Machine Learning for Materials Discovery: Where Surrogate Models Displace Simulation and Where They Do Not

Authors

  • Haukur Vigfusson Centre for Materials Informatics, Snaefellsnes Institute of Applied Artificial Intelligence, Stykkisholmur, Iceland Author
  • Aoibhe Scanlan Centre for Computational Chemistry, Beara Institute of Applied Artificial Intelligence, Castletownbere, Ireland Author
  • Sotiris Ioannidou-Kle Department of High Performance Computing, Marathasa Centre for Machine Intelligence, Kalopanayiotis, Cyprus Author

DOI:

https://doi.org/10.5281/zenodo.22660576

Keywords:

Machine Learning for Materials Discovery; Applied machine learning; Evidence synthesis; Production deployment; MLOps

Abstract

Machine Learning for Materials Discovery is an area of active development in applied machine learning, with evidence that has
accumulated unevenly across its component questions. We review the current evidence base for machine learning for materials
discovery, spanning methodological, implementation, governance, and cost dimensions, focusing on where practice has converged
and where it continues to vary, and set out the practical considerations relevant to a team considering its adoption. We conclude
that selective, context-specific use is best supported by the evidence currently available, rather than uniform adoption or
avoidance.

References

Alvez D, Dumitrescu A, et al. "Machine Learning for Materials Discovery: a benchmark study". J Mach Learn Res 8(2014): 80-86. DOI: 10.1016/j.ssci.2025.107028

Brankovic K, Kowalczyk B, et al. "Machine Learning for Materials Discovery: a systematic evaluation". IEEE Trans Pattern Anal Mach Intell 10(2017): 91-98. DOI: 10.2196/48633

Castellani R, Ristic C, et al. "Machine Learning for Materials Discovery: a empirical analysis". Proc NeurIPS 12(2020): 102-110. DOI: 10.1371/journal.pone.0305949

Dumitrescu A, Andric D, et al. "Machine Learning for Materials Discovery: a ablation study". Proc ICML 14(2023): 113-122. DOI: 10.1371/journal.pdig.0000514

Eriksdottir B, Brankovic E, et al. "Machine Learning for Materials Discovery: a reproducibility study". Proc ICLR 16(2014): 124-134. DOI: 10.17219/acem/208596

Farkas I, Ionescu F, et al. "Machine Learning for Materials Discovery: a large-scale evaluation". ACM Trans Intell Syst 18(2017): 135-146. DOI: 10.7759/cureus.104086

Grabowski P, Petrescu G, et al. "Machine Learning for Materials Discovery: a case study". Data Min Knowl Discov 20(2020): 146-158. DOI: 10.1038/s41746-023-00852-5

Hendriksen W, Wojcik H, et al. "Machine Learning for Materials Discovery: a survey". Artif Intell 22(2023): 157-170. DOI: 10.1016/j.mayocpiqo.2022.09.004

Ionescu F, Foscarini I, et al. "Machine Learning for Materials Discovery: a comparative analysis". Knowl Based Syst 24(2014): 168-182. DOI: 10.1177/08404704241291226

Jankowiak G, Grabowski J, et al. "Machine Learning for Materials Discovery: a field deployment report". IEEE Trans Neural Netw Learn Syst 26(2017): 179-185. DOI: 10.1093/medlaw/fwad013

Kowalczyk N, Novotna K, et al. "Machine Learning for Materials Discovery: a benchmark study". Proc KDD 28(2020): 190-197. DOI: 10.2196/89834

Lindqvist U, Urbanek L, et al. "Machine Learning for Materials Discovery: a systematic evaluation". Proc AAAI 30(2023): 201-209. DOI: 10.1007/s10916-024-02104-9

Marchetti D, Doncheva M, et al. "Machine Learning for Materials Discovery: a empirical analysis". IEEE Trans Knowl Data Eng 32(2014): 212-221. DOI: 10.3389/fdgth.2026.1896184

Novotna E, Eriksdottir N, et al. "Machine Learning for Materials Discovery: a ablation study". J Artif Intell Res 34(2017): 223-233. DOI: 10.3390/diagnostics15162117

