|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|1560095||1513902||2015||5 صفحه PDF||سفارش دهید||دانلود رایگان|
• Density functional theory (DFT) calculations of properties of amorphous SiO2 and Si3N4.
• Density, bulk modulus, and defect formation energy distributions were then determined.
• Intrinsic atomic variability outweigh those originating from the theoretical approximations.
• Importance of ensemble-based approaches to characterize properties of such materials.
• Approach/methodology is widely applicable for uncertainties in other amorphous materials.
We quantify uncertainties in density functional theory predictions of several fundamental materials properties of amorphous dielectrics focusing on those that arise from the intrinsic atomic variability of the glass structures and those stemming from approximations in the theory. The intrinsic, or aleatoric, uncertainties are quantified by performing calculations over ensembles of structures obtained by annealing independent liquid samples. We estimate model form, or epistemic, uncertainties by comparing results from two exchange and correlation functionals that exhibit different bonding characteristics: the local density approximation (that typically overbinds), and the generalized gradient approximation (that often underbinds). In the case of density, bulk modulus, and point defect formation energies predictions obtained from systems containing between 72 and 192 atoms, typical of current state-of-the-art calculations, show that the intrinsic variability in the atomic structure leads to uncertainties a factor of two to four times greater than those originating from model form. While model form discrepancies remain important, our results emphasize the importance of using ensembles of structures to make predictions of amorphous materials. The use of such probabilistic atomic-level data as input in multiscale materials or device models is critical for predictions with quantified uncertainties but also to uncover how atomic variability affects device performance.
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Journal: Computational Materials Science - Volume 109, November 2015, Pages 124–128