iamrp.net-Gj5B-1791491022-m083609483 1791491022 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 Anna Novak Tomas Hruby 2026 10.68159/m083609483 https://iamrp.net/pub/journal/2/article/m083609483 Mlinarić A, Horvat M, Šupak Smolčić V. Dealing with the positive publication bias: Why you should really publish your negative results. Biochem Med (Zagreb). 2017;27(3):030201. 10.11613/BM.2017.030201 Heesen R, Bright LK. Publication bias is bad for science if not necessarily scientists. R Soc Open Sci. 2025;12(4):240688. 10.1098/rsos.240688 Otyepka M, Pykal M, Otyepka M. Advancing materials discovery through artificial intelligence. Appl Mater Today. 2025;47:102981. 10.1016/j.apmt.2025.102981 Jain A. Machine learning in materials research: Developments over the last decade and challenges for the future. Curr Opin Solid State Mater Sci. 2024;33(1):101189. 10.1016/j.cossms.2024.101189 DeCost BL, Hattrick-Simpers JR, Trautt Z, Kusne AG, Campo E, Green ML. Scientific AI in materials science: A path to a sustainable and scalable paradigm. Mach Learn Sci Technol. 2020;1(3):033001. Olivetti EA, Cole JM, Kim E, Kononova O, Ceder G, Han TYJ, et al. Data-driven materials research enabled by natural language processing and information extraction. Appl Phys Rev. 2020;7(4):041317. 10.1063/5.0021106 Boyce B, Dingreville R, Desai S, Walker E, Shilt T, Bassett KL, et al. Machine learning for materials science: Barriers to broader adoption. Matter. 2023;6(5):1320-3. 10.1016/j.matt.2023.03.028 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 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. 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 Yelgel ÖC, Yelgel C. A review of machine learning approaches for the discovery of thermoelectric materials. Adv Phys X. 2025;10(1):2536269. 10.1080/23746149.2025.2536269 Huang L, Zhang X, Li S, Xie R. Text mining-assisted machine learning prediction and experimental validation of emission wavelengths. NPJ Comput Mater. 2026;12:98. 10.1038/s41524-026-01967-5 Zhou ZH. Machine learning. Singapore: Springer Singapore; 2021. 10.1007/978-981-15-1967-3 Wu Y, Liu L, Deng W, Jiang H, Li R, Hu M, et al. Data distribution matters: Enhancing model generalization in data-driven materials discovery by intentionally exploiting both positive and negative data. Acta Physico-Chim Sin. 2026:100291. 10.1016/j.actphy.2026.100291 Brazil R. Illuminating ‘the ugly side of science’: Fresh incentives for reporting negative results. Nature. 2024. 10.1038/d41586-024-01389-7 Cranford S. Want for nothing, need for null, useful output from negative results. Matter. 2024;7(5):1679-83. 10.1016/j.matt.2024.04.005 Bespalov A, Steckler T, Skolnick P. Be positive about negatives–recommendations for the publication of negative (or null) results. Eur Neuropsychopharmacol. 2019;29(12):1312-20. 10.1016/j.euroneuro.2019.10.007 Toniato A, Vaucher AC, Laino T, Graziani M. Negative chemical data boosts language models in reaction outcome prediction. Sci Adv. 2025;11(24):eadt5578. 10.1126/sciadv.adt5578 Yin X, Ma J, Zhang S, Cui G. Machine learning drives a new paradigm in inorganic solid-state electrolytes research. Comput Mater Today. 2026;10:100051. 10.1016/j.commt.2026.100051 Karl F, Kemeter LM, Dax G, Sierak P. Position: Embracing negative results in machine learning. arXiv [Preprint]. 2024;arXiv:2406.03980. 10.48550/arXiv.2406.03980 Zhuang Y, Yang X, Zhang C, Jia X, Zhang D, Li M, et al. Materials databases: Foundations of modern digital materials. Precis Chem. 2026. 10.1021/prechem.5c00449 Lau ML, Burleigh A, Terry J, Long M. Materials characterization: Can artificial intelligence be used to address reproducibility challenges? J Vac Sci Technol A. 2023;41(6):060801. 10.1116/6.0002809 Bruckner T, Wieschowski S, Heider M, Deutsch S, Drude N, Tölch U, et al. Measurement challenges and causes of incomplete results reporting of biomedical animal studies: Results from an interview study. PLoS One. 2022;17(8):e0271976. 10.1371/journal.pone.0271976 Colombo M, Duev G, Nuijten MB, Sprenger J. Statistical reporting inconsistencies in experimental philosophy. PLoS One. 2018;13(4):e0194360. 10.1371/journal.pone.0194360 Maloney MP, Coley CW, Genheden S, Carson N, Helquist P, Norrby PO, et al. Negative data in data sets for machine learning training. Org Lett. 2023;25(17):2945-7. 10.1021/acs.orglett.3c01282 Salvador RB, Cavallari DC, Rands D, Tomotani BM. Publication practice in taxonomy: Global inequalities and potential bias against negative results. PLoS One. 2022;17(6):e0269246. 10.1371/journal.pone.0269246 Alpaydin E. Machine learning. Rev updated ed. Cambridge (MA): MIT Press; 2021. 280 p. Reau M, Lagarde N, Zagury JF, Montes M. Nuclear receptors database including negative data (NR-DBIND): A database dedicated to nuclear receptors binding data including negative data and pharmacological profile: Miniperspective. J Med Chem. 2019;62(6):2894-904. 10.1021/acs.jmedchem.8b01105 Murty S, Russell RR. Bad outputs. In: Ray SC, Chambers RG, Kumbhakar SC, editors. Handbook of production economics. Singapore: Springer; 2022. p. 483-535. 10.1007/978-981-10-3455-8_3