Crystal structure, electronic structure and phase stability of the Cu2-xMxCd (M=Zn, Ga, Ge, Sn) pseudo-binary Laves phases: effect of valence electron concentration

Roy, Nilanjan; Kuila, Sandip K.; Mondal, Amit; Sikdar, Ritobroto; Harshit, -; Ghanta, Sivaprasad; Wang, Fei; Jana, Partha P.

doi:10.1016/j.jssc.2022.123283

Cited by 4 publications

(4 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the time of the development of these machine learning models, independent investigations were being conducted on the related solid solutions Cd(Cu 1– x M x ) 2 for M = Zn, Ga, Ge, and Sn . The Ga- and Ge-containing solid solutions were previously known .…”

Section: Resultsmentioning

confidence: 99%

“…The related solid solutions Cd(Cu 1– x Sn x ) 2 and Cd(Cu 1– x Zn x ) 2 , which also have high probabilities of being Laves phases, were prepared and evaluated in a similar manner as above …”

Section: Methodsmentioning

confidence: 99%

“…The related solid solutions Cd(Cu 1−x Sn x ) 2 and Cd(Cu 1−x Zn x ) 2 , which also have high probabilities of being Laves phases, were prepared and evaluated in a similar manner as above. 42 Structure Determination. Intensity data were collected on a single crystal of CdCu 1.80 Sb 0.20 at room temperature on a Bruker D8 QUEST diffractometer equipped with a PHOTON II CCD detector and a Mo Kα radiation source (λ = 0.71073 Å).…”

Section: ■ Introductionmentioning

confidence: 99%

“…At the time of the development of these machine learning models, independent investigations were being conducted on the related solid solutions Cd(Cu 1−x M x ) 2 for M = Zn, Ga, Ge, and Sn. 42 The Ga-and Ge-containing solid solutions were previously known. 63 However, the Zn-and Sn-containing ones were unseen by the machine learning models, which predict that they are likely to form Laves phases over different ranges of substitution (Figure 7).…”

Section: ■ Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Ahead by a Century: Discovery of Laves Phases Assisted by Machine Learning

Sikdar,

Roy,

Selvaratnam

et al. 2024

Inorg. Chem.

Self Cite

View full text Add to dashboard Cite

Laves phases AB 2 form the most abundant group of intermetallic compounds, consisting of combinations of larger electropositive metals A with smaller metals B. Many practical applications of Laves phases depend on the ability to tune their physical properties through appropriate substitution of either the A or B component. Although simple geometrical and electronic factors have long been thought to control the formation of Laves phases, no single factor alone can make predictions accurately. Several machine learning models have been developed to discover new Laves phases, including variations caused by solid solubility, using elemental properties solely on the basis of chemical composition. These models were trained on a data set comprising about 3700 entries of experimentally known phases AB 2 with Laves and non-Laves structures. Among these models, a decision tree algorithm gave very good performance (average recall of 95%, precision of 94%, and accuracy of 96% on the test set) by using only a small set of descriptors, the most important of which relates to the electron density at the boundary of the Wigner−Seitz cell for the B component. This model provides guidance for new experiments by making predictions on >400000 candidates very quickly. A chemically unintuitive candidate Cd(Cu 1−x Sb x ) 2 with a limited solid solubility of Sb for Cu was targeted; it was successfully synthesized and confirmed to adopt a cubic MgCu 2 -type Laves structure.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

See 2 more Smart Citations