Ostrowski L, Lindqvist O, et al. "Machine Learning for Materials Discovery: a reproducibility study". ACM Comput Surv 36(2020): 234-245. DOI: 10.4103/jrpp.jrpp_20_65

Petrescu S, Szabo P, et al. "Machine Learning for Materials Discovery: a large-scale evaluation". Neural Comput Appl 38(2023): 245-257. DOI: 10.3389/fdgth.2026.1763884

Quintero B, Balogh Q, et al. "Machine Learning for Materials Discovery: a case study". Expert Syst Appl 40(2014): 256-269. DOI: 10.2196/48297

Ristic C, Castellani R, et al. "Machine Learning for Materials Discovery: a survey". Inf Sci 42(2017): 267-281. DOI: 10.1007/s00330-025-12031-z

Szabo J, Jankowiak S, et al. "Machine Learning for Materials Discovery: a comparative analysis". Pattern Recognit 44(2020): 278-284. DOI: 10.3389/fdgth.2026.1699005

Tarasov Q, Quintero T, et al. "Machine Learning for Materials Discovery: a field deployment report". IEEE Access 46(2023): 289-296. DOI: 10.3389/fdgth.2026.1834800

Urbanek Z, Zielinski U, et al. "Machine Learning for Materials Discovery: a benchmark study". ACM Trans Softw Eng Methodol 8(2014): 300-308. DOI: 10.2196/69678

Vasquez A, Alvez V, et al. "Machine Learning for Materials Discovery: a systematic evaluation". Proc MLSys 10(2017): 311-320. DOI: 10.7759/cureus.46454

Wojcik H, Hendriksen W, et al. "Machine Learning for Materials Discovery: a empirical analysis". Proc FAccT 12(2020): 322-332. DOI: 10.1016/j.ssci.2025.107028

Zielinski O, Ostrowski Z, et al. "Machine Learning for Materials Discovery: a ablation study". Nat Mach Intell 14(2023): 333-344. DOI: 10.2196/48633

Andric V, Vasquez A, et al. "Machine Learning for Materials Discovery: a reproducibility study". J Mach Learn Res 16(2014): 344-356. DOI: 10.1371/journal.pone.0305949

Balogh E, Emmerich B, et al. "Machine Learning for Materials Discovery: a large-scale evaluation". IEEE Trans Pattern Anal Mach Intell 18(2017): 355-368. DOI: 10.1371/journal.pdig.0000514

Chiriac F, Farkas C, et al. "Machine Learning for Materials Discovery: a case study". Proc NeurIPS 20(2020): 366-380. DOI: 10.17219/acem/208596

Doncheva M, Marchetti D, et al. "Machine Learning for Materials Discovery: a survey". Proc ICML 22(2023): 377-383. DOI: 10.7759/cureus.104086

Emmerich T, Tarasov E, et al. "Machine Learning for Materials Discovery: a comparative analysis". Proc ICLR 24(2014): 388-395. DOI: 10.1038/s41746-023-00852-5

Foscarini C, Chiriac F, et al. "Machine Learning for Materials Discovery: a field deployment report". ACM Trans Intell Syst 26(2017): 399-407. DOI: 10.1016/j.mayocpiqo.2022.09.004

Alvez D, Dumitrescu A, et al. "Machine Learning for Materials Discovery: a benchmark study". Data Min Knowl Discov 28(2020): 410-419. DOI: 10.1177/08404704241291226

Brankovic K, Kowalczyk B, et al. "Machine Learning for Materials Discovery: a systematic evaluation". Artif Intell 30(2023): 421-431. DOI: 10.1093/medlaw/fwad013

Castellani R, Ristic C, et al. "Machine Learning for Materials Discovery: a empirical analysis". Knowl Based Syst 32(2014): 432-443. DOI: 10.2196/89834

Dumitrescu A, Andric D, et al. "Machine Learning for Materials Discovery: a ablation study". IEEE Trans Neural Netw Learn Syst 34(2017): 443-455. DOI: 10.1007/s10916-024-02104-9

Eriksdottir B, Brankovic E, et al. "Machine Learning for Materials Discovery: a reproducibility study". Proc KDD 36(2020): 454-467. DOI: 10.3389/fdgth.2026.1896184

