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Computational Thermodynamics-Assisted Machine Learning Approach to Predict Mechanical Properties of Magnesium Alloys for Bio-Implant Applications

https://doi.org/10.2139/ssrn.4804871
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inferredcites →https://doi.org/10.1002/sam.10022openalex-extracted
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inferredcites →https://doi.org/10.1007/s10853-022-07363-wopenalex-extracted
Machine learning-assisted design of biomedical high entropy alloys with low elastic modulus for orthopedic implants
inferredcites →https://doi.org/10.1007/s11661-002-0109-2openalex-extracted
Recent metallic materials for biomedical applications
inferredcites →https://doi.org/10.1007/s11837-020-04343-wopenalex-extracted
Predicting Tensile Properties of AZ31 Magnesium Alloys by Machine Learning
inferredcites →https://doi.org/10.1016/c2009-0-02433-7openalex-extracted
Biomaterials Science: An Introduction to Materials in Medicine
inferredcites →https://doi.org/10.1016/j.actamat.2019.11.067openalex-extracted
Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models
inferredcites →https://doi.org/10.1016/j.actamat.2021.117118openalex-extracted
Machine Learning Assisted Composition Effective Design for Precipitation Strengthened Copper Alloys
inferredcites →https://doi.org/10.1016/j.actamat.2021.117431openalex-extracted
A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness
inferredcites →https://doi.org/10.1016/j.actbio.2010.02.028openalex-extracted
The history of biodegradable magnesium implants: A review☆
inferredcites →https://doi.org/10.1016/j.actbio.2014.07.005openalex-extracted
Recent advances on the development of magnesium alloys for biodegradable implants
inferredcites →https://doi.org/10.1016/j.biomaterials.2005.10.003openalex-extracted
Magnesium and its alloys as orthopedic biomaterials: A review
inferredcites →https://doi.org/10.1016/j.biomaterials.2007.12.021openalex-extracted
The development of binary Mg–Ca alloys for use as biodegradable materials within bone
inferredcites →https://doi.org/10.1016/j.commatsci.2021.110544openalex-extracted
Machine learning assisted screening of non-rare-earth elements for Mg alloys with low stacking fault energy
inferredcites →https://doi.org/10.1016/j.commatsci.2023.112111openalex-extracted
Predicting thermodynamic stability of magnesium alloys in machine learning
inferredcites →https://doi.org/10.1016/j.corsci.2010.09.013openalex-extracted
A combined neural network and mechanistic approach for the prediction of corrosion rate and yield strength of magnesium-rare earth alloys
inferredcites →https://doi.org/10.1016/j.jma.2022.04.010openalex-extracted
Effects of heat treatment on the corrosion behavior and mechanical properties of biodegradable Mg alloys
inferredcites →https://doi.org/10.1016/j.jmbbm.2018.07.022openalex-extracted
Mechanical properties of magnesium alloys for medical application: A review
inferredcites →https://doi.org/10.1016/j.jmst.2013.02.005openalex-extracted
Novel Magnesium Alloys Developed for Biomedical Application: A Review
inferredcites →https://doi.org/10.1016/j.jmst.2013.03.002openalex-extracted
Biodegradable Materials for Bone Repairs: A Review
inferredcites →https://doi.org/10.1016/j.jmst.2019.01.007openalex-extracted
Microstructure, mechanical properties and deformation mechanisms of an as-cast Mg–Zn–Y–Nd–Zr alloy for stent applications
inferredcites →https://doi.org/10.1016/j.matdes.2013.06.055openalex-extracted
Mechanical and bio-corrosion properties of quaternary Mg–Ca–Mn–Zn alloys compared with binary Mg–Ca alloys
inferredcites →https://doi.org/10.1016/j.msea.2007.08.064openalex-extracted
Effects of heat treatments on the microstructures and mechanical properties of Mg–3Nd–0.2Zn–0.4Zr (wt.%) alloy
inferredcites →https://doi.org/10.1016/j.mseb.2012.05.020openalex-extracted
