Reverse design of Mg-Zn-Mn-Sr-Ca alloys for biodegradable implants by interpretable machine learning and genetic algorithm
[Display omitted] •A reverse design approach was employed for biodegradable Mg-Zn-Mn-Sr-Ca alloy.•Interpretable machine learning and multi-objective genetic algorithm were employed.•Four of six optimized alloys met strength and degradation criteria experimentally.•The results support data-driven des...
Saved in:
| Published in | Materials & design Vol. 257; p. 114494 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.09.2025
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0264-1275 1873-4197 |
| DOI | 10.1016/j.matdes.2025.114494 |
Cover
| Abstract | [Display omitted]
•A reverse design approach was employed for biodegradable Mg-Zn-Mn-Sr-Ca alloy.•Interpretable machine learning and multi-objective genetic algorithm were employed.•Four of six optimized alloys met strength and degradation criteria experimentally.•The results support data-driven design of biodegradable Mg alloys for implants.
Biodegradable Mg alloys for implants must meet both mechanical strength and biologically compatible degradation rate to promote bone healing. The present study proposes a reverse engineering approach that utilizes interpretable machine learning and a multi-objective genetic algorithm to design biodegradable Mg-Zn-Mn-Sr-Ca (ZMJX) alloys for load-bearing orthopedic implants. The training of neural networks was facilitated by a dataset comprising 1044 data points, which contained chemical compositions, ultimate compressive strengths (UCS), and in vitro degradation rates (DR). The optimized neural network models predicted UCS and DR with a coefficient of determination exceeding 0.92 on testing data. Shapley additive explanations identified Zn as the most influential element affecting both UCS and DR. A total of six optimal alloys were identified from the Pareto front of two fitness functions (UCS ≥ 240 MPa, DR ≤ 1 mm/y). Four of these alloys satisfied the conflicting dual target criteria, although notable performance discrepancies were observed between reverse design and experimental results. The findings emphasize the necessity to incorporate microstructural and textural characteristics into existing databases, thereby enhancing model accuracy and enabling more reliable, data-driven design of biodegradable Mg alloys for load-bearing implant applications. |
|---|---|
| AbstractList | [Display omitted]
•A reverse design approach was employed for biodegradable Mg-Zn-Mn-Sr-Ca alloy.•Interpretable machine learning and multi-objective genetic algorithm were employed.•Four of six optimized alloys met strength and degradation criteria experimentally.•The results support data-driven design of biodegradable Mg alloys for implants.
Biodegradable Mg alloys for implants must meet both mechanical strength and biologically compatible degradation rate to promote bone healing. The present study proposes a reverse engineering approach that utilizes interpretable machine learning and a multi-objective genetic algorithm to design biodegradable Mg-Zn-Mn-Sr-Ca (ZMJX) alloys for load-bearing orthopedic implants. The training of neural networks was facilitated by a dataset comprising 1044 data points, which contained chemical compositions, ultimate compressive strengths (UCS), and in vitro degradation rates (DR). The optimized neural network models predicted UCS and DR with a coefficient of determination exceeding 0.92 on testing data. Shapley additive explanations identified Zn as the most influential element affecting both UCS and DR. A total of six optimal alloys were identified from the Pareto front of two fitness functions (UCS ≥ 240 MPa, DR ≤ 1 mm/y). Four of these alloys satisfied the conflicting dual target criteria, although notable performance discrepancies were observed between reverse design and experimental results. The findings emphasize the necessity to incorporate microstructural and textural characteristics into existing databases, thereby enhancing model accuracy and enabling more reliable, data-driven design of biodegradable Mg alloys for load-bearing implant applications. Biodegradable Mg alloys for implants must meet both mechanical strength and biologically compatible degradation rate to promote bone healing. The present study proposes a reverse engineering approach that utilizes interpretable machine learning and a multi-objective genetic algorithm to design biodegradable Mg-Zn-Mn-Sr-Ca (ZMJX) alloys for load-bearing orthopedic implants. The training of neural networks was facilitated by a dataset comprising 1044 data points, which contained chemical compositions, ultimate compressive strengths (UCS), and in vitro degradation rates (DR). The optimized neural network models predicted UCS and DR with a coefficient of determination exceeding 0.92 on testing data. Shapley additive explanations identified Zn as the most influential element affecting both UCS and DR. A total of six optimal alloys were identified from the Pareto front of two fitness functions (UCS ≥ 240 MPa, DR ≤ 1 mm/y). Four of these alloys satisfied the conflicting dual target criteria, although notable performance discrepancies were observed between reverse design and experimental results. The findings emphasize the necessity to incorporate microstructural and textural characteristics into existing databases, thereby enhancing model accuracy and enabling more reliable, data-driven design of biodegradable Mg alloys for load-bearing implant applications. |
| ArticleNumber | 114494 |
| Author | Suh, Joung Sik Kim, Jae-Yeon Suh, Byeong-Chan Jang, Jae Hoon |
