{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T20:50:33Z","timestamp":1777150233781,"version":"3.51.4"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T00:00:00Z","timestamp":1744416000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T00:00:00Z","timestamp":1744416000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>This study investigates the application of artificial intelligence (AI) to accelerate the development of metal alloys for space applications, with a focus on aluminum, nickel, and titanium alloys. The research integrates data analysis, feature selection, and machine learning (ML) models to predict critical alloy properties for space environments, including Young\u2019s modulus, yield strength, tensile strength, specific heat, and the coefficient of thermal expansion. The study optimises ML models, such as multi-layer perceptrons and ensemble techniques, demonstrating superior predictive accuracy compared to traditional benchmarks. Additionally, predictive models are employed to recommend novel alloy compositions, potentially enhancing specific properties crucial for aerospace applications. The proposed framework identifies significant predictors through correlation analysis, optimises models to achieve superior predictive accuracy, and recommends novel alloy compositions with the potential for enhanced performance in space applications. This study significantly contributes to materials science by integrating AI with traditional methods, offering a more efficient and targeted approach to alloy development, with the potential to enhance the design and durability of space vehicles and structures.<\/jats:p>","DOI":"10.1007\/s44163-025-00260-6","type":"journal-article","created":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T10:05:14Z","timestamp":1744452314000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["AI-driven optimisation of metal alloys for space applications"],"prefix":"10.1007","volume":"5","author":[{"given":"Luke","family":"Rickard","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adamantios","family":"Bampoulas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meena","family":"Laad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eleni","family":"Mangina","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,12]]},"reference":[{"key":"260_CR1","unstructured":"Ritchie H, Roser M. Yearly Number of Objects Launched into Outer Space. 2023. https:\/\/ourworldindata.org\/grapher\/yearly-number-of-objects-launched-into-outer-space. Accessed 17 July 2024."},{"key":"260_CR2","doi-asserted-by":"publisher","first-page":"99810","DOI":"10.1117\/12.2237966","volume-title":"Proc SPIE 9981, planetary defense and space environment applications","author":"MG Pelizzo","year":"2016","unstructured":"Pelizzo MG, Corso AJ, Tessarolo E, Zuppella P, B\u00f6ttger R, Huebner R, Della Corte V, Palumbo P, Taglioni G, Preti G, et al. Optical components in harsh space environment. In: Dur R, editor., et al., Proc SPIE 9981, planetary defense and space environment applications. Bellingham: SPIE; 2016. p. 99810. https:\/\/doi.org\/10.1117\/12.2237966."},{"key":"260_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.tust.2023.105319","volume":"140","author":"Y Xia","year":"2023","unstructured":"Xia Y, Zhang C, Wang C, al. Prediction of bending strength of glass fiber reinforced methacrylate-based pipeline uv-cipp rehabilitation materials based on machine learning. Tunn Undergr Sp Technol. 2023;140: 105319.","journal-title":"Tunn Undergr Sp Technol"},{"key":"260_CR4","doi-asserted-by":"publisher","first-page":"235","DOI":"10.3390\/met14020235","volume":"14","author":"S Liu","year":"2024","unstructured":"Liu S, Yang C. Machine learning design for high-entropy alloys: models and algorithms. Metals. 2024;14:235.","journal-title":"Metals"},{"key":"260_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.compscitech.2022.109425","volume":"224","author":"B Liu","year":"2022","unstructured":"Liu B, Vu-Bac N, Zhuang X. Stochastic integrated machine learning based multiscale approach for the prediction of the thermal conductivity in carbon nanotube reinforced polymeric composites. Compos Sci Technol. 2022;224: 109425.","journal-title":"Compos Sci Technol"},{"key":"260_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.compstruct.2021.114269","volume":"273","author":"B Liu","year":"2021","unstructured":"Liu B, Vu-Bac N, Rabczuk T. A stochastic multiscale method for the prediction of the thermal conductivity of polymer nanocomposites through hybrid machine learning algorithms. Compos Struct. 2021;273: 114269.","journal-title":"Compos Struct"},{"key":"260_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.compstruct.2022.115393","volume":"289","author":"B Liu","year":"2022","unstructured":"Liu B, Vu-Bac N, Zhuang X, et al. Stochastic full-range multiscale modeling of thermal conductivity of polymeric carbon nanotubes composites: a machine learning approach. Compos Struct. 2022;289: 115393.","journal-title":"Compos Struct"},{"key":"260_CR8","unstructured":"Aerospace Aluminum. https:\/\/www.industrialmetalsupply.com\/blog\/aerospace-aluminum. Accessed 17 July 2024."},{"key":"260_CR9","doi-asserted-by":"publisher","DOI":"10.1108\/00022660110694995","author":"I Sen","year":"2001","unstructured":"Sen I, Peck P. Development and application of nickel alloys in aerospace engineering. Metal Ions Life Sci. 2001. https:\/\/doi.org\/10.1108\/00022660110694995.","journal-title":"Metal Ions Life Sci"},{"key":"260_CR10","unstructured":"3 Common Metals in Aerospace Systems Including Al and Ti. https:\/\/nsaerollc.com\/3-common-metals-aerospace-systems\/. Accessed 17 July 2024."},{"key":"260_CR11","unstructured":"Global integrated drought monitoring and prediction system (GIDMaPS) data sets. https:\/\/www.issnationallab.org\/alpha-space-misse-hwhap\/#:~:text=The%20harsh%20environment%20of%20space%20includes%20exposure%20to%20extreme%20heat,necessary%20for%20future%20mission%20success. Accessed 17 July 2024."