iamrp.net-1gDU-1790643545-g485340863 1790643545 Institute for Advanced Materials Research Press info@iamrp.net Institute for Advanced Materials Research Press Journal of Computational and Data-Driven Materials Engineering J. Comput. Data-Driven Mater. Eng. 3149-9368 2026 5 1 Fatima Al-Zahra Amina El Idrissi 2026 10.68159/g485340863 https://iamrp.net/pub/journal/2/article/g485340863 Richard C, Pichon PA, Nováková J, Irannejad Najafabadi N, Sá J. Application of machine learning to nanomaterial design with silver nanoprisms. Discov Nano. 2026;21(1):65. 10.1186/s11671-026-04503-y Otyepka M, Pykal M, Otyepka M. Advancing materials discovery through artificial intelligence. Appl Mater Today. 2025;47:102981. 10.1016/j.apmt.2025.102981 Xu RHJ. Workflows and automated platforms for nanocrystal synthesis, purification, and characterization [dissertation]. Urbana (IL): University of Illinois Urbana-Champaign; 2025. Callaghan S. Toward machine learning-enhanced high-throughput experimentation for chemistry. Patterns (N Y). 2021;2(3):100221. 10.1016/j.patter.2021.100221 Glasby LT, Moghadam PZ. Hydrogen storage in MOFs: Machine learning for finding a needle in a haystack. Patterns (N Y). 2021;2(7):100305. 10.1016/j.patter.2021.100305 Miran W, Imamura G, Okamoto A. Architecting data-driven microbial electrochemistry from scratch. Patterns (N Y). 2022;3(11):100637. 10.1016/j.patter.2022.100637 Persaud D, Ward L, Hattrick-Simpers J. Reproducibility in materials informatics: Lessons from ‘A general-purpose machine learning framework for predicting properties of inorganic materials’. Digit Discov. 2024;3(2):281-6. 10.1039/D3DD00199G Pouchard L, Lin Y, Van Dam H. Replicating machine learning experiments in materials science. In: Parallel computing: Technology trends. Amsterdam: IOS Press; 2020. p. 743-55. 10.3233/APC200105 Kapoor S, Narayanan A. Leakage and the reproducibility crisis in machine-learning-based science. Patterns (N Y). 2023;4(9):100804. 10.1016/j.patter.2023.100804 Yu X, Dutta S, Andreo J, Wuttke S. Identifying synthetic variables influencing the reproducible microfluidic synthesis of ZIF nano- and micro-particles. Commun Mater. 2024;5(1):210. 10.1038/s43246-024-00644-8 Sun W, David N. A critical reflection on attempts to machine-learn materials synthesis insights from text-mined literature recipes. Faraday Discuss. 2025;256:614-38. 10.1039/D4FD00112E Hemmer K, Boström HLB, Krause S, Lotsch BV, Fischer RA. Strategies to achieve reproducible synthesis of phase-pure Zr-porphyrin metal-organic frameworks. Commun Mater. 2024;5(1):264. 10.1038/s43246-024-00690-2 Li K, DeCost B, Choudhary K, Hattrick-Simpers J. A reproducibility study of atomistic line graph neural networks for materials property prediction. Digit Discov. 2024;3(6):1123-9. 10.1039/D4DD00064A Pineau J, Vincent-Lamarre P, Sinha K, Larivière V, Beygelzimer A, d'Alché-Buc F, et al. Improving reproducibility in machine learning research (a report from the NeurIPS 2019 reproducibility program). J Mach Learn Res. 2021;22(164):1-20. Ho SY, Wong L, Goh WW. Avoid oversimplifications in machine learning: Going beyond the class-prediction accuracy. Patterns (N Y). 2020;1(2):100025. 10.1016/j.patter.2020.100025 Kapoor S, Cantrell EM, Peng K, Pham TH, Bail CA, Gundersen OE, et al. REFORMS: Consensus-based recommendations for machine-learning-based science. Sci Adv. 2024;10(18):eadk3452. 10.1126/sciadv.adk3452 Butler KT, Choudhary K, Csányi G, Ganose AM, Kalinin SV, Morgan D. Setting standards for data driven materials science. NPJ Comput Mater. 2024;10(1):231. 10.1038/s41524-024-01411-6 Semmelrock H, Ross-Hellauer T, Kopeinik S, Theiler D, Haberl A, Thalmann S, et al. Reproducibility in machine-learning-based research: Overview, barriers, and drivers. AI Mag. 2025;46(2):e70002. 10.1002/aaai.70002 Leipzig J, Nüst D, Hoyt CT, Soiland-Reyes S, Ram K, Greenberg J. The role of metadata in reproducible computational research. Patterns (N Y). 2021;2(9):100322. 10.1016/j.patter.2021.100322 Hartley M, Olsson TSG. dtoolAI: Reproducibility for deep learning. Patterns (N Y). 2020;1(5):100073. 10.1016/j.patter.2020.100073 Horton MK, Woods-Robinson R. Addressing the critical need for open experimental databases in materials science. Patterns (N Y). 2021;2(12):100411. 10.1016/j.patter.2021.100411 Kuckertz P, Fuchs B, Schönau J, Gardian H, Knosala K, Arellano Ruiz ES, et al. A framework for reproducibly managing coupled research software and data assets based on shared transformation functions. Patterns (N Y). 2026;7(5):101493. 10.1016/j.patter.2026.101493 Schackart KE 3rd, Imker HJ, Cook CE. Detailed implementation of a reproducible machine learning-enabled workflow. Data Sci J. 2024;23(1):23. 10.5334/dsj-2024-023 Aguilar-Bejarano E, Lea D, Sivakumar K, Mase JM, Omidvar R, Li R, et al. Helix 1.0: An open-source framework for reproducible and interpretable machine learning on tabular scientific data [Preprint]. arXiv:2507.17791. 2025. 10.48550/arXiv.2507.17791 Turkyilmaz-van der Velden Y, Dintzner N, Teperek M. Reproducibility starts from you today. Patterns (N Y). 2020;1(6):100099. 10.1016/j.patter.2020.100099 Vescovi R, Chard R, Saint ND, Blaiszik B, Pruyne J, Bicer T, et al. Linking scientific instruments and computation: Patterns, technologies, and experiences. Patterns (N Y). 2022;3(10):100606. 10.1016/j.patter.2022.100606 Pelser T, Weinand JM, Kuckertz P, Stolten D. ETHOS.REFLOW: An open-source workflow for reproducible renewable energy potential assessments. Patterns (N Y). 2025;6(2):101172. 10.1016/j.patter.2025.101172 Heil BJ, Hoffman MM, Markowetz F, Lee SI, Greene CS, Hicks SC. Reproducibility standards for machine learning in the life sciences. Nat Methods. 2021;18(10):1132-5. 10.1038/s41592-021-01256-7