| Author_xml | – sequence: 1 givenname: Joung Sik surname: Suh fullname: Suh, Joung Sik email: jssuh@kims.re.kr organization: Lightweight Materials Research Division, Korea Institute of Materials Science, Changwon 51508, Republic of Korea – sequence: 2 givenname: Jae Hoon surname: Jang fullname: Jang, Jae Hoon organization: Materials Data & Analysis Research Division, Korea Institute of Materials Science, Changwon 51508, Republic of Korea – sequence: 3 givenname: Byeong-Chan surname: Suh fullname: Suh, Byeong-Chan organization: Lightweight Materials Research Division, Korea Institute of Materials Science, Changwon 51508, Republic of Korea – sequence: 4 givenname: Jae-Yeon surname: Kim fullname: Kim, Jae-Yeon organization: Lightweight Materials Research Division, Korea Institute of Materials Science, Changwon 51508, Republic of Korea |
| BookMark | eNqNkctu3CAUhlmkUpO0b5AFL-ApYGzsTaRq1EukRJF62XSDDnBwGNkwAjfV9OlL4irLqmyOBPo-Hf7_gpzFFJGQK852nPH-3WG3wOqw7AQT3Y5zKUd5Rs6Z6GXDhepek4tSDowJoVp5Tn5_wUfMBWlFwhRp8vRuan7E5i42X3OzBwrznE6F-pSpCcnhlMGBmZGG5ThDXAs1JxriivmYcX1-WcA-hIh0RsgxxIlCdHTCiGuw1TelHNaH5Q155WEu-PbvvCTfP374tv_c3N5_utm_v21s249ro5SzRnDB7OC4teicajs_9G3HRjZ0zDFjPTNOjCDYUE9vPHBALsBJUH17SW42r0tw0MccFsgnnSDo54uUJw25bjajVtK2owLOBm-lcQaktX2nWM-d90Ly6uo21894hNOvms2LkDP9VIA-6K0A_VSA3gqonNw4m1MpGf3_YtcbhjWfx4BZFxsw1hBCRrvWD4R_C_4AnLunmw |
| Cites_doi | 10.1016/j.matdes.2019.108259 10.1016/j.jma.2023.09.010 10.1023/A:1010933404324 10.1214/aos/1013203451 10.1016/j.jallcom.2016.08.164 10.1021/acs.chemrev.5b00691 10.1016/j.jma.2021.12.007 10.1016/j.jmst.2024.09.038 10.1007/s11837-011-0089-z 10.1016/j.artint.2021.103502 10.1016/j.jma.2019.12.001 10.1016/j.jmst.2021.02.021 10.1016/j.actbio.2009.06.028 10.1016/j.jmrt.2022.11.137 10.1016/j.corsci.2018.04.024 10.1016/j.engstruct.2020.110927 10.1016/j.corsci.2012.07.015 10.1016/j.corsci.2007.01.001 10.1016/j.jmst.2021.07.045 10.1016/j.msec.2013.11.011 10.1016/j.jmst.2023.04.072 10.1023/B:STCO.0000035301.49549.88 10.1007/s12540-022-01327-0 10.1016/j.actbio.2018.11.045 10.1038/srep02367 10.4028/www.scientific.net/JBBTE.12.25 10.1002/mgea.54 10.1007/s11661-020-06132-1 10.5923/j.ajbe.20120206.02 10.1016/j.actbio.2014.11.048 10.1038/ncomms11241 10.1016/B978-012470862-4/50036-2 10.1016/j.cossms.2009.04.001 10.1016/S0010-938X(01)00101-9 10.1016/j.commatsci.2021.110881 10.1007/s12540-019-00495-w 10.1016/j.corsci.2019.06.022 10.1016/j.jallcom.2016.10.244 10.1038/s41524-019-0227-7 10.1016/j.jallcom.2023.172007 10.1016/j.matdes.2022.111442 10.1016/j.matlet.2021.130627 10.1038/s41524-019-0153-8 10.1016/j.corsci.2012.08.037 10.1002/sam.10018 10.1016/j.msec.2012.07.042 10.1016/j.commatsci.2021.110544 10.1016/j.msea.2008.06.019 10.1016/j.matdes.2013.06.055 10.1016/j.jma.2021.06.019 10.1016/j.jma.2023.07.005 |
| ContentType | Journal Article |
| Copyright | 2025 |
| Copyright_xml | – notice: 2025 |
| DBID | 6I. AAFTH AAYXX CITATION ADTOC UNPAY DOA |
| DOI | 10.1016/j.matdes.2025.114494 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef Unpaywall for CDI: Periodical Content Unpaywall DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| ExternalDocumentID | oai_doaj_org_article_74c397a108fc4bdba4cc657061dff241 10.1016/j.matdes.2025.114494 10_1016_j_matdes_2025_114494 S0264127525009141 |
| GroupedDBID | --K --M -~X .~1 0R~ 1B1 1~. 29M 4.4 457 4G. 5GY 5VS 6I. 7-5 8P~ 9JN AABNK AABXZ AAEDT AAEDW AAFTH AAKOC AALRI AAOAW AAQFI AAQXK AATTM AAXKI AAXUO AAYWO ABJNI ABMAC ABWVN ABXDB ACDAQ ACGFS ACLOT ACRLP ACRPL ACVFH ADBBV ADCNI ADEZE ADMUD ADNMO ADVLN AEBSH AEIPS AEKER AEUPX AEZYN AFJKZ AFPUW AFRZQ AFTJW AGHFR AGQPQ AGUBO AHHHB AHJVU AIEXJ AIGII AIIUN AIKHN AITUG AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU APXCP ASPBG AVWKF AXJTR AZFZN BCNDV BJAXD BKOJK BLXMC EBS EFJIC EFKBS EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA GROUPED_DOAJ HVGLF HZ~ IHE J1W JJJVA KOM M41 MAGPM MO0 O9- OAUVE OK1 P-8 P-9 P2P PC. Q38 R2- RNS ROL RPZ SDF SDG SDP SEW SMS SPC SSM SST SSZ T5K WUQ ~G- AAYXX CITATION ADTOC AGCQF UNPAY |
| ID | FETCH-LOGICAL-c369t-77dcb2120c8d1ccedd735f8635090850d0bcf0bd29a2088886bfa1ae12ad4a763 |
| IEDL.DBID | AIKHN |
| ISSN | 0264-1275 1873-4197 |
| IngestDate | Fri Oct 03 12:43:38 EDT 2025 Tue Aug 19 09:34:45 EDT 2025 Wed Oct 01 05:26:14 EDT 2025 Sat Oct 04 17:01:57 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Degradation Reverse design, Machine learning Biodegradable Strength Magnesium alloy |
| Language | English |
| License | This is an open access article under the CC BY license. cc-by |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c369t-77dcb2120c8d1ccedd735f8635090850d0bcf0bd29a2088886bfa1ae12ad4a763 |
| OpenAccessLink | https://www.sciencedirect.com/science/article/pii/S0264127525009141 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_74c397a108fc4bdba4cc657061dff241 unpaywall_primary_10_1016_j_matdes_2025_114494 crossref_primary_10_1016_j_matdes_2025_114494 elsevier_sciencedirect_doi_10_1016_j_matdes_2025_114494 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | September 2025 2025-09-00 2025-09-01 |
| PublicationDateYYYYMMDD | 2025-09-01 |
| PublicationDate_xml | – month: 09 year: 2025 text: September 2025 |
| PublicationDecade | 2020 |
| PublicationTitle | Materials & design |
| PublicationYear | 2025 |
| Publisher | Elsevier Ltd Elsevier |
| Publisher_xml | – name: Elsevier Ltd – name: Elsevier |
| References | Haynes, Lide, Bruno (b0260) 2012 Lee, Sagong, Jung, Kim, Kim (b0160) 2023; 22 Suh, Kim, Yim, Suh, Bae, Lee (b0190) 2023; 968 Song (b0050) 2007; 49 W.M. Haynes, D.R. Lide, eds., CRC handbook of chemistry and physics: a ready-reference book of chemical and physical data, 92nd ed., 2011–2012, CRC Press, Boca Raton, Fla., 2011. Bonora, Andrei, Eliezer, Gutman (b0040) 2002; 44 Bornapour, Celikin, Cerruti, Pekguleryuz (b0055) 2014; 35 Xue, Balachandran, Hogden, Theiler, Xue, Lookman (b0265) 2016; 7 Song, Han, Shan, Yim, You (b0030) 2012; 65 Witte, Hort, Vogt, Cohen, Kainer, Willumeit, Feyerabend (b0230) 2008; 12 Eddy Jai Poinern, Brundavanam, Fawcett (b0090) 2013; 2 Bhadeshia (b0105) 2009; 1 Cheng, Chen, Yan, Su, Yu, Xia, Gong (b0245) 2017; 691 Suh, Suh, Bae, Kim (b0115) 2023; 225 Mangalathu, Hwang, Jeon (b0210) 2020; 219 Mi, Tian, Tang, Kang, Peng, She, Wang, Chen, Pan (b0095) 2022; 201 Aas, Jullum, Løland (b0155) 2021; 298 Wang, Xie, Tang, Wang, Ying, Zhu, Zeng (b0145) 2024; 12 Friedman (b0220) 2001; 29 Lookman, Balachandran, Xue, Yuan (b0275) 2019; 5 Breiman (b0195) 2001; 45 Bakhsheshi-Rad, Idris, Abdul-Kadir, Ourdjini, Medraj, Daroonparvar, Hamzah (b0240) 2014; 53 Cha, Han, Yang, Kim, Hong, Lee, Jung, Ahn, Kim, Cho, Byun, Lee, Yang, Seok (b0075) 2013; 3 Yang, He, Dianyu, Yang, Qi, Xie, Shen, Peng, Shuai (b0020) 2020; 185 Zhang, Yang (b0070) 2008; 497 Yuan, Ding, Wen (b0270) 2019; 4 Kang, Lee, Jang, Seong, Kim, Koh, Song, Jung (b0005) 2019; 84 Smola, Schölkopf (b0200) 2004; 14 Sanchez, Luthringer, Feyerabend, Willumeit (b0235) 2015; 13 Liu, Wang, Jin, Zeng, Dong, Wang, Wang, Dong (b0165) 2025; 221 Thekkepat, Han, Choi, Lee, Yoon, Li, Seok, Kim, Kim, Cha (b0060) 2022; 10 Ghorbani, Boley, Nakashima, Birbilis (b0120) 2023; 11 Guan, Chen, Xin, Xu, Feng, Huang, Liu (b0135) 2024; 12 Suh, Suh, Lee, Bae, Moon (b0185) 2022; 107 S.M. Lundberg, S.-I. Lee, A Unified Approach to Interpreting Model Predictions, (n.d.). Liu, Wang, Zhang, Zhu, Wang, Zhang, Zeng (b0150) 2021; 52 Pan, Pang, Cui, Ge, Man, Wang, Cui (b0015) 2019; 157 Mesbah, Fattahi, Bushroa, Faraji, Wong, Basirun, Fallahpour, Nasiri-Tabrizi (b0110) 2021; 27 Cai, Lei, Li, Feng (b0025) 2012; 32 Persaud-Sharma, McGoron (b0080) 2012; 12 Feng, Wang, Jiang, Zhao, Zhang (b0100) 2023; 167 Wang, Yu, Chen, Huang (b0130) 2021; 196 Li, Mesbah, Fallahpour, Nasiri-Tabrizi, Liu (b0140) 2021; 305 Kirkland, Staiger, Nisbet, Davies, Birbilis (b0065) 2011; 63 Zhang, Zhang, Zhao, Li, Song, Xie, Tao, Zhang, He, Jiang, Bian (b0035) 2010; 6 Bakhsheshi-Rad, Abdul-Kadir, Idris, Farahany (b0010) 2012; 64 Wang, Fu, Jiang, Xue, Xie (b0170) 2019; 5 Gou, Shi, Zhu, Gu, Dai, Gao, Wang (b0175) 2024; 2 Le, Winkler (b0215) 2016; 116 Bahmani, Arthanari, Shin (b0250) 2020; 8 Cho, Lee, Park, Cho, Park (b0045) 2017; 695 He, Wang, Akebono, Sugeta (b0125) 2021; 90 R. Marcus, S. Majumder, The Nature of Osteoporosis, in: Osteoporosis, Elsevier, 2001: pp. 3–17. https://doi.org/10.1016/B978-012470862-4/50036-2. Suh, Ha, Suh, Kang (b0180) 2023; 29 Yang, Liu, Ma, Zhang, Zhou, Thompson (b0085) 2018; 139 Breiman (10.1016/j.matdes.2025.114494_b0195) 2001; 45 He (10.1016/j.matdes.2025.114494_b0125) 2021; 90 Cha (10.1016/j.matdes.2025.114494_b0075) 2013; 3 Bhadeshia (10.1016/j.matdes.2025.114494_b0105) 2009; 1 Cho (10.1016/j.matdes.2025.114494_b0045) 2017; 695 Sanchez (10.1016/j.matdes.2025.114494_b0235) 2015; 13 Xue (10.1016/j.matdes.2025.114494_b0265) 2016; 7 Bonora (10.1016/j.matdes.2025.114494_b0040) 2002; 44 Yuan (10.1016/j.matdes.2025.114494_b0270) 2019; 4 Suh (10.1016/j.matdes.2025.114494_b0185) 2022; 107 Song (10.1016/j.matdes.2025.114494_b0030) 2012; 65 Bakhsheshi-Rad (10.1016/j.matdes.2025.114494_b0240) 2014; 53 Feng (10.1016/j.matdes.2025.114494_b0100) 2023; 167 Liu (10.1016/j.matdes.2025.114494_b0165) 2025; 221 Thekkepat (10.1016/j.matdes.2025.114494_b0060) 2022; 10 Kang (10.1016/j.matdes.2025.114494_b0005) 2019; 84 Gou (10.1016/j.matdes.2025.114494_b0175) 2024; 2 Kirkland (10.1016/j.matdes.2025.114494_b0065) 2011; 63 Mangalathu (10.1016/j.matdes.2025.114494_b0210) 2020; 219 Song (10.1016/j.matdes.2025.114494_b0050) 2007; 49 Wang (10.1016/j.matdes.2025.114494_b0130) 2021; 196 Smola (10.1016/j.matdes.2025.114494_b0200) 2004; 14 10.1016/j.matdes.2025.114494_b0225 Mi (10.1016/j.matdes.2025.114494_b0095) 2022; 201 Bahmani (10.1016/j.matdes.2025.114494_b0250) 2020; 8 Persaud-Sharma (10.1016/j.matdes.2025.114494_b0080) 2012; 12 Suh (10.1016/j.matdes.2025.114494_b0115) 2023; 225 Zhang (10.1016/j.matdes.2025.114494_b0035) 2010; 6 Eddy Jai Poinern (10.1016/j.matdes.2025.114494_b0090) 2013; 2 Li (10.1016/j.matdes.2025.114494_b0140) 2021; 305 Suh (10.1016/j.matdes.2025.114494_b0180) 2023; 29 Zhang (10.1016/j.matdes.2025.114494_b0070) 2008; 497 Yang (10.1016/j.matdes.2025.114494_b0085) 2018; 139 Guan (10.1016/j.matdes.2025.114494_b0135) 2024; 12 Bakhsheshi-Rad (10.1016/j.matdes.2025.114494_b0010) 2012; 64 Liu (10.1016/j.matdes.2025.114494_b0150) 2021; 52 Suh (10.1016/j.matdes.2025.114494_b0190) 2023; 968 Witte (10.1016/j.matdes.2025.114494_b0230) 2008; 12 Cheng (10.1016/j.matdes.2025.114494_b0245) 2017; 691 Wang (10.1016/j.matdes.2025.114494_b0170) 2019; 5 Haynes (10.1016/j.matdes.2025.114494_b0260) 2012 Aas (10.1016/j.matdes.2025.114494_b0155) 2021; 298 10.1016/j.matdes.2025.114494_b0255 Ghorbani (10.1016/j.matdes.2025.114494_b0120) 2023; 11 Mesbah (10.1016/j.matdes.2025.114494_b0110) 2021; 27 Cai (10.1016/j.matdes.2025.114494_b0025) 2012; 32 Friedman (10.1016/j.matdes.2025.114494_b0220) 2001; 29 10.1016/j.matdes.2025.114494_b0205 Pan (10.1016/j.matdes.2025.114494_b0015) 2019; 157 Wang (10.1016/j.matdes.2025.114494_b0145) 2024; 12 Lookman (10.1016/j.matdes.2025.114494_b0275) 2019; 5 Yang (10.1016/j.matdes.2025.114494_b0020) 2020; 185 Bornapour (10.1016/j.matdes.2025.114494_b0055) 2014; 35 Le (10.1016/j.matdes.2025.114494_b0215) 2016; 116 Lee (10.1016/j.matdes.2025.114494_b0160) 2023; 22 |
| References_xml | – volume: 12 start-page: 1406 year: 2024 end-page: 1418 ident: b0145 article-title: High-throughput calculations combining machine learning to investigate the corrosion properties of binary Mg alloys publication-title: J. Magn. Alloys – volume: 1 start-page: 296 year: 2009 end-page: 305 ident: b0105 article-title: Neural Networks and Information in Materials Science publication-title: Stat. Anal. Data Min. ASA Data Sci. J. – volume: 2 start-page: 218 year: 2013 end-page: 240 ident: b0090 article-title: Biomedical Magnesium Alloys: a Review of Material Properties, Surface modifications and potential as a Biodegradable Orthopaedic Implant publication-title: Am. J. Biomed. Eng. – volume: 201 year: 2022 ident: b0095 article-title: A reverse design model for high-performance and low-cost magnesium alloys by machine learning publication-title: Comput. Mater. Sci – volume: 22 start-page: 413 year: 2023 end-page: 423 ident: b0160 article-title: Explainable machine learning for understanding and predicting geometry and defect