The manuscript also discusses major challenges such as limited datasets, interpretability issues, and computational complexity associated with AI-driven materials research. Finally, future perspectives involving quantum ML, self-driving laboratories, and AI-integrated quantum technologies are highlighted. The convergence of artificial intelligence, computational materials science, and nanotechnology is expected to fundamentally reshape the future of condensed matter physics and advanced quantum engineering.

Keywords: Artificial intelligence, quantum materials, machine learning, deep learning, materials informatics, spintronics, topological insulators, computational materials science

[This article belongs to Journal of Materials & Metallurgical Engineering ]

How to cite this article: Ravuri Hema Krishna. Role of Artificial Intelligence in Quantum Materials Research. Journal of Materials & Metallurgical Engineering. 2026; 16(02):13-27.
How to cite this URL: Ravuri Hema Krishna. Role of Artificial Intelligence in Quantum Materials Research. Journal of Materials & Metallurgical Engineering. 2026; 16(02):13-27. Available from: https://journals.stmjournals.com/jomme/article=2026/view=256748

References

  1. Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Machine learning for molecular and materials science.  Nature. 2018;559(7715):547-555.  doi:10.1038/s41586-018-0337-2. PMID:
  2. Schmidt J, Marques MRG, Botti S, Marques MAL. Recent advances and applications of machine learning in solid-state materials science. npj Comput Mater. 2019;5:83. doi:10.1038/s41524-019-0221-0.
  3. Carleo G, Troyer M. Solving the quantum many-body problem with artificial neural networks. Science. 2017;355:602-606. doi:10.1126/science.aag2302. PMID:28183973.
  4. Ward L, Agrawal A, Choudhary A, Wolverton C. A general-purpose machine learning framework for predicting properties of inorganic materials. npj Comput Mater. 2016;2:16028. doi:10.1038/npjcompumats.2016.28.
  5. Jain A, Ong SP, Hautier G, Chen W, Richards WD, Dacek S, et al. Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. APL Mater. 2013;1(1). doi:10.1063/1.4812323.
  6. Zunger A. Inverse design in search of materials with target functionalities. Nat Rev Chem. 2018;2(4):0121. doi:10.1038/s41570-018-0121-z.
  7. Batra R, Song L, Ramprasad R. Emerging materials intelligence ecosystems propelled by machine learning. Nat Rev Mater. 2021;6(8):655-678. doi:10.1038/s41578-020-00255-y.
  8. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-444. doi:10.1038/nature14539. PMID:26017442.
  9. Ramprasad R, Batra R, Pilania G, Mannodi-Kanakkithodi A, Kim C. Machine learning in materials informatics: Recent applications and prospects. npj Comput Mater. 2017;3(1):54. doi:10.1038/s41524-017-0056-5.
  10. Isayev O, Oses C, Toher C, Gossett E, Curtarolo S, Tropsha A. Universal fragment descriptors for predicting properties of inorganic crystals. Nat Commun. 2017;8(1):15679. doi:10.1038/ncomms15679. PMID:28580961.
  11. Pilania G, Wang C, Jiang X, Rajasekaran S, Ramprasad R. Accelerating materials property predictions using machine learning. Sci Rep. 2013;3(1):2810. doi:10.1038/srep02810. PMID:24077117.
  12. Behler J, Parrinello M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys Rev Lett. 2007;98(14):146401. doi:10.1103/PhysRevLett.98.146401. PMID:17501293.
  13. Rupp M, Tkatchenko A, Müller KR, von Lilienfeld OA. Fast and accurate modeling of molecular atomization energies with machine learning. Phys Rev Lett. 2012;108(5):058301. doi:10.1103/PhysRevLett.108.058301. PMID:22400967.
  14. Xie T, Grossman JC. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys Rev Lett. 2018;120:145301. doi:10.1103/PhysRevLett.120.
    PMID:29694125.
  15. Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE. Neural message passing for quantum chemistry. In: International Conference on Machine Learning; 2017. p.1263-1272. PMLR.
  16. Ren F, Ward L, Williams T, Laws KJ, Wolverton C, Hattrick-Simpers J, et al. Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments. Sci Adv. 2018;4(4). doi:10.1126/sciadv.aaq1566. PMID:29662953.
  17. Sendek AD, Cubuk ED, Antoniuk ER, Cheon G, Cui Y, Reed EJ. Machine learning-assisted discovery of solid Li-ion conducting materials. Chem Mater. 2019;31(2):342-352. doi:10.1021/
    chemmater.8b03272.
  18. Jha D, Ward L, Paul A, Liao WK, Choudhary A, Wolverton C, et al. ElemNet: Deep learning the chemistry of materials from only elemental composition. Sci Rep. 2018;8(1):17593. doi:10.1038/s41598-018-35934-y. PMID:30514926.
  19. Chen C, Ye W, Zuo Y, Zheng C, Ong SP. Graph networks as a universal machine learning framework for molecules and crystals. Chem Mater. 2019;31(9):3564-3572. doi:10.1021/acs.
