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Article: Machine learning recommends affordable new Ti alloy with bone-like modulus

TitleMachine learning recommends affordable new Ti alloy with bone-like modulus
Authors
KeywordsBeta titanium alloy
Biomedical titanium alloy
Chemical compositions
High throughput
Its efficiencies
Issue Date2020
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/mattod
Citation
Materials Today, 2020, v. 34, p. 41-50 How to Cite?
AbstractA neural-network machine called “βLow” enables a high-throughput recommendation for new β titanium alloys with Young’s moduli lower than 50 GPa. The machine was trained by using a very general approach with small data from experiments. Its efficiency and accuracy break the barrier for alloy discovery. βLow’s best recommendation, Ti-12Nb-12Zr-12Sn (in wt.%) alloy, was unexpected in previous methods. This new alloy meets the requirements for bio-compatibility, low modulus, and low cost, and holds promise for orthopedic and prosthetic implants. Moreover, βLow’s prediction guides us to realize that the unexplored space of the chemical compositions of low-modulus biomedical titanium alloys is still large. Machine-learning-aided materials design accelerates the progress of materials development and reduces research costs in this work.
Persistent Identifierhttp://hdl.handle.net/10722/289748
ISSN
2023 Impact Factor: 21.1
2023 SCImago Journal Rankings: 5.949
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWu, CT-
dc.contributor.authorCHANG, HT-
dc.contributor.authorWU, CY-
dc.contributor.authorCHEN, SW-
dc.contributor.authorHUANG, SY-
dc.contributor.authorHuang, M-
dc.contributor.authorPAN, YT-
dc.contributor.authorBRADURY, P-
dc.contributor.authorCHOU, J-
dc.contributor.authorYEN, HW-
dc.date.accessioned2020-10-22T08:16:54Z-
dc.date.available2020-10-22T08:16:54Z-
dc.date.issued2020-
dc.identifier.citationMaterials Today, 2020, v. 34, p. 41-50-
dc.identifier.issn1369-7021-
dc.identifier.urihttp://hdl.handle.net/10722/289748-
dc.description.abstractA neural-network machine called “βLow” enables a high-throughput recommendation for new β titanium alloys with Young’s moduli lower than 50 GPa. The machine was trained by using a very general approach with small data from experiments. Its efficiency and accuracy break the barrier for alloy discovery. βLow’s best recommendation, Ti-12Nb-12Zr-12Sn (in wt.%) alloy, was unexpected in previous methods. This new alloy meets the requirements for bio-compatibility, low modulus, and low cost, and holds promise for orthopedic and prosthetic implants. Moreover, βLow’s prediction guides us to realize that the unexplored space of the chemical compositions of low-modulus biomedical titanium alloys is still large. Machine-learning-aided materials design accelerates the progress of materials development and reduces research costs in this work.-
dc.languageeng-
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/mattod-
dc.relation.ispartofMaterials Today-
dc.subjectBeta titanium alloy-
dc.subjectBiomedical titanium alloy-
dc.subjectChemical compositions-
dc.subjectHigh throughput-
dc.subjectIts efficiencies-
dc.titleMachine learning recommends affordable new Ti alloy with bone-like modulus-
dc.typeArticle-
dc.identifier.emailHuang, M: mxhuang@hku.hk-
dc.identifier.authorityHuang, M=rp01418-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.mattod.2019.08.008-
dc.identifier.scopuseid_2-s2.0-85072691688-
dc.identifier.hkuros317272-
dc.identifier.volume34-
dc.identifier.spage41-
dc.identifier.epage50-
dc.identifier.isiWOS:000531095800012-
dc.publisher.placeNetherlands-
dc.identifier.issnl1369-7021-

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