types in Fe-Ni alloys fabricated by laser metal deposition additive manufacturing publication-title: J. Mater. Res. Technol. – volume: 139 start-page: 421 year: 2018 end-page: 429 ident: b0085 article-title: Effect of iron content on the corrosion of pure magnesium: critical factor for iron tolerance limit publication-title: Corros. Sci. – volume: 107 start-page: 52 year: 2022 end-page: 63 ident: b0185 article-title: Quantitative analysis of mechanical properties associated with aging treatment and microstructure in Mg-Al-Zn alloys through machine learning publication-title: J. Mater. Sci. Technol. – volume: 29 year: 2001 ident: b0220 article-title: Greedy function approximation: a gradient boosting machine publication-title: Ann. Stat. – volume: 7 start-page: 11241 year: 2016 ident: b0265 article-title: Accelerated search for materials with targeted properties by adaptive design publication-title: Nat. Commun. – volume: 90 start-page: 9 year: 2021 end-page: 19 ident: b0125 article-title: Machine learning-based predictions of fatigue life and fatigue limit for steels publication-title: J. Mater. Sci. Technol. – volume: 157 start-page: 420 year: 2019 end-page: 437 ident: b0015 article-title: Effect of alloyed Sr on the microstructure and corrosion behavior of biodegradable Mg-Zn-Mn alloy in Hanks’ solution publication-title: Corros. Sci. – volume: 6 start-page: 626 year: 2010 end-page: 640 ident: b0035 article-title: Research on an Mg–Zn alloy as a degradable biomaterial publication-title: Acta Biomater. – volume: 497 start-page: 111 year: 2008 end-page: 118 ident: b0070 article-title: Microstructure, mechanical properties and bio-corrosion properties of Mg–Zn–Mn–Ca alloy for biomedical application publication-title: Mater. Sci. Eng. A – reference: R. Marcus, S. Majumder, The Nature of Osteoporosis, in: Osteoporosis, Elsevier, 2001: pp. 3–17. https://doi.org/10.1016/B978-012470862-4/50036-2. – volume: 12 start-page: 4436 year: 2024 end-page: 4442 ident: b0135 article-title: Predicting the Hall-Petch slope of magnesium alloys by machine learning publication-title: J. Magn. Alloys – volume: 11 start-page: 4197 year: 2023 end-page: 4205 ident: b0120 article-title: A machine learning approach for accelerated design of magnesium alloys. Part B: Regression and property prediction publication-title: J. Magn. Alloys – volume: 84 start-page: 453 year: 2019 end-page: 467 ident: b0005 article-title: Biomimetic porous Mg with tunable mechanical properties and biodegradation rates for bone regeneration publication-title: Acta Biomater. – volume: 32 start-page: 2570 year: 2012 end-page: 2577 ident: b0025 article-title: Effects of Zn on microstructure, mechanical properties and corrosion behavior of Mg–Zn alloys publication-title: Mater. Sci. Eng. C – volume: 185 year: 2020 ident: b0020 article-title: Mg bone implant: Features, developments and perspectives publication-title: Mater. Des. – volume: 225 year: 2023 ident: b0115 article-title: Machine learning-based design of biodegradable Mg alloys for load-bearing implants publication-title: Mater. Des. – volume: 691 start-page: 95 year: 2017 end-page: 102 ident: b0245 article-title: Effects of minor Sr addition on microstructure, mechanical and bio-corrosion properties of the Mg-5Zn based alloy system publication-title: J. Alloy. Compd. – volume: 53 start-page: 283 year: 2014 end-page: 292 ident: b0240 article-title: Mechanical and bio-corrosion properties of quaternary Mg–Ca–Mn–Zn alloys compared with binary Mg–Ca alloys publication-title: Mater. Des. – volume: 29 start-page: 1841 year: 2023 end-page: 1852 ident: b0180 article-title: Determination of Optimum Zn Content for Mg–xZn–0.5Mn–0.5Sr Alloy in terms of Mechanical Properties and In Vitro Corrosion Resistance publication-title: Met. Mater. Int. – volume: 64 start-page: 184 year: 2012 end-page: 197 ident: b0010 article-title: Relationship between the corrosion behavior and the thermal characteristics and microstructure of Mg–0.5Ca–xZn alloys publication-title: Corros. Sci. – volume: 13 start-page: 16 year: 2015 end-page: 31 ident: b0235 article-title: Mg and Mg alloys: how comparable are in vitro and in vivo corrosion rates? a review publication-title: Acta Biomater. – volume: 305 year: 2021 ident: b0140 article-title: Mechanical strength estimation of ultrafine-grained magnesium implant by neural-based predictive machine learning publication-title: Mater. Lett. – volume: 14 start-page: 199 year: 2004 end-page: 222 ident: b0200 article-title: A tutorial on support vector regression publication-title: Stat. Comput. – volume: 27 start-page: 277 year: 2021 end-page: 297 ident: b0110 article-title: Experimental and Modelling Study of Ultra-Fine Grained ZK60 Magnesium Alloy with simultaneously improved Strength and Ductility Processed by Parallel Tubular Channel Angular Pressing publication-title: Met. Mater. Int. – volume: 2 start-page: e54 year: 2024 ident: b0175 article-title: Multi‐objective optimization of three mechanical properties of Mg alloys through machine learning publication-title: Mater. Genome Eng. Adv. – volume: 298 year: 2021 ident: b0155 article-title: Explaining individual predictions when features are dependent: more accurate approximations to Shapley values publication-title: Artif. Intell. – volume: 5 start-page: 87 year: 2019 ident: b0170 article-title: A property-oriented design strategy for high performance copper alloys via machine learning publication-title: npj Comput. Mater. – volume: 116 start-page: 6107 year: 2016 end-page: 6132 ident: b0215 article-title: Discovery and Optimization of Materials using Evolutionary Approaches publication-title: Chem. Rev. – volume: 12 start-page: 25 year: 2012 end-page: 39 ident: b0080 article-title: Biodegradable Magnesium Alloys: a Review of Material Development and applications publication-title: J. Biomim. Biomater. Tissue Eng. – volume: 52 start-page: 943 year: 2021 end-page: 954 ident: b0150 article-title: Accelerated Development