Figure - available from: Journal of Materials Science
This content is subject to copyright. Terms and conditions apply.
![Phase diagrams of the constituent binary systems: a Cu–Mg [23]; b Cu–Zn [16]; c Mg–Zn [24]; d enlarged portion of Mg–Zn [24]](publication/349804414/figure/fig1/AS:997868169076736@1614921719096/Phase-diagrams-of-the-constituent-binary-systems-a-Cu-Mg-23-b-Cu-Zn-16-c-Mg-Zn.png)
Phase diagrams of the constituent binary systems: a Cu–Mg [23]; b Cu–Zn [16]; c Mg–Zn [24]; d enlarged portion of Mg–Zn [24]
Source publication
![Phase diagrams of the constituent binary systems: a Cu–Mg [23]; b Cu–Zn...](publication/349804414/figure/fig1/AS:997868169076736@1614921719096/Phase-diagrams-of-the-constituent-binary-systems-a-Cu-Mg-23-b-Cu-Zn-16-c-Mg-Zn_Q320.jpg)
The Cu–Mg–Zn system is one of the important ternary sub-systems in multicomponent light alloys. This ternary system was reassessed in the framework of the CALPHAD method in view of the drawbacks of previous thermodynamic assessments. A critical review of the literature data on phase equilibria and thermodynamic properties has been performed. An upd...
Citations
... Li et al. [51] found that Zn's solubility in Mg-Zn-Ca alloy was almost twice that of the Mg-Zn binary alloy, suggesting that Ca dissolving enhanced the Zn's solubility. In contrast, Dreval et al. [52] and Li et. al [53] reported respectively that additional Cu and Zr elements could reduce Zn's solubility. ...
To investigate the effects of adding trace amounts of Li on the microstructure, mechanical properties, corrosion behavior, antibacterial activity and biocompatibility of the Zn–Cu alloy, Zn–1.2Cu, Zn–1.2Cu–0.02Li as well as Zn–0.02Li (as a reference) alloys were prepared in this work. The results showed that the grain size of the as-cast Zn–1.2Cu–0.02Li alloy is significantly smaller than that of the Zn–1.2Cu alloy, suggesting a notable effect of the trace amounts of Li on the grain refinement. Moreover, a bimodal structure was formed in both the hot-extruded Zn–1.2Cu and Zn–1.2Cu–0.02Li alloys due to the precipitation of submicron-sized ɛ-CuZn4 particles. However, the latter has a much higher volume fraction of submicron-sized ɛ-CuZn4 particles than the former. Furthermore, nano-sized ɛ-CuZn4 particles precipitate from the matrix of the fine-grained region in the latter alloy. The hot-extruded Zn–1.2Cu–0.02Li alloy exhibits a yield strength (YS) of 278 ± 3 MPa and ultimate tensile strength (UTS) of 320 ± 1 MPa, which are 76% and 25% higher than the hot-extruded Zn–1.2Cu alloy (YS = 158 ± 3 MPa and UTS = 225 ± 1 MPa), indicating a strong strengthening effect of trace amounts of Li. Furthermore, the hot-extruded Zn–1.2Cu–0.02Li alloy has a lower corrosion rate than the Zn–1.2Cu alloy, indicating a positive effect of trace amounts of Li on the corrosion resistance. In addition, the hot-extruded Zn–1.2Cu and Zn–1.2Cu–0.02Li alloys both demonstrate hemolysis rates of less than 5% and antibacterial rates of more than 97%, indicating good hemocompatibility and antibacterial property. Also, the MC3T3-E1 cells show over 110% viability in the 50% extracts of both alloys, exhibiting excellent cytocompatibility.
Graphical Abstract
... Unfortunately, factors that help enhance the strength usually leads to the undesirable scattering of electrons that consequently reduce the electrical conductivity [6]. For example, the alloying approach to strengthen copper was found to seriously compromise its electrical conductivity [7]. Therefore, in order to increase the strength while maintaining high electrical conductivity in copper, it is essential to explore the basic factors that regulate these properties and associated processing techniques [8,9]. ...
... To investigate the performance of PDC when multiple suggestions are explored simultaneously, CALPHAD calculations of the Cu-Mg-Zn phase diagram were performed based on the description in Ref. [36]. Phase equilibria data were retrieved between 500 K and 1500 K with steps of 5 at.% and 50 K using the high-throughput calculation function of the Pandat software [37]. ...
To know phase diagrams is a time saving approach for developing novel materials. To efficiently construct phase diagrams, a machine learning technique was developed using uncertainty sampling, which is called as PDC (Phase Diagram Construction) package [K. Terayama et al. Phys. Rev. Mater. 3, 033802 (2019).]. In this method, the most uncertain point in the phase diagram was suggested as the next experimental condition. However, owing to recent progress in lab automation techniques and robotics, high-throughput batch experiments can be performed. To benefit from such a high-throughput nature, multiple conditions must be selected simultaneously to effectively construct a phase diagram using a machine learning technique. In this study, we consider some strategies to do so, and their performances were compared when exploring ternary isothermal sections (two-dimensional) and temperature-dependent ternary phase diagrams (three-dimensional). We show that even if the suggestions are explored several instead of one at a time, the performance did not change drastically. Thus, we conclude that PDC with multiple suggestions is suitable for high-throughput batch experiments and can be expected to play an active role in next-generation automated material development.
