Tuesday 8:00 AM

March 1, 2022

Room: 255A

Location: Anaheim Convention Center

Disordered multicomponent systems - occupying the mostly uncharted centers of phase diagrams - have been studied for the last two decades for their potential revolutionary properties. Very resilient compositions can be stabilized by maximizing entropy of (near) equimolar mixtures. The search for new systems is mostly performed with trial-and-error techniques, as effective computational discovery is challenged by the immense number of configurations. Synthesizability is typically assessed using ideal entropy along with the formation enthalpies from DFT, with simplified descriptors or machine learning methods. With respect to vibrations, even if they may be significant on phase stability, their contributions are drastically approximated to reduce the high computational cost, or avoided with the hope of them being negligible. In this presentation I will address many of the problems in the discovery of disordered systems, offer some effective solutions, and discuss the avenues opened by the latter.

In this talk, I will discuss several strategies that we have been developed in my group and with collaborators to accelerate the rate at which the vast HEA space can be explored. Specifically, we introduce some examples in which we use novel ML-assisted algorithms to identify the feasible regions in a number of important FCC and BCC HEA systems. We also discuss the incorporation of closed loop high-throughput frameworks for the exploration of these alloy spaces via computational and experimental means. Some conclusions on the likely high-value regions of these systems will be presented, which hopefully will motivate further work to explore this very complex chemical space.

The PRISMS Center was founded to address key challenges posed by the Materials Genome Initiative. This talk will review PRISMS Center’s progress in creation and dissemination of a framework for accelerating predictive materials science. There are three key elements of this framework. This first is a suite of high performance, open-source multi-scale computational tools for predicting microstructural evolution and mechanical behavior of structural metals. The second is the Materials Commons, a knowledge repository and virtual collaboration space for archiving and disseminating information. The third element is a set of scientific “Use Cases” in which the results of simulations, experiments and theory are integrated to accelerate predictive understanding of magnesium and Mg alloys. This talk will focus on our progress improving understanding of twinning and detwinning in Mg and Mg alloys during cyclic deformation by integrating knowledge ranging from electronic and atomistic structure to microscopic and macroscopic mechanical behavior.

Phenomenological models that accurately describe the high-temperature behavior of materials are crucial to the design and discovery of high-performance alloys. At the atomistic scale, these models must capture a range of structural and chemical disorder. Atomistic models can then be coupled with statistical mechanics techniques to derive thermodynamic, kinetic and chemo-mechanical descriptions of materials. In this talk, we will highlight recent theoretical advances that enable the rigorous coarse-graining of electronic structure calculations through statistical mechanics techniques. Outputs from our methodology can be used to inform continuum descriptions of microstructure and dislocation evolution in multi-component alloys. We will employ these techniques to model the high-temperature behavior of multi-component magnesium and refractory alloys.

Extended defects in crystalline solids such as dislocations, grain boundaries, stacking faults, and deformation twins may cause solute re-distribution, alter phase equilibria and change phase transformation behavior. If the extended defects are generated during deformation, dynamic phase transformations may occur at these defects, which will alter the deformation pathway and hence impact significantly the mechanical properties. In this presentation, we show that starting from ab initio calculations of generalized stacking fault (GSF) energy and interaction energy between solute and the extended defects, one can predict (a) the structures of these extended defects and novel deformation mechanisms such as dislocation transformation by using the microscopic phase field method; (b) Solute segregation at the defects by using segregation isotherm through coupling with thermodynamic databases; (c) localized phase transformation (LPT) at stacking faults, offering either LPT-strengthening or LPT-softening during deformation. The work is supported by NSF under DMREF programs.

Databases of density functional theory (DFT) calculations, such as the Open Quantum Materials Database (OQMD), have paved the way for accelerated materials discovery. DFT calculations require crystal structure information as input; however, due to inherent challenges in solving a compound’s structure from powder diffraction data alone, there are thousands of experimentally synthesized compounds whose structures remain unsolved. We present a rapid DFT-based structure solution method capable of resolving numerous outstanding structure solution problems at low computational cost. As this approach is straightforward and inexpensive, we employ it to solve 514 previously unsolved structures from the Powder Diffraction File, resulting in a 1.3% expansion of the set of all experimental structures in the OQMD. In addition, we apply a variety of machine-learning-based tools to automate the prediction of new inorganic compounds. Leveraging these tools, we perform high throughput predictions of thousands of new stable, previously-undiscovered inorganic compounds.