2016
DOI: 10.1103/physrevb.94.045105
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Computational and experimental investigation for new transition metal selenides and sulfides: The importance of experimental verification for stability

Abstract: Expanding the library of known inorganic materials with functional electronic or magnetic behavior is a longstanding goal in condensed matter physics and materials science. Recently, the transition metal chalchogenides including selenium and sulfur have been of interest because of their correlated-electron properties, as seen in the iron based superconductors and the transition metal dichalcogenides. However, the chalcogenide chemical space is less explored than that of oxides, and there is an open question of… Show more

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Cited by 33 publications
(32 citation statements)
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“…(29)) for the correlated computation will be sharper, if the energy of X falls below the hull, its probability will clearly be larger than 0.5, whereas if the energy is well above the hull we argue that it is unlikely to exist. Narayan et al 112 constructed a related statistical model to address theoretical predictions of new materials using formation energies computed with the Materials Project corrections. They proposed an optimal cutoff energy 0 = 0.1 eV , to minimize the rates of false-positives and false-negatives.…”
Section: Statistical Interpretationmentioning
confidence: 99%
“…(29)) for the correlated computation will be sharper, if the energy of X falls below the hull, its probability will clearly be larger than 0.5, whereas if the energy is well above the hull we argue that it is unlikely to exist. Narayan et al 112 constructed a related statistical model to address theoretical predictions of new materials using formation energies computed with the Materials Project corrections. They proposed an optimal cutoff energy 0 = 0.1 eV , to minimize the rates of false-positives and false-negatives.…”
Section: Statistical Interpretationmentioning
confidence: 99%
“…Unfortunately, attempted syntheses of many of the new compounds that have been predicted to be stable have failed. For example, a recent paper predicted 24 likely new compounds in phase diagrams that did not contain any known ternary compounds . The authors tried to prepare these predicted compounds, containing a cation, a transition metal and a chalcogen, using several synthesis approaches.…”
Section: Introductionmentioning
confidence: 99%
“…However, the number of ab initio evaluations critically drops to a few tens of evaluations when exploring non-equilibrium properties (e.g., ionic conductivity) because the computational cost is too large even with a massive multicore architecture 5,6 . A combination of ab initio calculations and machine learning is therefore employed to broaden the search space 5,[7][8][9] . The rapid inference of machine learning yields potential candidates from hundreds of thousands of compounds in a database as a first-pass screening.…”
Section: Introductionmentioning
confidence: 99%