Monday 8:00 AM

March 14, 2022

Room: Materials Design

Location: On-Demand Room

While it is well-established that atoms diffuse along interfaces much faster than in the perfect lattice, atomistic understanding of this “short-circuit” diffusion phenomenon remains incomplete. We will discuss several topics related to interface diffusion: atomic mechanisms of grain boundary (GB) diffusion, the effect of GB segregation on the GB diffusion, and the role of GB diffusion in the solute drag. These effects are studied by atomistic computer simulations combining molecular dynamics, Monte Carlo methods, and other computational approaches. It is shown that solute segregation can cause acceleration or retardation of GB diffusion, depending on the system’s thermodynamics, GB structure, and the segregation mechanism. In a solute-drag study, the drag force has been investigated systematically as a function of temperature, alloy composition, and the GB velocity. GB diffusion coefficients and diffusion mechanisms are compared with those for metal/semiconductor phase boundaries with different orientation relationships between the phases.

During creep multiple processes occur simultaneously, straining just beyond the elastic limit while diffusion, especially along grain boundaries, can play a significant role. In self-healing creep steels the accumulation of vacancies, which ultimately leads to nanovoid formation, can be countered by grain boundary segregation and localized precipitation. In the presentation the role of highly localized non-average (deviatoric) stresses at grain boundaries on diffusional fluxes and precipitation will be discussed.

The free energy encodes the thermodynamic coupling between mechanics and chemistry within continuum descriptions of non-equilibrium materials phenomena. In mechano-chemically interacting materials systems, even consideration of only compositions, order parameters and strains results in a free energy description that occupies a high-dimensional space. Scale bridging between the electronic structure of a solid and continuum descriptions of its non-equilibrium behavior can be realized with integrable deep neural networks (IDNN) that are trained to free energy derivative data generated by first-principles statistical mechanics simulations and then analytically integrating to recover a free energy density function. Here we combine the IDNN with an active learning workflow to ensure well-distributed sampling of the free energy derivative data in high-dimensional input spaces, thereby enabling true scale bridging between first-principles statistical mechanics and continuum phase field models. As a prototypical material systems we focus on applications in Ni-Al alloys and in the battery cathode material: LixCoO2.

The phase field approach provides a powerful tool for the investigation of microstructural evolution and material design through microstructural control. However, quantitative predictions require accurate material-specific thermodynamic and kinetic input. Atomistic modeling techniques, such as density functional theory and the Monte Carlo approach, are critical for generating information that are difficult or impossible to obtain through experiments. This talk will highlight the applications of integrated approaches involving atomistic and phase field methods, as well as resources that are now freely available to the community, developed through the Predictive Integrated Structural Material Science (PRISMS) Center, a DOE Software Innovation Center, to which Prof. Van der Ven contributes to. The highlights will include a collaborative work with Prof. Van der Ven, as well as recently developed phase field modeling capabilities. A perspective on the future directions in integrated, multiscale computational approaches will also be discussed.

Cross phenomena, representing responses of a system to external stimuli, are ubiquitous in every scale from quantum to macro. The Onsager theorem is often used to describe that the coefficient matrix of cross phenomena connecting the driving forces and the fluxes of internal processes is symmetric. Here we show that this matrix is intrinsically diagonal when the driving forces are chosen from the potentials in the combined law of thermodynamics. We emphasize that an internal process for the change of a molar quantity is driven solely by its conjugate potential based on the combined law of thermodynamics, and cross phenomena manifest through the dependence of the potential on other potentials and are not related to the Onsager reciprocal relations. An approach is presented to predict the coupling coefficients through the second derivatives of free energy or first derivatives between potentials.

Alloy nanoparticles are promising catalysts due to their high surface-to-volume ratios and structural flexibility. Catalytic properties can be optimized by modifying the size, shape, and atomic order of the nanoalloys. However the variety of possible structures also poses a significant challenge for the rational design of catalysts. I will demonstrate how this challenge can be addressed by using density functional theory (DFT) calculations and cluster expansions to understand the properties of nanoalloy catalysts and guide synthesis efforts. With a focus on Pt-Ni catalysts for the oxygen reduction reaction, I will show how DFT calculations, combined with experimental analysis, provide an explanation for how nanoparticle stability can be improved by alloying with Cu. I will also present novel computationally-generated size-composition catalytic activity maps of Pt-Ni nanoparticles. These maps reveal the nanoparticle sizes and compositions that are predicted to maximize catalytic activity, an important step towards the rational design of alloy nanocatalysts.

Deviations from the ideal bulk stoichiometry of ordered intermetallic compounds are commonly realized by point defects, such as antisites or vacancies. Recent high-resolution electron microscopy experiments show, however, a second mechanism: A high concentration of extended defects such as stacking faults that can accommodate large amounts of alloying elements. To understand the formation and thermodynamics of such highly nonstoichiometric defects we have adapted and generalized the concept of defect phase diagrams. These diagrams show, in analogy to the well-established thermodynamic bulk phase diagrams, the thermodynamically most stable defect phase as function of state variables such as temperature, chemical potential, stresses etc. The power and performance of this concept are demonstrated using the example of nonstoichiometric stacking faults in the Fe-Nb Laves phase where these diagrams are shown to provide an accurate tool to correctly predict the rich set of experimentally observed defect structures [Acta Materialia 183, 362 (2020)].