Publications by authors named "Sogol Lotfi"

The physical properties of solid-state materials are closely tied to their crystal structure, yet our understanding of how competing structural arrangements energetically compare is limited. In this work, we explore how small differences in composition affect the structure in the La(AuGe) series of compounds, which comprises four unique structure types between LaGe and LaAu. This family includes the previously unknown AlB-type compound with stoichiometry La(AuGe) as well as La(AuGe), an intergrowth of the AlB and ThSi structure types.

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Polar intermetallics are an intriguing class of compounds with complex relationships between composition and structure that are not fully understood. This work reports a systematic study of the underexplored ternary composition space RE-Au-Tt (RE = La, Ce, Pr, Nd; Tt = Ge, Pb) to expand our knowledge of the intriguing chemistry and diversity achievable with these metallic constituents. These composition spaces are particularly interesting because of the potential to find Au-bearing, highly polar intermetallic compounds.

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Intermetallics adopt an array of crystal structures, boast diverse chemical compositions, and possess exotic physical properties that have led to a wide range of applications from the biomedical to aerospace industries. Despite a long history of intermetallic synthesis and crystal structure analysis, identifying new intermetallic phases has remained challenging due to the prolonged nature of experimental phase space searching or the need for fortuitous discovery. In this Minireview, new approaches that build on the traditional methods for materials synthesis and characterization are discussed with a specific focus on realizing novel intermetallics.

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During a systematic search of the RE-Au-Sn (RE = La, Ce, Pr, Nd) ternary phase space, a series of compounds with the general formula REAuSn have been identified. These phases can be synthesized by arc melting the elemental metals, followed by annealing. The crystal structures were solved using single-crystal X-ray diffraction, with the composition confirmed by energy-dispersive X-ray spectroscopy.

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A method to predict the crystal structure of equiatomic ternary compositions based only on the constituent elements was developed using cluster resolution feature selection (CR-FS) and support vector machine (SVM) classification. The supervised machine-learning model was first trained with 1037 individual compounds that adopt the most populated ternary 1:1:1 structure types (TiNiSi-, ZrNiAl-, PbFCl-, LiGaGe-, YPtAs-, UGeTe-, and LaPtSi-type) and then validated using an additional 519 compounds. The CR-FS algorithm improves class discrimination and indicates that 113 variables including size, electronegativity, number of valence electrons, and position on the periodic table (group number) influence the structure preference.

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