of High-Strength Magnesium Alloys by Machine Learning publication-title: Metall. Mater. Trans. A – volume: 968 year: 2023 ident: b0190 article-title: Interpretable machine learning-based analysis of mechanical properties of extruded Mg-Al-Zn-Mn-Ca-Y alloys publication-title: J. Alloy. Compd. – volume: 3 start-page: 2367 year: 2013 ident: b0075 article-title: Biodegradability engineering of biodegradable Mg alloys: Tailoring the electrochemical properties and microstructure of constituent phases publication-title: Sci. Rep. – volume: 5 start-page: 21 year: 2019 ident: b0275 article-title: Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design publication-title: npj Comput. Mater. – volume: 4 start-page: 56 year: 2019 end-page: 70 ident: b0270 article-title: Additive manufacturing technology for porous metal implant applications and triple minimal surface structures: a review publication-title: Bioact. Mater. – volume: 35 start-page: 267 year: 2014 end-page: 282 ident: b0055 article-title: Magnesium implant alloy with low levels of strontium and calcium: the third element effect and phase selection improve bio-corrosion resistance and mechanical performance publication-title: Mater. Sci. Eng. C – volume: 63 start-page: 28 year: 2011 end-page: 34 ident: b0065 article-title: Performance-driven design of Biocompatible Mg alloys publication-title: JOM – volume: 49 start-page: 1696 year: 2007 end-page: 1701 ident: b0050 article-title: Control of biodegradation of biocompatable magnesium alloys publication-title: Corros. Sci. – volume: 167 start-page: 1 year: 2023 end-page: 13 ident: b0100 article-title: Simultaneous enhancement in mechanical and corrosion properties of Al-Mg-Si alloys using machine learning publication-title: J. Mater. Sci. Technol. – volume: 65 start-page: 322 year: 2012 end-page: 330 ident: b0030 article-title: The effect of Zn concentration on the corrosion behavior of Mg–xZn alloys publication-title: Corros. Sci. – volume: 10 start-page: 1972 year: 2022 end-page: 1980 ident: b0060 article-title: Computational design of Mg alloys with minimal galvanic corrosion publication-title: J. Magn. Alloys – volume: 221 start-page: 194 year: 2025 end-page: 203 ident: b0165 article-title: Towards high stiffness and ductility—The Mg-Al-Y alloy design through machine learning publication-title: J. Mater. Sci. Technol. – volume: 44 start-page: 729 year: 2002 end-page: 749 ident: b0040 article-title: Corrosion behaviour of stressed magnesium alloys publication-title: Corros. Sci. – volume: 196 year: 2021 ident: b0130 article-title: Machine learning assisted screening of non-rare-earth elements for Mg alloys with low stacking fault energy publication-title: Comput. Mater. Sci – volume: 8 start-page: 134 year: 2020 end-page: 149 ident: b0250 article-title: Formulation of corrosion rate of magnesium alloys using microstructural parameters publication-title: J. Magn. Alloys – volume: 45 start-page: 5 year: 2001 end-page: 32 ident: b0195 article-title: Random forests publication-title: Mach. Learn. – volume: 695 start-page: 1166 year: 2017 end-page: 1174 ident: b0045 article-title: Effect of Mn addition on corrosion properties of biodegradable Mg-4Zn-0.5Ca-xMn alloys publication-title: J. Alloy. Compd. – reference: S.M. Lundberg, S.-I. Lee, A Unified Approach to Interpreting Model Predictions, (n.d.). – volume: 12 start-page: 63 year: 2008 end-page: 72 ident: b0230 article-title: Degradable biomaterials based on magnesium corrosion publication-title: Curr. Opin. Solid State Mater. Sci. – reference: W.M. Haynes, D.R. Lide, eds., CRC handbook of chemistry and physics: a ready-reference book of chemical and physical data, 92nd ed., 2011–2012, CRC Press, Boca Raton, Fla., 2011. – volume: 219 year: 2020 ident: b0210 article-title: Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach publication-title: Eng. Struct. – year: 2012 ident: b0260 article-title: CRC handbook of chemistry and physics: a ready reference book of chemical and physical data – volume: 185 year: 2020 ident: 10.1016/j.matdes.2025.114494_b0020 article-title: Mg bone implant: Features, developments and perspectives publication-title: Mater. Des. doi: 10.1016/j.matdes.2019.108259 – volume: 11 start-page: 4197 year: 2023 ident: 10.1016/j.matdes.2025.114494_b0120 article-title: A machine learning approach for accelerated design of magnesium alloys. Part B: Regression and property prediction publication-title: J. Magn. Alloys doi: 10.1016/j.jma.2023.09.010 – volume: 45 start-page: 5 year: 2001 ident: 10.1016/j.matdes.2025.114494_b0195 article-title: Random forests publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – volume: 29 year: 2001 ident: 10.1016/j.matdes.2025.114494_b0220 article-title: Greedy function approximation: a gradient boosting machine publication-title: Ann. Stat. doi: 10.1214/aos/1013203451 – volume: 691 start-page: 95 year: 2017 ident: 10.1016/j.matdes.2025.114494_b0245 article-title: Effects of minor Sr addition on microstructure, mechanical and bio-corrosion properties of the Mg-5Zn based alloy system publication-title: J. Alloy. Compd. doi: 10.1016/j.jallcom.2016.08.164 – volume: 116 start-page: 6107 year: 2016 ident: 10.1016/j.matdes.2025.114494_b0215 article-title: Discovery and Optimization of Materials using Evolutionary Approaches publication-title: Chem. Rev. doi: 10.1021/acs.chemrev.5b00691 – volume: 12 start-page: 1406 year: 2024 ident: 10.1016/j.matdes.2025.114494_b0145 article-title: High-throughput calculations combining machine learning to investigate the corrosion properties of binary Mg alloys publication-title: J. Magn. Alloys doi: 10.1016/j.jma.2021.12.007 – volume: 221 start-page: 194 year: 2025 ident: 10.1016/j.matdes.2025.114494_b0165 article-title: Towards high stiffness and ductility—The Mg-Al-Y alloy design through machine learning publication-title: J. Mater. Sci. Technol. doi: 10.1016/j.jmst.2024.09.038 – volume: 63 start-page: 28 year: 2011 ident: 