Scientific Events

The development and improvement of catalysts for chemical energy conversion, such as (photo-)electrocatalytic water splitting or alcohol oxidation, requires mechanistic understanding at theatomic/molecular level. In my talk I will address several examples for the application of densityfunctional theory calculations to model, understand and tailor the catalytic activity of anodematerials for water splitting. To disentangle the role of structural motifs, crystallographic orientationand dopants, I will focus on iron and cobalt containing transition metal oxides with spinel [1-3],corundum [4] vs. perovskite [5] structure. The aim is to establish a link between the energetic trendsand the underlying structural and electronic properties and to identify potential active sites. Afurther topic is the reduction of iron oxide surfaces and bulk via hydrogen adsorption [6] andincorporation.Funding by the German Research Foundation DFT within SPP 1613 and CRC TRR247 as well ascomputational time at the Leibniz Rechenzentrum and the supercomputer MagnitUDE at UDE isgratefully acknowledged.[1] K. Chakrapani, G. Bendt, H. Hajiyani, I. Schwarzrock, T. Lunkenbein, S. Salamon, J. Landers, H.Wende, R. Schlögl, R. Pentcheva, M. Behrens, S. Schulz, ChemCatChem. 9, 2988-2995, (2017)[2] H. Hajiyani, R. Pentcheva, ACS Catal. 8, 11773-11782 (2018)[3] Y. Peng, H. Hajiyani, R. Pentcheva, ACS Catal. 11, 5601–5613, (2021)[4] A.G. Hufnagel, H. Hajiyani, S. Zhang, T. Li, O. Kasian, B. Gault, B. Breitbach, T. Bein, D. Fattakhova-Rohlfing, C. Scheu, R. Pentcheva, Adv. Funct. Mater., 165, 1804472 (2018)[5] A. Füngerlings, A. Koul, M. Dreyer, A. Rabe, D. M. Morales, W. Schuhmann, M. Behrens, and R.Pentcheva, Chemistry - A European Journal, accepted.[6] G. S. Parkinson, N. Mulakaluri, Y. Losovyj, P. Jacobson, R. Pentcheva, and U. Diebold, Phys. Rev. B82, 125413 (2010).

Where: virtual on Zoom (link follows) [more]

Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics through self-driving cars are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiment (AE) in areas ranging from materials synthesis to imaging. The implementation of this vision requires the synergy of three intertwined components, including the engineering controls of the physical tools, the algorithmic developments that allow to define non-trivial exploratory patterns, and physics-driven interpretation. In this presentation, I will discuss recent progress in automated experiment in electron and scanning probe microscopy, ranging from feature finding to physics discovery via active learning. The applications of classical deep learning methods in streaming image analysis are strongly affected by the out of distribution drift effects, and the approaches to minimize though are discussed. We further present invariant variational autoencoders as a method to disentangle affine distortions and rotational degrees of freedom from other latent variables in imaging and spectral data. The analysis of the latent space of autoencoders further allows establishing physically relevant transformation mechanisms. Extension of encoder approach towards establishing structure-property relationships will be illustrated on the example of plasmonic structures. Finally, I illustrate transition from post-experiment data analysis to active learning process. Here, the strategies based on simple Gaussian Processes often tend to produce sub-optimal results due to the lack of prior knowledge and very simplified (via learned kernel function) representation of spatial complexity of the system. Comparatively, deep kernel learning (DKL) methods allow to realize both the exploration of complex systems towards the discovery of structure-property relationship, and enable automated experiment targeting physics (rather than simple spatial feature) discovery. The latter is illustrated via experimental discovery of the edge plasmons in STEM/EELS and ferroelectric domain dynamics in PFM. This research is supported by the by the U.S. Department of Energy, Basic Energy Sciences, Materials Sciences and Engineering Division and the Center for Nanophase Materials Sciences, which is sponsored at Oak Ridge National Laboratory by the Scientific User Facilities Division, BES DOE. [more]

Besides their ubiquitous use in load-bearing structures, metals also possess qualities of energetic materials. Lithium, for example, is a common fuel in batteries, while aluminum is frequently added to solid rocket propellants and used in pyrotechnics. At high temperatures, metal powders can be burned in air in a similar way to hydrocarbon fuels, releasing chemically stored energy as sensible heat. Contrary to hydrocarbon combustion, however, the main reaction products are solid oxide particles that can, in principle, be retrieved from the exhaust fumes. This amenability to oxide sequestration has stimulated the idea of harnessing metal powders as recyclable energy carriers which are burned, retrieved and, subsequently, recharged by a reduction process based on clean primary energy. Conceptually, the metal powders are akin to high-temperature batteries, serving as a means to buffer the large spatial and temporal intermittency associated with renewable energy sources. Motivated by the potential use of metal powders as recyclable fuels, we qualitatively discuss the physical and chemical processes involved in the combustion of a single metal particle and of metal dusts, respectively. Subsequently, a population balance model for predicting the size distribution of the oxide smoke precipitating in the vicinity of a single burning aluminum particle is presented. Here, we specifically focus on the kinetic rates that control the phase transitions and smoke dynamics, integrating recently developed detailed kinetics for gas phase and heterogeneous surface reactions. The population balance equation governing the oxide size distribution is solved with the aid of a tailored adaptive grid method. An alternative, potentially more economical solution approach we propose is based on an embedded reduced order representation of the particle size distribution that is informed by a training step. The accuracy and convergence properties of this method are investigated for a simplified test case involving particle growth and dispersion in a laminar plane jet. In the final part of the seminar, the physical description, from an Eulerian viewpoint, of metal powders is discussed with a particular emphasis on the ramifications of carrier flow turbulence. In order to account for the small-scale interactions between dispersed particles and the ambient gas phase, the population balance equation governing the metal powder or oxide smoke is integrated into a probabilistic description that naturally accounts for the variability among independent realizations of a turbulent, particle-laden flow. Owing to the high dimensionality of the resulting transport equations, a stochastic solution approach based on Eulerian stochastic fields is proposed for which we show preliminary accuracy and convergence analyses.

Abstract: Many layered materials of interest for electrochemical energy storage and conversion applications are flexible hosts whose interlayers can be expanded to accommodate not just ions but also solvents, organic molecules, polymers, and organometallics. When these “hybrid” materials are placed into an electrochemical environment, the distinction between surface and bulk becomes blurred since the electrochemical interface can now be viewed to extend into the interlayer. During this seminar, I will discuss fundamental aspects of charge storage at electrochemical interfaces and how interfacial charge storage and reactivity change under confinement. I will also describe synthesis of hybrid layered materials and the use of in situ and operando characterization to understand the relationships between structure and composition and the resulting electrochemical reactivity. [more]

Engineered systems are an indispensable part of our modern life with far-reaching applications that include aerial and ground transportation, electronics, large-scale structures, and medicine. The ever-evolving societal, environmental, and cultural awareness calls for significantly complex systems with unprecedented properties that reliably meet stakeholders’ demands under extreme conditions. To accelerate the design and deployment of such systems while reducing the reliance on costly and time-consuming experiments, it is necessary to develop advanced computational methods that streamline their design and analysis process. In this talk, I will present some of our recent works for solving challenging problems in engineering design, solid mechanics, and fluid dynamics. In particular, I will demonstrate how we can (1) accelerate multiscale simulations of casting materials ten times via mechanistic reduced order models, (2) surrogate plastic and history dependent deformation of fiber composites with deep learning, (3) optimize material composition with latent map Gaussian processes and Bayesian optimization, and (4) solve partial differential equations with transfer learning. [more]

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