... For each phase diagram, phase equilibria data are retrieved across the whole 800 K isothermal section with steps of 2 at.% using the high-throughput calculation function of the Pandat software [20]. The thermodynamic descriptions adopted in the calculations are the following: Al-Cu-Mg [21], Al-Cu-Si [22], Al-Cu-Zn [23], Al-Si-Zn [24], Al-Mg-Si [25], Al-Mg-Zn [26], Cu-Mg-Si [27], Cu-Mg-Zn [28], Cu-Si-Zn [29], and Mg-Si-Zn [30]. As a result, 1326 datapoints per system are obtained. ...
... 800 K isothermal section of the Cu-Mg-Zn as (a) calculated based on the assessment from Ref.[28] (real section), (b) extrapolated from descriptions of the Cu-Mg[35], Cu-Zn[39], and Mg-Zn[26] binaries by CALPHAD calculations, and (c) predicted by RFC using the "Magpie + Thermo + CALPHAD" descriptor set. (d), (e), and (f) are the RFC predicted probabilities of having single, two and three phase domains, respectively. ...
The determination of phase diagrams is one of the central tasks in materials science. When exploring new materials whose phase diagram is unknown, experimentalists often look up for the known phase diagrams of similar systems beforehand in an attempt to select the key experiments to be performed. To enhance this practical strategy, we challenge to estimate unknown phase diagrams from known phase diagrams by a machine-learning based classification approach. As a proof-of-concept, the focus is placed on predicting the number of coexisting phases across an isothermal section of each of the ternaries of the Al-Cu-Mg-Si-Zn system from the other phase diagrams. To increase the prediction accuracy, we introduce new descriptors generated from the thermodynamic properties of the elements and CALPHAD extrapolations from lower-order systems. Using random forest method, 84% prediction accuracy is achieved. The proposed approach represents a promising tool to assist the investigator in developing new materials and determining phase equilibria efficiently.
Understanding the phase stability of a chemical system constitutes the foundation of materials science. Knowledge of the equilibrium state of a system under arbitrary thermodynamic conditions provides valuable information about the types of phases that are likely to be synthesized and how to get there. Accessing the phase diagram in a materials system provides one with the information necessary to design materials and microstructures with optimal properties. While the materials science community has long been focused on exploiting this knowledge to navigate the materials space, recent advances in machine learning (ML) and artificial intelligence (AI) have provided the community with novel ways of interrogating the materials thermodynamics space. This work presents some of the most recent advances in ML/AI applied to phase stability and thermodynamics of materials. Prof. John Morral always had a passion for understanding and teaching the fundamental characteristics of phase diagrams. This review is written to honor his memory.
The content of alloying elements in most thermally conductive magnesium alloys is as low as possible, and this will lead to lack of alloying effect. In this paper, a Mg-xZn-xCu(x=1,3,5, wt.%) alloy was studied with comparatively high alloying element contents. The thermal conductivity of this alloy declines very slowly with the increment of element concentration. Even with the additive amount of 10 wt.% of alloying elements, this alloy can attain a high thermal conductivity of 136.5 W/(m·K). After T6 treatment, the thermal conductivity of Mg-5Zn-5Cu alloy increases to 141.6 W/(m·K). The CuMgZn intermetallic compound are specially prepared to study the thermal conduction mechanism. The results show that the formation of CuMgZn ternary compound in Mg-Zn-Cu alloy consumes massive solute atoms, which makes the thermal conductivity of the alloy insensitive to alloy composition changes.
Knowledge of phase diagrams is essential for material design as it helps in understanding microstructure evolution during processing. The determination of phase diagrams is thus one of the central tasks in materials science. When exploring new materials for which the phase diagram is unknown, experimentalists often try to determine the key experiments that should be performed by referencing known phase diagrams of similar systems. To enhance this practical strategy, we attempted to estimate unknown phase diagrams based on known phase diagrams using a machine learning–based classification approach. As a proof of concept, we focused on predicting the number of coexisting phases across the 800 K isothermal section of each of the 10 ternaries of the Al-Cu-Mg-Si-Zn system from the other 9 sections. To increase the prediction accuracy, we introduced new descriptors generated from the thermodynamic properties of the elements and CALPHAD extrapolations from lower-order systems. Using the random forest method, the presence of single-, two-, and three-phase domains was predicted with an average accuracy of 84% across all 10 considered sections with a standard deviation of 11%. The proposed approach represents a promising tool for assisting the investigator in developing new materials and determining phase equilibria efficiently.