10.1016/j.matdes.2025.114494_b0065 article-title: Performance-driven design of Biocompatible Mg alloys publication-title: JOM doi: 10.1007/s11837-011-0089-z – volume: 298 year: 2021 ident: 10.1016/j.matdes.2025.114494_b0155 article-title: Explaining individual predictions when features are dependent: more accurate approximations to Shapley values publication-title: Artif. Intell. doi: 10.1016/j.artint.2021.103502 – volume: 8 start-page: 134 year: 2020 ident: 10.1016/j.matdes.2025.114494_b0250 article-title: Formulation of corrosion rate of magnesium alloys using microstructural parameters publication-title: J. Magn. Alloys doi: 10.1016/j.jma.2019.12.001 – volume: 90 start-page: 9 year: 2021 ident: 10.1016/j.matdes.2025.114494_b0125 article-title: Machine learning-based predictions of fatigue life and fatigue limit for steels publication-title: J. Mater. Sci. Technol. doi: 10.1016/j.jmst.2021.02.021 – volume: 6 start-page: 626 year: 2010 ident: 10.1016/j.matdes.2025.114494_b0035 article-title: Research on an Mg–Zn alloy as a degradable biomaterial publication-title: Acta Biomater. doi: 10.1016/j.actbio.2009.06.028 – volume: 22 start-page: 413 year: 2023 ident: 10.1016/j.matdes.2025.114494_b0160 article-title: Explainable machine learning for understanding and predicting geometry and defect types in Fe-Ni alloys fabricated by laser metal deposition additive manufacturing publication-title: J. Mater. Res. Technol. doi: 10.1016/j.jmrt.2022.11.137 – volume: 139 start-page: 421 year: 2018 ident: 10.1016/j.matdes.2025.114494_b0085 article-title: Effect of iron content on the corrosion of pure magnesium: critical factor for iron tolerance limit publication-title: Corros. Sci. doi: 10.1016/j.corsci.2018.04.024 – volume: 219 year: 2020 ident: 10.1016/j.matdes.2025.114494_b0210 article-title: Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach publication-title: Eng. Struct. doi: 10.1016/j.engstruct.2020.110927 – ident: 10.1016/j.matdes.2025.114494_b0255 – volume: 64 start-page: 184 year: 2012 ident: 10.1016/j.matdes.2025.114494_b0010 article-title: Relationship between the corrosion behavior and the thermal characteristics and microstructure of Mg–0.5Ca–xZn alloys publication-title: Corros. Sci. doi: 10.1016/j.corsci.2012.07.015 – volume: 49 start-page: 1696 year: 2007 ident: 10.1016/j.matdes.2025.114494_b0050 article-title: Control of biodegradation of biocompatable magnesium alloys publication-title: Corros. Sci. doi: 10.1016/j.corsci.2007.01.001 – volume: 107 start-page: 52 year: 2022 ident: 10.1016/j.matdes.2025.114494_b0185 article-title: Quantitative analysis of mechanical properties associated with aging treatment and microstructure in Mg-Al-Zn alloys through machine learning publication-title: J. Mater. Sci. Technol. doi: 10.1016/j.jmst.2021.07.045 – volume: 35 start-page: 267 year: 2014 ident: 10.1016/j.matdes.2025.114494_b0055 article-title: Magnesium implant alloy with low levels of strontium and calcium: the third element effect and phase selection improve bio-corrosion resistance and mechanical performance publication-title: Mater. Sci. Eng. C doi: 10.1016/j.msec.2013.11.011 – volume: 167 start-page: 1 year: 2023 ident: 10.1016/j.matdes.2025.114494_b0100 article-title: Simultaneous enhancement in mechanical and corrosion properties of Al-Mg-Si alloys using machine learning publication-title: J. Mater. Sci. Technol. doi: 10.1016/j.jmst.2023.04.072 – volume: 14 start-page: 199 year: 2004 ident: 10.1016/j.matdes.2025.114494_b0200 article-title: A tutorial on support vector regression publication-title: Stat. Comput. doi: 10.1023/B:STCO.0000035301.49549.88 – volume: 4 start-page: 56 year: 2019 ident: 10.1016/j.matdes.2025.114494_b0270 article-title: Additive manufacturing technology for porous metal implant applications and triple minimal surface structures: a review publication-title: Bioact. Mater. – volume: 29 start-page: 1841 year: 2023 ident: 10.1016/j.matdes.2025.114494_b0180 article-title: Determination of Optimum Zn Content for Mg–xZn–0.5Mn–0.5Sr Alloy in terms of Mechanical Properties and In Vitro Corrosion Resistance publication-title: Met. Mater. Int. doi: 10.1007/s12540-022-01327-0 – volume: 84 start-page: 453 year: 2019 ident: 10.1016/j.matdes.2025.114494_b0005 article-title: Biomimetic porous Mg with tunable mechanical properties and biodegradation rates for bone regeneration publication-title: Acta Biomater. doi: 10.1016/j.actbio.2018.11.045 – volume: 3 start-page: 2367 year: 2013 ident: 10.1016/j.matdes.2025.114494_b0075 article-title: Biodegradability engineering of biodegradable Mg alloys: Tailoring the electrochemical properties and microstructure of constituent phases publication-title: Sci. Rep. doi: 10.1038/srep02367 – volume: 12 start-page: 25 year: 2012 ident: 10.1016/j.matdes.2025.114494_b0080 article-title: Biodegradable Magnesium Alloys: a Review of Material Development and applications publication-title: J. Biomim. Biomater. Tissue Eng. doi: 10.4028/www.scientific.net/JBBTE.12.25 – volume: 2 start-page: e54 year: 2024 ident: 10.1016/j.matdes.2025.114494_b0175 article-title: Multi‐objective optimization of three mechanical properties of Mg alloys through machine learning publication-title: Mater. Genome Eng. Adv. doi: 10.1002/mgea.54 – volume: 52 start-page: 943 year: 2021 ident: 10.1016/j.matdes.2025.114494_b0150 article-title: Accelerated Development of High-Strength Magnesium Alloys by Machine Learning publication-title: Metall. Mater. Trans. A doi: 10.1007/s11661-020-06132-1 – volume: 2 start-page: 218 year: 2013 ident: 10.1016/j.matdes.2025.114494_b0090 article-title: Biomedical Magnesium Alloys: a Review of Material Properties, Surface modifications and potential as a Biodegradable Orthopaedic Implant publication-title: Am. J. Biomed. Eng. doi: 10.5923/j.ajbe.20120206.02 – volume: 13 start-page: 16 year: 2015 ident: 10.1016/j.matdes.2025.114494_b0235 article-title: Mg and Mg alloys: how comparable are in vitro and in vivo corrosion rates? a review publication-title: Acta Biomater. doi: 10.1016/j.actbio.2014.11.048 – volume: 7 start-page: 11241 year: 2016 ident: 10.1016/j.matdes.2025.114494_b0265 article-title: Accelerated search for materials with targeted properties by adaptive design publication-title: Nat. Commun. doi: 10.1038/ncomms11241 – ident: 10.1016/j.matdes.2025.114494_b0225 doi: 10.1016/B978-012470862-4/50036-2 – volume: 12 start-page: 63 year: 2008 ident: 10.1016/j.matdes.2025.114494_b0230 article-title: Degradable biomaterials based on magnesium corrosion publication-title: Curr. Opin. Solid State Mater. Sci. doi: 10.1016/j.cossms.2009.04.001 – volume: 44 start-page: 729 year: 2002 ident: 10.1016/j.matdes.2025.114494_b0040 article-title: Corrosion behaviour of stressed magnesium alloys publication-title: Corros. Sci. doi: 10.1016/S0010-938X(01)00101-9 – volume: 201 year: 2022 ident: 10.1016/j.matdes.2025.114494_b0095 article-title: A reverse design model for high-performance and low-cost magnesium alloys by machine learning publication-title: Comput. Mater. Sci doi: 10.1016/j.commatsci.2021.110881 – volume: 27 start-page: 277 year: 2021 ident: 10.1016/j.matdes.2025.114494_b0110 article-title: Experimental and Modelling Study of Ultra-Fine Grained ZK60 Magnesium Alloy with simultaneously improved Strength and Ductility Processed by Parallel Tubular Channel Angular Pressing publication-title: Met. Mater. Int. doi: 10.1007/s12540-019-00495-w – volume: 157 start-page: 420 year: 2019 ident: 10.1016/j.matdes.2025.114494_b0015 article-title: Effect of alloyed Sr on the microstructure and corrosion behavior of biodegradable Mg-Zn-Mn alloy in Hanks’ solution publication-title: Corros. Sci. doi: 10.1016/j.corsci.2019.06.022 – volume: 695 start-page: 1166 year: 2017 ident: 10.1016/j.matdes.2025.114494_b0045 article-title: Effect of Mn addition on corrosion properties of biodegradable Mg-4Zn-0.5Ca-xMn alloys publication-title: J. Alloy. Compd. doi: 10.1016/j.jallcom.2016.10.244 – volume: 5 start-page: 87 year: 2019 ident: 10.1016/j.matdes.2025.114494_b0170 article-title: A property-oriented design strategy for high performance copper alloys via machine learning publication-title: npj Comput. Mater. doi: 10.1038/s41524-019-0227-7 – volume: 968 year: 2023 ident: 10.1016/j.matdes.2025.114494_b0190 article-title: Interpretable machine learning-based analysis of mechanical properties of extruded Mg-Al-Zn-Mn-Ca-Y alloys publication-title: J. Alloy. Compd. doi: 10.1016/j.jallcom.2023.172007 – volume: 225 year: 2023 ident: 10.1016/j.matdes.2025.114494_b0115 article-title: Machine learning-based design of biodegradable Mg alloys for load-bearing implants publication-title: Mater. Des. doi: 10.1016/j.matdes.2022.111442 – volume: 305 year: 2021 ident: 10.1016/j.matdes.2025.114494_b0140 article-title: Mechanical strength estimation of ultrafine-grained magnesium implant by neural-based predictive machine learning publication-title: Mater. Lett. doi: 10.1016/j.matlet.2021.130627 – volume: 5 start-page: 21 year: 2019 ident: 10.1016/j.matdes.2025.114494_b0275 article-title: Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design publication-title: npj Comput. Mater. doi: 10.1038/s41524-019-0153-8 – volume: 65 start-page: 322 year: 2012 ident: 10.1016/j.matdes.2025.114494_b0030 article-title: The effect of Zn concentration on the corrosion behavior of Mg–xZn alloys publication-title: Corros. Sci. doi: 10.1016/j.corsci.2012.08.037 – volume: 1 start-page: 296 year: 2009 ident: 10.1016/j.matdes.2025.114494_b0105 article-title: Neural Networks and Information in Materials Science publication-title: Stat. Anal. Data Min. ASA Data Sci. J. doi: 10.1002/sam.10018 – year: 2012 ident: 10.1016/j.matdes.2025.114494_b0260 – volume: 32 start-page: 2570 year: 2012 ident: 10.1016/j.matdes.2025.114494_b0025 article-title: Effects of Zn on microstructure, mechanical properties and corrosion behavior of Mg–Zn alloys publication-title: Mater. Sci. Eng. C doi: 10.1016/j.msec.2012.07.042 – volume: 196 year: 2021 ident: 10.1016/j.matdes.2025.114494_b0130 article-title: Machine learning assisted screening of non-rare-earth elements for Mg alloys with low stacking fault energy publication-title: Comput. Mater. Sci doi: 10.1016/j.commatsci.2021.110544 – volume: 497 start-page: 111 year: 2008 ident: 10.1016/j.matdes.2025.114494_b0070 article-title: Microstructure, mechanical properties and bio-corrosion properties of Mg–Zn–Mn–Ca alloy for biomedical application publication-title: Mater. Sci. Eng. A doi: 10.1016/j.msea.2008.06.019 – volume: 53 start-page: 283 year: 2014 ident: 10.1016/j.matdes.2025.114494_b0240 article-title: Mechanical and bio-corrosion properties of quaternary Mg–Ca–Mn–Zn alloys compared with binary Mg–Ca alloys publication-title: Mater. Des. doi: 10.1016/j.matdes.2013.06.055 – volume: 10 start-page: 1972 year: 2022 ident: 10.1016/j.matdes.2025.114494_b0060 article-title: Computational design of Mg alloys with minimal galvanic corrosion publication-title: J. Magn. Alloys doi: 10.1016/j.jma.2021.06.019 – volume: 12 start-page: 4436 year: 2024 ident: 10.1016/j.matdes.2025.114494_b0135 article-title: Predicting the Hall-Petch slope of magnesium alloys by machine learning publication-title: J. Magn. Alloys doi: 10.1016/j.jma.2023.07.005 – ident: 10.1016/j.matdes.2025.114494_b0205 |
| SSID | ssj0022734 |
| Score | 2.455866 |
| Snippet | [Display omitted]
•A reverse design approach was employed for biodegradable Mg-Zn-Mn-Sr-Ca alloy.•Interpretable machine learning and multi-objective genetic... Biodegradable Mg alloys for implants must meet both mechanical strength and biologically compatible degradation rate to promote bone healing. The present study... |
| SourceID | doaj unpaywall crossref elsevier |
| SourceType | Open Website Open Access Repository Index Database Publisher |
| StartPage | 114494 |