},{"key":"260_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2022.103398","volume":"176","author":"B Liu","year":"2023","unstructured":"Liu B, Vu-Bac N, Zhuang X, et al. Al-demat: a web-based expert system platform for computationally expensive models in materials design. Adv Eng Softw. 2023;176: 103398.","journal-title":"Adv Eng Softw"},{"key":"260_CR13","doi-asserted-by":"publisher","first-page":"19447","DOI":"10.1007\/s10853-022-07793-6","volume":"57","author":"UMHU Kankanamge","year":"2022","unstructured":"Kankanamge UMHU, Reiner J, Ma X, Gallo SC, Xu W. Machine learning guided alloy design of high-temperature NiTiHf shape memory alloys. J Mater Sci. 2022;57:19447\u201365.","journal-title":"J Mater Sci"},{"key":"260_CR14","doi-asserted-by":"crossref","unstructured":"Buranich V, Rogoz V, Postolnyi B, Pogrebnjak A. Predicting the properties of the refractory high-entropy alloys for additive manufacturing-based fabrication and mechatronic applications. 2020 IEEE 10th International Conference Nanomaterials: Applications and Properties (NAP). 2020.","DOI":"10.1109\/NAP51477.2020.9309720"},{"key":"260_CR15","unstructured":"Li N, Zhao S, Zhang Z. Predicting the Properties of the Refractory High-Entropy Alloys for Additive Manufacturing-Based Fabrication and Mechatronic Applications. 2021 IEEE 6th International Conference on Big Data Analytics (ICBDA). 2021."},{"key":"260_CR16","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.paerosci.2018.03.004","volume":"98","author":"MZ Naser","year":"2018","unstructured":"Naser MZ, Chehab AI. Materials and design concepts for space-resilient structures. Prog Aerosp Sci. 2018;98:74\u201390.","journal-title":"Prog Aerosp Sci"},{"key":"260_CR17","doi-asserted-by":"crossref","unstructured":"Naser MZ. Extraterrestrial construction materials. Progress in Materials Science. 2019.","DOI":"10.1016\/j.pmatsci.2019.100577"},{"key":"260_CR18","doi-asserted-by":"crossref","unstructured":"Nalc\u0131 AS, \u00c7etinkaya M, Karalar AB. Estimation of strain values of aluminum based aerospace materials by using artificial neural networks. 2023 10th International Conference on Recent Advances in Air and Space Technologies, RAST. 2023","DOI":"10.1109\/RAST57548.2023.10197946"},{"key":"260_CR19","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6668\/ac80d8","volume":"35","author":"M Yazdani-Asrami","year":"2022","unstructured":"Yazdani-Asrami M, Sadeghi A, Song W, Madureira A, Murta-Pina J, Morandi A, Parizh M. Artificial intelligence methods for applied superconductivity: material, design, manufacturing, testing, operation, and condition monitoring. Supercond Sci Technol. 2022;35: 123001.","journal-title":"Supercond Sci Technol"},{"key":"260_CR20","unstructured":"MATWEB. https:\/\/www.matweb.com\/. Accessed 17 July 2024."},{"key":"260_CR21","doi-asserted-by":"publisher","first-page":"2873","DOI":"10.1007\/s11661-021-06279-5","volume":"52","author":"M Hu","year":"2021","unstructured":"Hu M, Tan Q, Knibbe R, et al. Prediction of mechanical properties of wrought aluminium alloys using feature engineering assisted machine learning approach. Metall Mater Trans A. 2021;52:2873\u201384.","journal-title":"Metall Mater Trans A"},{"key":"260_CR22","doi-asserted-by":"crossref","unstructured":"Li X, Sun J, Chen X. Machine learning-based prediction of high-entropy alloy hardness: design and experimental validation of superior hardness. Trans Indian Inst Met. 2024;77(11):3973\u201381.","DOI":"10.1007\/s12666-024-03450-5"},{"key":"260_CR23","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1038\/s41578-021-00340-w","volume":"6","author":"GLW Hart","year":"2021","unstructured":"Hart GLW, Mueller T, Toher C, Curtarolo S. Machine learning for alloys. Nat Rev Mater. 2021;6:730\u201355.","journal-title":"Nat Rev Mater"},{"key":"260_CR24","first-page":"9","volume":"2","author":"MFN Taufique","year":"2024","unstructured":"Taufique MFN, Mamun O, Roy A, et al. Machine learning guided prediction of the yield strength and hardness of multi-principal element alloys. Nat Revi Mater. 2024;2:9.","journal-title":"Nat Revi Mater"},{"key":"260_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.compstruct.2023.117601","volume":"327","author":"B Liu","year":"2024","unstructured":"Liu B, Lu W, Olofsson T, et al. Stochastic interpretable machine learning based multiscale modeling in thermal conductivity of polymeric graphene-enhanced composites. Compos Struct. 2024;327: 117601.","journal-title":"Compos Struct"},{"key":"260_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.techsoc.2023.102347","volume":"75","author":"B Liu","year":"2023","unstructured":"Liu B, Lu Penaka S R, W, et al. Data-driven quantitative analysis of an integrated open digital ecosystems platform for user-centric energy retrofits: a case study in northern Sweden. Technol Soc. 2023;75: 102347.","journal-title":"Technol Soc"},{"key":"260_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.mechmat.2019.103280","volume":"142","author":"B Liu","year":"2020","unstructured":"Liu B, Vu-Bac N, Zhuang X, Rabczuk T. Stochastic multiscale modeling of heat conductivity of polymeric clay nanocomposites. Mech Mater. 2020;142: 103280.","journal-title":"Mech Mater"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00260-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-025-00260-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00260-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T10:05:20Z","timestamp":1744452320000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-025-00260-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,12]]},"references-count":27,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["260"],"URL":"https:\/\/doi.org\/10.1007\/s44163-025-00260-6","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,12]]},"assertion":[{"value":"10 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 April 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"35"}}