Half-Heusler Structures with Full-Heusler Counterparts: Machine-Learning Predictions and Experimental Validation

Gzyl, Alexander; Oliynyk, Anton O.; Mar, Arthur

doi:10.1021/acs.cgd.0c00646

Cited by 27 publications

(37 citation statements)

References 55 publications

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“…With more than a thousand members reported in the literature, the Heusler family remains one of the most interesting and intensively studied intermetallic systems in materials science 1 . Among this class of materials we can find catalysts 2 , ferromagnets 3 , 4 , thermoelectric 5 – 7 and magnetocaloric materials 8 .…”

Section: Introductionmentioning

confidence: 99%

Superconductivity in LiGa2Ir Heusler type compound with VEC = 16

Górnicka

Kuderowicz

Winiarski

et al. 2021

Sci Rep

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Polycrystalline LiGa2Ir has been prepared by a solid state reaction method. A Rietveld refinement of powder x-ray diffraction data confirms a previously reported Heusler-type crystal structure (space group Fm-3m, No. 225) with lattice parameter a = 6.0322(1) Å. The normal and superconducting state properties were studied by magnetic susceptibility, heat capacity, and electrical resistivity techniques. A bulk superconductivity with Tc = 2.94 K was confirmed by detailed heat capacity studies. The measurements indicate that LiGa2Ir is a weak-coupling type-II superconductor ($${\uplambda }$$ λ e–p = 0.57, $${\Delta }$$ Δ C/$${\upgamma }$$ γ Tc = 1.4). Electronic structure, lattice dynamics, and the electron–phonon interaction are studied from first principles calculations. Ir and two Ga atoms equally contribute to the Fermi surface with a minor contribution from Li. The phonon spectrum contains separated high frequency Li modes, which are seen clearly as an Einstein-like contribution in the specific heat. The calculated electron–phonon coupling constant $${\uplambda }$$ λ e–p = 0.68 confirms the electron–phonon mechanism for the superconductivity. LiGa2Ir and recently reported isoelectronic LiGa2Rh are the only two known representatives of the Heusler superconductors with the valence electron count VEC = 16.

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Section: Introductionmentioning

confidence: 99%

Superconductivity in LiGa2Ir Heusler type compound with VEC = 16

Górnicka

Kuderowicz

Winiarski

et al. 2021

Sci Rep

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show abstract

“…Our model reproduces an expected correlation between DFT stability and synthesizability, yet it also can identify metastable HHs and stable HHs that are unsynthesizable (compositions where a non-HH structure is metastable). We show prediction improvements for several HHs predicted by Gzyl et al [45], and our model achieves a precision of 0.83 and recall of 0.85. We conclude by applying our model on several thousand ABC compositions unreported in literature and identify promising candidates for further experimental study.…”

Section: Correlatedmentioning

confidence: 60%

“…We begin by focusing on the synthesizability models by Gzyl et al [45] and Legrain et al [46], which predict the synthesizability of an ABC composition in the HH structure. Legrain et al used a random forest algorithm with a training dataset comprised of ABC compositions flagged as reported in the ICSD within the AFLOW [12] database.…”

Section: Previous Half-heusler Synthesizability Modelsmentioning

confidence: 99%

“…Figure 2: Comparisons of E hull from the OQMD and synthesizability for synthesizable HHs as determined by Gzyl et al's [45] and Legrain et al's [46] models. Both models are not informed of DFT stability and predict many unstable HHs to be synthesizable.…”

Section: Synthesizability Vs Dft Stability For Previous Hh Modelsmentioning

confidence: 99%

“…Nevertheless, we are confident VRhSn and MnRuSb are not ordered HHs as originally claimed. For each composition in table 2, we also list the compounds that [45] for the seven compositions their model predicted to be HH. We simulate XRD patterns (see Supplemental fig.…”

Section: Importancementioning

confidence: 99%

See 2 more Smart Citations

Machine Learned Synthesizability Predictions Aided by Density Functional Theory

Lee¹,

Sarker²,

Saal³

et al. 2022

Preprint

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A grand challenge of materials science is the computational prediction of synthesis pathways for novel compounds. Data-driven and machine learning (ML) approaches have made significant progress in addressing a portion of this problem, namely, predicting whether a compound is synthesizable or not. However, some recent attempts to do so have not incorporated energetic or phase stability information. Here, we combine thermodynamic stability calculated using density functional theory (DFT) with composition-based features to train a ML model that predicts a material's synthesizability. We focus on compositions with ABC stoichiometry and predict synthesizability in the half-heusler (HH) structure. Our model is trained on experimentally reported ABC compositions, and achieves a cross-validated precision of 0.83 and recall of 0.85. Our model shows improvement in identifying compositions that do not form HH when compared to a similar HH synthesizability model from a previous study. Our model identifies 163 synthesizable candidates out of 4141 unreported ABC compositions. More notably, 38 stable compositions are predicted unsynthesizable while 103 unstable compositions are predicted synthesizable; these findings otherwise cannot be made using DFT stability alone. This study presents a new approach for accurately predicting synthesizability, and identifies newly synthesizable candidate HH compositions for further experimental exploration.

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Machine Learning in Solid‐State Chemistry: Heusler Compounds

Gzyl

Mar

Oliynyk

2021

Encyclopedia of Inorganic and Bioinorganic Chemistry

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Machine learning attempts to find underlying trends in data and offer predictions of outcomes. When machine learning is applied to materials science, in a discipline called materials informatics, the complex relationships between composition, structure, and properties can be unraveled even when the quantity of data is limited. To illustrate this application, the large class of materials known as Heusler compounds are modeled through machine learning, enabling new candidates to be predicted or existing compounds to be screened for potentially interesting properties. Data, algorithms, and preprocessing techniques are important components of a successful machine‐learning model. Efforts to predict structures and properties of Heusler compounds are reviewed, and other machine‐learning approaches to discover materials in general are discussed. Ultimately, a machine‐learning model is only valuable if its predictions are validated by experimental results. Thus, perspectives are offered to guide experimentalists on how machine learning can be useful for targeting new Heusler compounds.

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Half-Heusler Structures with Full-Heusler Counterparts: Machine-Learning Predictions and Experimental Validation

Cited by 27 publications

References 55 publications

Superconductivity in LiGa2Ir Heusler type compound with VEC = 16

Superconductivity in LiGa2Ir Heusler type compound with VEC = 16

Machine Learned Synthesizability Predictions Aided by Density Functional Theory

Machine Learning in Solid‐State Chemistry: Heusler Compounds

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