Engineering topics

Gabriel, Joshua J.

Publications and source records attributed to Gabriel, Joshua J..

3 records

DOE OSTI2021

Bayesian automated weighting of aggregated DFT, MD, and experimental data for candidate thermodynamic models of aluminum with uncertainty quantification

Atomic-scale modeling methods such as density functional theory (DFT) and molecular dynamics (MD) can predict the thermodynamic properties of materials at a lower cost than experimental measurements. However, their regular usage in thermodynamic model construction is hampered by the lack of quantitative agreement with experimental measurements and the lack of uncertainty estimates on the data. To make regular usage of this atomistic simulation data, it is important to assess whether the atomistic simulation datasets, by themselves or in combination with experimental measurements, result in the same physics-informed models best supported by experimental measurements alone. Here, models of aluminum thermodynamic properties are discussed using three data sources: atomistic calculations (DFT and MD), experiments, and a combination of atomistic calculations and experiments. The study shows that, after ensuring self-consistency in predicting key invariant points, both experimental measurements and atomistic calculations can significantly contribute to an optimal model.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Uncertainty Quantification in Atomistic Modeling of Metals and Its Effect on Mesoscale and Continuum Modeling: A Review

The design of next-generation alloys through the integrated computational materials engineering (ICME) approach relies on multiscale computer simulations to provide thermodynamic properties when experiments are difficult to conduct. Atomistic methods such as density functional theory (DFT) and molecular dynamics (MD) have been successful in predicting properties of never before studied compounds or phases. However, uncertainty quantification (UQ) of DFT and MD results is rarely reported due to computational and UQ methodology challenges. Over the past decade, studies that mitigate this gap have emerged. These advances are reviewed in the context of thermodynamic modeling and information exchange with mesoscale methods such as the phase-field method (PFM) and calculation of phase diagrams (CALPHAD). The importance of UQ is illustrated using properties of metals, with aluminum as an example, and highlighting deterministic, frequentist, and Bayesian methodologies. Finally, challenges facing routine uncertainty quantification and an outlook on addressing them are also presented.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Interface-Driven Structural Distortions and Composition Segregation in Two-Dimensional Heterostructures

The discovery of emergent phenomena in 2D materials has sparked substantial research efforts in the materials community. A significant experimental challenge for this field is exerting atomistic control over the structure and composition of the constituent 2D layers and understanding how the interactions between layers drive both structure and properties. While no segregation for single bilayers was observed, segregation of Pb to the surface of three bilayer thick PbSe–SnSe alloy layers was discovered within [(Pb x Sn 1– x Se) 1+δ ] n (TiSe 2 ) 1 heterostructures using electron microscopy. This segregation is thermodynamically favored to occur when Pb x Sn 1– x Se layers are interdigitated with TiSe 2 monolayers. DFT calculations indicate that the observed segregation depends on what is adjacent to the Pb x Sn 1– x Se layers. As a result, the interplay between interface- and volume-free energies controls both the structure and composition of the constituent layers, which can be tuned using layer thickness.

2D materials↗