Improvement of mechanical properties and corrosion resistance of biodegradable Mg–Nd–Zn–Zr alloys by double extrusion
inferredcites →https://doi.org/10.1016/s0364-5916(02)00035-4openalex-extracted
FactSage thermochemical software and databases
inferredcites →https://doi.org/10.1016/s1003-6326(11)60990-4openalex-extracted
Effects of heat treatment on microstructure and mechanical properties of Mg-3Sn-1Mn magnesium alloy
inferredcites →https://doi.org/10.1023/a:1010933404324openalex-extracted
Random Forests
inferredcites →https://doi.org/10.1023/b:stco.0000035301.49549.88openalex-extracted
A tutorial on support vector regression
inferredcites →https://doi.org/10.1038/s41524-017-0056-5openalex-extracted
Machine learning in materials informatics: recent applications and prospects
inferredcites →https://doi.org/10.1038/s41524-018-0081-zopenalex-extracted
A strategy to apply machine learning to small datasets in materials science
inferredcites →https://doi.org/10.1038/s41524-018-0094-7openalex-extracted
Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials
inferredcites →https://doi.org/10.1039/c8qm00507aopenalex-extracted
Biodegradable metallic bone implants
inferredcites →https://doi.org/10.1080/07350015.2019.1624293openalex-extracted
Causal Interpretations of Black-Box Models
inferredcites →https://doi.org/10.1080/10667857.2017.1377973openalex-extracted
Challenges and opportunities for biodegradable magnesium alloy implants
inferredcites →https://doi.org/10.1080/10667857.2018.1492227openalex-extracted
Comparative study of the effect of Nd and Y content on the mechanical and biodegradable properties of Mg-Zn-Zr-xNd/Y (x=0.5, 1, 2) alloys
inferredcites →https://doi.org/10.1080/1478422x.2020.1804094openalex-extracted
Microstructure, mechanical and corrosion properties of Mg–Zn–Sr–Ca alloys for use as potential biodegradable implant materials
inferredcites →https://doi.org/10.1088/2057-1976/ab9426openalex-extracted
Bio-inspired biomaterial Mg–Zn–Ca: a review of the main mechanical and biological properties of Mg-based alloys
inferredcites →https://doi.org/10.1109/icbda51983.2021.9402996openalex-extracted
Property Prediction of Medical Magnesium Alloy based on Machine Learning
inferredcites →https://doi.org/10.1109/tcbb.2019.2911071openalex-extracted
XGBoost Model for Chronic Kidney Disease Diagnosis
inferredcites →https://doi.org/10.1145/2939672.2939778openalex-extracted
"Why Should I Trust You?"
inferredcites →https://doi.org/10.1145/2939672.2939785openalex-extracted
XGBoost
inferredcites →https://doi.org/10.1177/0885328219834656openalex-extracted
Degradable magnesium-based alloys for biomedical applications: The role of critical alloying elements
inferredcites →https://doi.org/10.1179/026708394790163401openalex-extracted
Magnesium alloys and applications
inferredcites →https://doi.org/10.1179/1743278213y.0000000146openalex-extracted
Mechanical and corrosion properties of newly developed Mg–Mn–Ca alloys as potential biodegradable implant materials
inferredcites →https://doi.org/10.1214/aos/1013203451openalex-extracted
Greedy function approximation: A gradient boosting machine.
inferredcites →https://doi.org/10.17877/de290r-690openalex-extracted
A practical overview of quantitative structure-activity relationship
inferredcites →https://doi.org/10.3390/met5010001openalex-extracted
Mechanical Properties of Magnesium-Rare Earth Alloy Systems: A Review
inferredcites →https://doi.org/10.4028/www.scientific.net/msf.488-489.793openalex-extracted
A Model on the Correlation between Composition and Mechanical Properties of Mg-Al-Zn Alloys by Using Artificial Neural Network
inferredcites →https://doi.org/10.48550/arxiv.2305.12060openalex-extracted
Mechanical Property Design of Bio-compatible Mg alloys using Machine-Learning Algorithms
inferredcites →https://doi.org/10.1179/mst.1994.10.1.1openalex-snapshot
Magnesium alloys and applications
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