First-principles investigation of competing magnetic interactions in (Mn,Fe)Ru2_2Sn Heusler solid solutions

Many Heusler compounds possess magnetic properties well-suited for applications as spintronic materials. The pseudo-binary Mn$_{0.5}$Fe$_{0.5}$Ru$_2$Sn, formed as a solid solution of two full Heuslers, has recently been shown to exhibit exchange hardening suggestive of two magnetic phases, despite existing as a \textit{single} chemical phase. We have performed a first-principles study of the chemical and magnetic degrees of freedom in the Mn$_{1-x}$Fe$_{x}$Ru$_2$Sn pseudo-binary to determine the origin of the unique magnetic behavior responsible for exchange hardening within a single phase. We find a transition from antiferromagnetic (AFM) to ferromagnetic (FM) behavior upon replacement of Mn with Fe, consistent with experimental results. The lowest energy orderings in Mn$_{1-x}$Fe$_{x}$Ru$_2$Sn consist of chemically- and magnetically-uniform (111) planes, with Fe-rich regions preferring FM ordering and Mn-rich regions preferring AFM ordering, independent of the overall composition. Analysis of the electronic structure suggests that the magnetic behavior of this alloy arises from a competition between AFM-favoring Sn-mediated superexchange and FM-favoring RKKY exchange mediated by spin-polarized conduction electrons. Changes in valency upon replacement of Mn with Fe shifts the balance from superexchange-dominated interactions to RKKY-dominated interactions.Comment: 14 pages, 9 figure

Protein Engineering of Bacterioferritin: Applications to Bionanotechnology

Biological protein assemblies are the products of evolutionary necessity that have arisen to protect the cell, improve reaction rates, and/or compartmentalize biological systems. Belonging to this category of sel-assembling bionano-structures are cage proteins. Cage proteins are composed of multiple protomers (subunits) that self-assemble into many unique structures, all having interior, exterior, and interface surfaces available for controllable modification. Bacterioferritins (Bfr) are a set of cage proteins belonging to the ferritin superfamily of iron storage proteins, are roughly spherical with a 12 nm exterior diameter and an 8 nm interior diameter, are composed of 24 identical protomers, and contain 12 heme cofactors. An important feature of Bfr that was utilized for advancement in platform development was the locality of the N- and C- termini. These termini point towards the exterior and interior of the protein cage, which made them ideal for genetic engineering these two surfaces. Polyhistidine amino acid sequences (His6-tags) were added to the C-termini of the Bfr protein subunits in order to provide new selective affinity interactions on the interior protein surface of this multiprotein cavity. This His6-tag affinity interaction allowed for Ni2+-dependent complexation between the host Bfr and two guest molecules modified with a nitrilotriacetic (NTA) acid. The two proof of concept guests studied in our laboratory were streptavidin binding a biotin-XNTA functionality, and a gold nanoparticle (GNP) containing NTA functionalities. This approach was designed to make both streptavidin and GNPs addressable to the interior surface of Bfr through the NTA/His6-tag affinity interaction. The purpose of these experiments was to explore various methodologies to form controllable interactions between guest molecules and internal protein cavity surfaces. Additionally, the endogenous heme molecules were exchanged for fluorescently-modified hemes as a further approach to engineer this cage protein for host-guest interactions. The information gained from the above experiments will help develop the bionanotechnology applications of Bfr

Scale bridging materials physics: Active learning workflows and integrable deep neural networks for free energy function representations in alloys

The free energy plays a fundamental role in descriptions of many systems in continuum physics. Notably, in multiphysics applications, it encodes thermodynamic coupling between different fields. It thereby gives rise to driving forces on the dynamics of interaction between the constituent phenomena. In mechano-chemically interacting materials systems, even consideration of only compositions, order parameters and strains can render the free energy to be reasonably high-dimensional. In proposing the free energy as a paradigm for scale bridging, we have previously exploited neural networks for their representation of such high-dimensional functions. Specifically, we have developed an integrable deep neural network (IDNN) that can be trained to free energy derivative data obtained from atomic scale models and statistical mechanics, then analytically integrated to recover a free energy density function. The motivation comes from the statistical mechanics formalism, in which certain free energy derivatives are accessible for control of the system, rather than the free energy itself. Our current work combines the IDNN with an active learning workflow to improve sampling of the free energy derivative data in a high-dimensional input space. Treated as input-output maps, machine learning accommodates role reversals between independent and dependent quantities as the mathematical descriptions change with scale bridging. As a prototypical system we focus on Ni-Al. Phase field simulations using the resulting IDNN representation for the free energy density of Ni-Al demonstrate that the appropriate physics of the material have been learned. To the best of our knowledge, this represents the most complete treatment of scale bridging, using the free energy for a practical materials system, that starts with electronic structure calculations and proceeds through statistical mechanics to continuum physics