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Register a new userIngrained -- An automated framework for fusing atomic-scale image simulations into experiments
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To fully leverage the power of image simulation to corroborate and explain patterns and structures in atomic resolution microscopy (e.g., electron and scanning probe), an initial correspondence between the simulation and experimental image must be established at the outset of further high accuracy simulations or calculations. Furthermore, if simulation is to be used in context of highly automated processes or high-throughput optimization, the process of finding this correspondence itself must be automated. In this work, we introduce ingrained, an open-source automation framework which solves for this correspondence and fuses atomic resolution image simulations into the experimental images to which they correspond. We describe herein the overall ingrained workflow, focusing on its application to interface structure approximations, and the development of an experimentally rationalized forward model for scanning tunneling microscopy simulation.
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Understanding the structure and properties of refractory oxides are critical for high temperature applications. In this work, a combined experimental and simulation approach uses an automated closed loop via an active-learner, which is initialized by X-ray and neutron diffraction measurements, and sequentially improves a machine-learning model until the experimentally predetermined phase space is covered. A multi-phase potential is generated for a canonical example of the archetypal refractory oxide, HfO2, by drawing a minimum number of training configurations from room temperature to the liquid state at ~2900oC. The method significantly reduces model development time and human effort.
Cerium oxide (ceria, CeO2) is one of the most promising mixed ionic and electronic conducting materials. Previous atomistic analysis has covered widely the effects of substitution on oxygen vacancy migration. However, an in-depth analysis of the role of cation substitution beyond trivalent cations has rarely been explored. Here, we investigate soluble monovalent, divalent, trivalent and tetravalent cation substituents. By combining classical simulations and quantum mechanical calculations, we provide an insight into defect association energies between substituent cations and oxygen vacancies as well as their effects on the diffusion mechanisms. Our simulations indicate that oxygen ionic diffusivity of subvalent cation-substituted systems follows the order Gd>Ca>Na. With the same charge, a larger size mismatch with Ce cation yields a lower oxygen ionic diffusivity, i.e., Na>K, Ca>Ni, Gd>Al. Based on these trends, we identify species that could tune the oxygen ionic diffusivity: we estimate that the optimum oxygen vacancy concentration for achieving fast oxygen ionic transport is 2.5% for GdxCe1-xO2-x/2, CaxCe1-xO2-x and NaxCe1-xO2-3x/2 at 800 K. Remarkably, such a concentration is not constant and shifts gradually to higher values as the temperature is increased. We find that co-substitutions can enhance the impact of the single substitutions beyond that expected by their simple addition. Furthermore, we identify preferential oxygen ion migration pathways, which illustrate the electro-steric effects of substituent cations in determining the energy barrier of oxygen ion migration. Such fundamental insights into the factors that govern the oxygen diffusion coefficient and migration energy would enable design criteria to be defined for tuning the ionic properties of the material, e.g., by co-doping.
Dendrite formation is a major obstacle, such as capacity loss and short circuit, to the next-generation high-energy-density lithium (Li) metal batteries. The development of successful Li dendrite mitigation strategies is impeded by an insufficient understanding of Li dendrite growth mechanisms. Li-plating-induced internal stress in Li metal and its effect on dendrite growth have been studied in previous models and experiments, while the underlying microcosmic mechanism is elusive. Here, we analyze the role of plating-induced stress in dendrite formation through first-principles calculations and ab initio molecular dynamics simulations. We show that the deposited Li forms a stable atomic nanofilm structure on copper (Cu) substrate. It is found that the adsorption energy of Li atoms increases from the Li-Cu interface to deposited Li surface, leading to more aggregated Li atoms at the interface. Compared to the pristine Li metal, the deposited Li in the early stage becomes compacted and suffers in-plane compressive stress. Interestingly, we find that there is a giant strain gradient distribution from the Li-Cu interface to deposited Li surface, which makes the deposited atoms adjacent to the Cu surface tend to press upwards with perturbation, causing the dendrite growth. This understanding provides an insight to the atomic-scale origin of Li dendrite growth and may be useful for suppressing the Li dendrite in the Li-metal-based rechargeable batteries.
The c(6x2) is a reconstruction of the SrTiO3(001) surface that is formed between 1050-1100oC in oxidizing annealing conditions. This work proposes a model for the atomic structure for the c(6x2) obtained through a combination of results from transmission electron diffraction, surface x-ray diffraction, direct methods analysis, computational combinational screening, and density functional theory. As it is formed at high temperatures, the surface is complex and can be described as a short-range ordered phase featuring microscopic domains composed of four main structural motifs. Additionally, non-periodic TiO2 units are present on the surface. Simulated scanning tunneling microscopy images based on the electronic structure calculations are consistent with experimental images.
Atomic scale simulations are a key element of modern science in that they allow to understand, and even predict, complex physical or chemical phenomena on the basis of the fundamental laws of nature. Among the different existing atomic scale simulation approaches, molecular dynamics (MD) has imposed itself as the method of choice to model the behavior of the structure of materials under the action of external stimuli, say temperature, strain or stress, irradiation, etc. Despite the widespread use of MD in condensed matter science, some basic material characteristics remain difficult to determine. This is for instance the case of the long-range strain tensor in heavily disordered materials, or the quantification of rotated crystalline domains lacking clearly defined boundaries. In this work, we introduce computational diffraction as a fast and reliable structural characterization tool of atomic scale simulation cells. As compared to usual direct-space methods, computational diffraction operates in the reciprocal-space and is therefore highly sensitive to long-range spatial correlations. With the example of defective UO2, it is demonstrated that the homogeneous strain tensor, the heterogeneous strain tensor, the disorder, as well as rotated crystallites are straightforwardly and unambiguously determined. Computational diffraction can be applied to any type of atomic scale simulation and can be performed in real time, in parallel with other analysis tools. In experimental workflows, diffraction and microscopy are almost systematically used together in order to benefit from their complementarity. Computational diffraction, used together with computational microscopy, can potentially play a major role in the future of atomic scale simulations.
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