    9b01294.
  20. Goodall REA, Lee AA. Predicting materials properties without crystal structure: Deep representation learning from stoichiometry. Nat Commun. 2020;11:6280. doi:10.1038/s41467-020-19964-7. PMID:33293567.
  21. Tshitoyan V, Dagdelen J, Weston L, Dunn A, Rong Z, Kononova O, et al. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature. 2019;571(7763):95-98. doi:10.1038/s41586-019-1335-8. PMID:31270483.
  22. Ceder G, Persson K, Levitan D, Fessenden M, Greenemeier L, Billings L, et al. World changing ideas: 10 ways science may jazz up our gadgets, help to solve our most intractable problems and save lives. Sci Am. 2013;309:34-49. doi:10.1038/scientificamerican1213-36. PMID:24383363.
  23. Butler KT, Frost JM, Walsh A. Ferroelectric materials for solar energy conversion: Photoferroics revisited. Energy Environ Sci. 2015;8:838-848. doi:10.1039/C4EE03523B.
  24. Hautier G, Fischer CC, Jain A, Mueller T, Ceder G. Finding nature’s missing ternary oxide compounds using machine learning and density functional theory. Chem Mater. 2010;22(12):3762-3767. doi:10.1021/cm100795d.
  25. Meredig B, Agrawal A, Kirklin S, Saal JE, Doak JW, Thompson A, et al. Combinatorial screening for new materials in unconstrained composition space with machine learning. Phys Rev B. 2014;89(9):094104. doi:10.1103/PhysRevB.89.094104.
  26. Wang AYT, Murdock RJ, Kauwe SK, Oliynyk AO, Gurlo A, Brgoch J, et al. Machine learning for materials scientists: An introductory guide toward best practices. Chem Mater. 2020;32(12):4954-4965. doi:10.1021/acs.chemmater.0c01907.
  27. von Lilienfeld OA, Müller KR, Tkatchenko A. Exploring chemical compound space with quantum-based machine learning. Nat Rev Chem. 2020;4(7):347-358. doi:10.1038/s41570-020-0189-9. PMID:37127950.
  28. Noé F, Tkatchenko A, Müller KR, Clementi C. Machine learning for molecular simulation. Annu Rev Phys Chem. 2020;71(1):361-390. doi:10.1146/annurev-physchem-042018-052331. PMID:32092281.
  29. Choudhary K, DeCost B, Chen C, Jain A, Tavazza F, Cohn R, et al. Recent advances and applications of deep learning methods in materials science. npj Comput Mater. 2022;8(1):59. doi:10.1038/s41524-022-00734-6.
  30. Settles B. Active Learning Literature Survey. Madison (WI): University of Wisconsin-Madison; 2010. doi:10.48550/arXiv.1206.5533.
  31. Schleder GR, Padilha ACM, Acosta CM, Costa M, Fazzio A. From DFT to machine learning: Recent approaches to materials science—a review. J Phys Mater. 2019;2(3):032001. doi:10.1088/2515-7639/ab084b.
  32. Agrawal A, Choudhary A. Perspective: Materials informatics and big data: Realization of the ‘fourth paradigm’ of science in materials science. APL Mater. 2016;4:053208. doi:10.1063/1.4946894.
  33. Lookman T, Balachandran PV, Xue D, Yuan R. Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design. npj Comput Mater. 2019;5(1):21. doi:10.1038/s41524-019-0153-8.
  34. Zhang Y, Kitchaev DA, Yang J, Chen T, Dacek ST, Sarmiento-Pérez RA, et al. Efficient first-principles prediction of solid stability: Towards chemical accuracy. npj Comput Mater. 2018;4(1):9. doi:10.1038/s41524-018-0065-z.
  35. Schütt KT, Sauceda HE, Kindermans PJ, Tkatchenko A, Müller KR. SchNet—a deep learning architecture for molecules and materials. J Chem Phys. 2018;148(24):241722. doi:10.1063/1.5019779. PMID:29960322.
  36. Jablonka KM, Schwaller P, Ortega-Guerrero A, Smit B. Leveraging large language models for predictive chemistry. Nat Mach Intell. 2024;6(2):161-169. doi:10.1038/s42256-023-00788-1.
  37. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. Adv Neural Inf Process Syst. 2017;30.
  38. Kingma DP, Welling M. Auto-encoding variational Bayes. In: International Conference on Learning Representations; 2014. doi:10.48550/arXiv.1312.6114.
  39. Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial nets. Adv Neural Inf Process Syst. 2014;27.
  40. Zhang L, Han J, Wang H, Car R, E W. Deep potential molecular dynamics: A scalable model with the accuracy of quantum mechanics. Phys Rev Lett. 2018;120(14):143001. doi:10.1103/PhysRevLett.120.143001. PMID:29694129.
  41. Draxl C, Scheffler M. NOMAD: The FAIR concept for big data-driven materials science. MRS Bull. 2018;43(9):676-682. doi:10.1557/mrs.2018.208.
  42. Curtarolo S, Setyawan W, Hart GLW, Jahnatek M, Chepulskii RV, Taylor RH, et al. AFLOW: An automatic framework for high-throughput materials discovery. Comput Mater Sci. 2012;58:218-226. doi:10.1016/j.commatsci.2012.02.005.
Regular Issue Subscription Original Research
Volume 16
Issue 02
Received 22/05/2026
Accepted 28/05/2026
Published 22/06/2026
Publication Time 31 Days

Login

My IP

PlumX Metrics

Support