| SubjectTerms | Biodegradable Degradation Magnesium alloy Reverse design, Machine learning Strength |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV07T8MwELYQCzAgnqK85IHV4CTOawREVSGVgYeEWCI_S1CbVqUIlV_PXZxUZYKBDBmS6GLdnXKfne8-E3IGoEFpGaUstSJmQlrLVJbDxzCONUx-4jAzuN7Rv0t6T-L2OX5e2uoLOWFeHtg77iIVGkqmDHjmtFBGSaE10jWSwDgX1i3rIc_ydjLVTLVQtMWvrqAqXxq3TXM1swugoLEo1R3GKJUrcvGjKNXa_T9q09pHNZHzTzkcLtWe7hbZbEAjvfSD3SYrttohG0tSgrvk694iwcJSU1My6NjR_oC9VKxfsYcpu5YU_7DP3ymAVKrKsUGRCIN9U7QcTYbIhqFqTssFBxHvjGqmpaXN1hIDKitDIeOw8RHsDcbTcvY62iNP3ZvH6x5r9lVgOkryGQBqoxWULK4zE2htjUmj2GUAPXiOCnaGK-24MmEuwbVwJMrJQNoglEZI-CDtk9VqXNkDQiMZJI6byCquRBQlcALLWR7onHNnZYew1rHFxMtnFC2v7K3wgSgwEIUPRIdcofcXz6L4dX0BUqJoUqL4LSU6JG1jVzQ4wuMDMFX-8vrzRaj_NN7D_xjvEVlHk565dkxWZ9MPewJQZ6ZO66z-Bv8--6I priority: 102 providerName: Directory of Open Access Journals – databaseName: Unpaywall dbid: UNPAY link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lj9MwEB4t3QPiwBtRBMgHjrhyEidxjsuK1QqpKwRUWrhEfpYsbVqVVKj765nJo9oiIZZLDnlMHHvs-WJ_8xngDYIGY3WS89zLlEvtPTeqwMEwTS3-_KSxcjTfMb3Izmfyw2V6eQRvh1yYg_X7loeFwM15EtaOUxK2lYW8A8dZish7BMezi48nX7tpFJLfa3V1I5UntLiZD5lyfzFzEIlawf6DgHR3W6_17pdeLG4EnLMHMB2K2vFMfky2jZnY6z9UHG_7LQ_hfo882UnnKo_gyNeP4d4NPcIncP3JE0vDM9fyOtgqsOmcf6v5tOafN_xUM1qm3_1kiHSZqVaOlCYcJV-xarleEKWGmR2r9kRGurJs6Zqe9ftTzJmuHUO3pexJtDdfbarm-_IpzM7efzk95_3mDNwmWdEgKnfWYNwTVrnIWu9cnqRBIX4RBcngOWFsEMbFhY5xJFMqM0FH2kexdlLjqPYMRvWq9s-BJTrKgnCJN8LIJMnwgJZVEdlCiOD1GPjQUOW60-AoB3LaVdnVaUl1WnZ1OoZ31Jr7e0lBuz2BjVH2HbLMpUUopiOhgpXGGS2tJRpQFrkQENaMIR98oezBSAcy0FT1j9dP9q5zq_K--N8HXsKo2Wz9K4RDjXnd94LfpjsG6g priority: 102 providerName: Unpaywall |
| Title | Reverse design of Mg-Zn-Mn-Sr-Ca alloys for biodegradable implants by interpretable machine learning and genetic algorithm |
| URI | https://dx.doi.org/10.1016/j.matdes.2025.114494 https://doi.org/10.1016/j.matdes.2025.114494 https://doaj.org/article/74c397a108fc4bdba4cc657061dff241 |
| UnpaywallVersion | publishedVersion |
| Volume | 257 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals issn: 1873-4197 databaseCode: DOA dateStart: 20190101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.doaj.org/ omitProxy: true ssIdentifier: ssj0022734 providerName: Directory of Open Access Journals – providerCode: PRVESC databaseName: Baden-Württemberg Complete Freedom Collection (Elsevier) issn: 1873-4197 databaseCode: GBLVA dateStart: 20110101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: true ssIdentifier: ssj0022734 providerName: Elsevier – providerCode: PRVESC databaseName: Elsevier ScienceDirect Freedom Collection issn: 1873-4197 databaseCode: AIKHN dateStart: 20181205 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: true ssIdentifier: ssj0022734 providerName: Elsevier – providerCode: PRVESC databaseName: Elsevier SD Complete Freedom Collection [SCCMFC] issn: 1873-4197 databaseCode: ACRLP dateStart: 20181205 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: true ssIdentifier: ssj0022734 providerName: Elsevier – providerCode: PRVLSH databaseName: Elsevier Journals issn: 1873-4197 databaseCode: AKRWK dateStart: 20151005 customDbUrl: isFulltext: true mediaType: online dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0022734 providerName: Library Specific Holdings |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3Na9swFBclPaw9jO6jNFtbdNhVjWzLtnzMQku2kTDWBrpdjD4zj8QOWcrI_vq954_QnFp2MUg2ktAT7_0k_95PhHwA0KCNilKWOhEzoZxjWmbgDOPYwOYnDqXF847JNBnPxOf7-P6AjLpcGKRVtr6_8em1t25rBu1sDlZFMbiF3YNAeXII4hD0MHn9EOKPlD1yOPz0ZTzd7btQwaU5akGJvjTuMuhqmhfgQutQtzuMUTdXZGIvQtVC_nuB6sVDuVLbP2qxeBSIbk7IyxZB0mEzyFfkwJWvyfEjXcE35O83h2wLR23Nz6CVp5M5-1GySclu12ykKP5u3_6mgFipLiqLihEWk6hosVwtkBpD9ZYWO0IivlnWtEtH23sm5lSVlsLywyxIaG9erYvNz-VbMru5vhuNWXvJAjNRkm0AXVujIX5xI21gYNZtGsVeAg7hGcrZWa6N59qGmQrBI0mZaK8C5YJQWaHAO52SXlmV7ozQSAWJ5zZymmsRRQk8oGWZBSbj3DvVJ6yb2HzVaGnkHcnsV94YIkdD5I0h-uQjzv7uW1TCriuq9Txvl0KeCgOQSgVceiO01UoYg3SeJLDeAzzpk7SzXb63sKCp4onur3amftZ43_13V-_JEZYa7to56W3WD-4CwM5GX7aL-bI-LIDSbPp1-P0fop8ARQ |
| linkProvider | Elsevier |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV07b9swECaCZEg7FOkLdZO0HLqypiRSjzE1EjhN7KFJgKALwaerwpYMx0Hh_vre6WHEU4suGkiBJHjU3Ufqu4-EfALQYKxOMpZ5IZnQ3jOTF-AMpbSw-ZFx7vC8YzJNx3fi67283yOjPhcGaZWd7299euOtu5JhN5vDZVkOb2D3IFCeHII4BD1MXj8QMsng6zw4u7waT7f7LlRwaY9aUKIvk30GXUPzAlzoPOp2xxJ1c0UhdiJUI-S_E6gOH6ul3vzS8_mTQHRxRF50CJKetYN8SfZ89Yo8f6Ir-Jr8_uaRbeGpa_gZtA50MmPfKzap2M2KjTTF3-2bBwqIlZqydqgY4TCJipaL5RypMdRsaLklJGLNoqFdetrdMzGjunIUlh9mQUJ7s3pVrn8s3pC7i_Pb0Zh1lywwm6TFGtC1swbiF7e5iyzMussSGXLAIbxAOTvHjQ3cuLjQMXikPE9N0JH2Uayd0OCd3pL9qq78O0ITHaWBu8QbbkSSpPCAlvMisgXnwesBYf3EqmWrpaF6ktlP1RpCoSFUa4gB-YKzv30XlbCbgno1U91SUJmwAKl0xPNghXFGC2uRzpNGLgSAJwOS9bZTOwsLmir_0v3nran_abzv_7urj-RwfDu5VteX06tj8gxrWh7bCdlfrx79KQCftfnQLew_nVkAgQ |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lj9MwEB4t3QPiwBtRBMgHjrhyEidxjsuK1QqpKwRUWrhEfpYsbVqVVKj765nJo9oiIZZLDnlMHHvs-WJ_8xngDYIGY3WS89zLlEvtPTeqwMEwTS3-_KSxcjTfMb3Izmfyw2V6eQRvh1yYg_X7loeFwM15EtaOUxK2lYW8A8dZish7BMezi48nX7tpFJLfa3V1I5UntLiZD5lyfzFzEIlawf6DgHR3W6_17pdeLG4EnLMHMB2K2vFMfky2jZnY6z9UHG_7LQ_hfo882UnnKo_gyNeP4d4NPcIncP3JE0vDM9fyOtgqsOmcf6v5tOafN_xUM1qm3_1kiHSZqVaOlCYcJV-xarleEKWGmR2r9kRGurJs6Zqe9ftTzJmuHUO3pexJtDdfbarm-_IpzM7efzk95_3mDNwmWdEgKnfWYNwTVrnIWu9cnqRBIX4RBcngOWFsEMbFhY5xJFMqM0FH2kexdlLjqPYMRvWq9s-BJTrKgnCJN8LIJMnwgJZVEdlCiOD1GPjQUOW60-AoB3LaVdnVaUl1WnZ1OoZ31Jr7e0lBuz2BjVH2HbLMpUUopiOhgpXGGS2tJRpQFrkQENaMIR98oezBSAcy0FT1j9dP9q5zq_K--N8HXsKo2Wz9K4RDjXnd94LfpjsG6g |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Reverse+design+of+Mg-Zn-Mn-Sr-Ca+alloys+for+biodegradable+implants+by+interpretable+machine+learning+and+genetic+algorithm&rft.jtitle=Materials+%26+design&rft.au=Suh%2C+Joung+Sik&rft.au=Jang%2C+Jae+Hoon&rft.au=Suh%2C+Byeong-Chan&rft.au=Kim%2C+Jae-Yeon&rft.date=2025-09-01&rft.issn=0264-1275&rft.volume=257&rft.spage=114494&rft_id=info:doi/10.1016%2Fj.matdes.2025.114494&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_matdes_2025_114494 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0264-1275&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0264-1275&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0264-1275&client=summon |