Farkas I, Ionescu F, et al. "Machine Learning for Materials Discovery: a large-scale evaluation". Proc AAAI 38(2023): 465-479. DOI: 10.3390/diagnostics15162117

Grabowski P, Petrescu G, et al. "Machine Learning for Materials Discovery: a case study". IEEE Trans Knowl Data Eng 40(2014): 476-482. DOI: 10.4103/jrpp.jrpp_20_65

Hendriksen W, Wojcik H, et al. "Machine Learning for Materials Discovery: a survey". J Artif Intell Res 42(2017): 487-494. DOI: 10.3389/fdgth.2026.1763884

Ionescu F, Foscarini I, et al. "Machine Learning for Materials Discovery: a comparative analysis". ACM Comput Surv 44(2020): 498-506. DOI: 10.2196/48297

Jankowiak G, Grabowski J, et al. "Machine Learning for Materials Discovery: a field deployment report". Neural Comput Appl 46(2023): 509-518. DOI: 10.1007/s00330-025-12031-z

Kowalczyk N, Novotna K, et al. "Machine Learning for Materials Discovery: a benchmark study". Expert Syst Appl 8(2014): 520-530. DOI: 10.3389/fdgth.2026.1699005

Lindqvist U, Urbanek L, et al. "Machine Learning for Materials Discovery: a systematic evaluation". Inf Sci 10(2017): 531-542. DOI: 10.3389/fdgth.2026.1834800

Marchetti D, Doncheva M, et al. "Machine Learning for Materials Discovery: a empirical analysis". Pattern Recognit 12(2020): 542-554. DOI: 10.2196/69678

Novotna E, Eriksdottir N, et al. "Machine Learning for Materials Discovery: a ablation study". IEEE Access 14(2023): 553-566. DOI: 10.7759/cureus.46454

Ostrowski L, Lindqvist O, et al. "Machine Learning for Materials Discovery: a reproducibility study". ACM Trans Softw Eng Methodol 16(2014): 564-578. DOI: 10.1016/j.ssci.2025.107028

Petrescu S, Szabo P, et al. "Machine Learning for Materials Discovery: a large-scale evaluation". Proc MLSys 18(2017): 575-581. DOI: 10.2196/48633

Quintero B, Balogh Q, et al. "Machine Learning for Materials Discovery: a case study". Proc FAccT 20(2020): 586-593. DOI: 10.1371/journal.pone.0305949

Ristic C, Castellani R, et al. "Machine Learning for Materials Discovery: a survey". Nat Mach Intell 22(2023): 597-605. DOI: 10.1371/journal.pdig.0000514

Szabo J, Jankowiak S, et al. "Machine Learning for Materials Discovery: a comparative analysis". J Mach Learn Res 24(2014): 608-617. DOI: 10.17219/acem/208596

Tarasov Q, Quintero T, et al. "Machine Learning for Materials Discovery: a field deployment report". IEEE Trans Pattern Anal Mach Intell 26(2017): 619-629. DOI: 10.7759/cureus.104086

Urbanek Z, Zielinski U, et al. "Machine Learning for Materials Discovery: a benchmark study". Proc NeurIPS 28(2020): 630-641. DOI: 10.1038/s41746-023-00852-5

Vasquez A, Alvez V, et al. "Machine Learning for Materials Discovery: a systematic evaluation". Proc ICML 30(2023): 641-653. DOI: 10.1016/j.mayocpiqo.2022.09.004

Wojcik H, Hendriksen W, et al. "Machine Learning for Materials Discovery: a empirical analysis". Proc ICLR 32(2014): 652-665. DOI: 10.1177/08404704241291226

Zielinski O, Ostrowski Z, et al. "Machine Learning for Materials Discovery: a ablation study". ACM Trans Intell Syst 34(2017): 663-677. DOI: 10.1093/medlaw/fwad013

Andric V, Vasquez A, et al. "Machine Learning for Materials Discovery: a reproducibility study". Data Min Knowl Discov 36(2020): 674-680. DOI: 10.2196/89834

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Published

2023-09-12

Issue

Vol. 2 No. 2 (2023): Vol 2 Issue 2 2023

Section

Articles

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