Linking electronic structure calculations to generalized stacking fault energies in multicomponent alloys

The generalized stacking fault energy is a key ingredient to mesoscale models of dislocations. Here we develop an approach to quantify the dependence of generalized stacking fault energies on the degree of chemical disorder in multicomponent alloys. We introduce the notion of a "configurationally-resolved planar fault" (CRPF) energy and extend the cluster expansion method from alloy theory to express the CRPF as a function of chemical occupation variables of sites surrounding the fault. We apply the approach to explore the composition and temperature dependence of the unstable stacking fault energy (USF) in binary Mo-Nb alloys. First-principles calculations are used to parameterize a formation energy and CRPF cluster expansion. Monte Carlo simulations show that the distribution of USF energies is significantly affected by chemical composition and temperature. The formalism can be applied to any multicomponent alloy and will enable the development of rigorous models for deformation mechanisms in high-entropy alloys

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

Ring Formation from an Oscillating Black Hole

Massive black hole (BH) mergers can result in the merger remnant receiving a "kick", of order 200 km s$^{-1}$ or more, which will cause the remnant to oscillate about the galaxy centre. Here we analyze the case where the BH oscillates through the galaxy centre perpendicular or parallel to the plane of the galaxy for a model galaxy consisting of an exponential disk, a Plummer model bulge, and an isothermal dark matter halo. For the perpendicular motion we find that there is a strong resonant forcing of the disk radial motion near but somewhat less than the "resonant radii" $r_R$ where the BH oscillation frequency is equal one-half, one-fourth, (1/6, etc.) of the radial epicyclic frequency in the plane of the disk. Near the resonant radii there can be a strong enhancement of the radial flow and disk density which can lead to shock formation. In turn the shock may trigger the formation of a ring of stars near $r_R$. As an example, for a BH mass of $10^8 M_\odot$ and a kick velocity of 150 km s$^{-1}$, we find that the resonant radii lie between 0.2 and 1 kpc. For BH motion parallel to the plane of the galaxy we find that the BH leaves behind it a supersonic wake where star formation may be triggered. The shape of the wake is calculated as well as the slow-down time of the BH. The differential rotation of the disk stretches the wake into ring-like segments.Comment: 7 pages, 7 figure

Constraints on the shapes of galaxy dark matter haloes from weak gravitational lensing

We study the shapes of galaxy dark matter haloes by measuring the anisotropy of the weak gravitational lensing signal around galaxies in the second Red-sequence Cluster Survey (RCS2). We determine the average shear anisotropy within the virial radius for three lens samples: all galaxies with 19<m_r'<21.5, and the `red' and `blue' samples, whose lensing signals are dominated by massive low-redshift early-type and late-type galaxies, respectively. To study the environmental dependence of the lensing signal, we separate each lens sample into an isolated and clustered part and analyse them separately. We also measure the azimuthal dependence of the distribution of physically associated galaxies around the lens samples. We find that these satellites preferentially reside near the major axis of the lenses, and constrain the angle between the major axis of the lens and the average location of the satellites to =43.7 deg +/- 0.3 deg for the `all' lenses, =41.7 deg +/- 0.5 deg for the `red' lenses and =42.0 deg +/- 1.4 deg for the `blue' lenses. For the `all' sample, we find that the anisotropy of the galaxy-mass cross-correlation function =0.23 +/- 0.12, providing weak support for the view that the average galaxy is embedded in, and preferentially aligned with, a triaxial dark matter halo. Assuming an elliptical Navarro-Frenk-White (NFW) profile, we find that the ratio of the dark matter halo ellipticity and the galaxy ellipticity f_h=e_h/e_g=1.50+1.03-1.01, which for a mean lens ellipticity of 0.25 corresponds to a projected halo ellipticity of e_h=0.38+0.26-0.25 if the halo and the lens are perfectly aligned. For isolated galaxies of the `all' sample, the average shear anisotropy increases to =0.51+0.26-0.25 and f_h=4.73+2.17-2.05, whilst for clustered galaxies the signal is consistent with zero. (abridged)Comment: 28 pages, 23 figues, accepted for publication in A&

Leucine zipper transcription factor-like 1 binds adaptor protein complex-1 and 2 and participates in trafficking of transferrin receptor 1.

LZTFL1 participates in immune synapse formation, ciliogenesis, and the localization of ciliary proteins, and knockout of LZTFL1 induces abnormal distribution of heterotetrameric adaptor protein complex-1 (AP-1) in the Lztfl1-knockout mouse photoreceptor cells, suggesting that LZTFL1 is involved in intracellular transport. Here, we demonstrate that in vitro LZTFL1 directly binds to AP-1 and AP-2 and coimmunoprecipitates AP-1 and AP-2 from cell lysates. DxxFxxLxxxR motif of LZTFL1 is essential for these bindings, suggesting LZTFL1 has roles in AP-1 and AP-2-mediated protein trafficking. Since AP-1 and AP-2 are known to be involved in transferrin receptor 1 (TfR1) trafficking, the effect of LZTFL1 on TfR1 recycling was analyzed. TfR1, AP-1 and LZTFL1 from cell lysates could be coimmunoprecipitated. However, pull-down results indicate there is no direct interaction between TfR1 and LZTFL1, suggesting that LZTFL1 interaction with TfR1 is indirect through AP-1. We report the colocalization of LZTFL1 and AP-1, AP-1 and TfR1 as well as LZTFL1 and TfR1 in the perinuclear region (PNR) and the cytoplasm, suggesting a potential complex between LZTFL1, AP-1 and TfR1. The results from the disruption of adaptin recruitment with brefeldin A treatment suggested ADP-ribosylation factor-dependent localization of LZFL1 and AP-1 in the PNR. Knockdown of AP-1 reduces the level of LZTFL1 in the PNR, suggesting that AP-1 plays a role in LZTFL1 trafficking. Knockout of LZTFL1 reduces the cell surface level and the rate of internalization of TfR1, leading to a decrease of transferrin uptake, efflux, and internalization. However, knockout of LZTFL1 did not affect the cell surface levels of epidermal growth factor receptor and cation-independent mannose 6-phosphate receptor, indicating that LZTFL1 specifically regulates the cell surface level of TfR1. These data support a novel role of LZTFL1 in regulating the cell surface TfR1 level by interacting with AP-1 and AP-2

Machine-learning the configurational energy of multicomponent crystalline solids

Machine learning tools such as neural networks and Gaussian process regression are increasingly being implemented in the development of atomistic potentials. Here, we develop a formalism to leverage such non-linear interpolation tools in describing properties dependent on occupation degrees of freedom in multicomponent solids. Symmetry-adapted cluster functions are used to differentiate distinct local orderings. These local features are used as input to neural networks that reproduce local properties such as the site energy. We apply the technique to reproduce a synthetic cluster expansion Hamiltonian with multi-body interactions, as well as the formation energies calculated from first-principles for the intercalation of lithium into TiS2. The formalism and results presented here show that complex multi-body interactions may be approximated by non-linear models involving smaller clusters