Publications

We examine the interactions between actively rotating proteins moving in a membrane. Experimental evidence suggests that such rotor proteins, like the ATP synthases of the inner mitochondrial membrane, can arrange themselves into lattices. We show that crystallization is possible through a combination of hydrodynamic and repulsive interactions between the rotor proteins. In particular, hydrodynamic interactions induce rotational motion of the rotor protein assembly that, in the presence of repulsion, drives the system into a hexagonal lattice. The entire crystal rotates with an angular velocity which increases with motor density and decreases with lattice diameter - larger and sparser arrays rotate at a slower pace. The rotational interactions allow ensembles of proteins to sample configurations and reach an ordered steady state, which are inaccessible to the quenched nonrotational system. Rotational interactions thus act as a sort of temperature that removes disorder, except that actual thermal diffusion leads to expansion and loss of order. In contrast, the rotational interactions are bounded in space. Hence, once an ordered state is reached, it is maintained at all times.

Prior to the detection of black holes (BHs) via the gravitational waves (GWs) they generate at merger, the presence of BHs was inferred in X-ray binaries, mostly via dynamical measurements, with measured masses in the range between ∼5−20 M⊙
The LIGO discovery of the first BHs via GWs was surprising in that the two BHs that merged had masses of 35.6 and 28.6 M⊙
, which are both above the range inferred from X-ray binaries. With 10 binary BH detections to date, it has become apparent that, while the two distributions are not disjoint, they are most certainly distinct. In this Letter, we suggest that the reason for the apparent dichotomy is due to a predominance of different formation channels: isolated binary evolution for X-ray binaries, and dynamical exchanges in dense star clusters for the LIGO BHs. We show, via timescale arguments, that BHs in high-masss X-ray binaries are preferentially seen when they have lower mass accretors. We then perform high-resolution N-body simulations of a cluster of isolated BHs with a range of initial mass spectra, and show that BH binaries are preferentially formed by the most massive BHs, and additionally that these tend to be the tightest binaries (hence with shorter merger timescales). We also perform a simulation with neutron stars (NSs) in addition to BHs, more abundant by a factor of 5, and show that the formation of NS-BH binaries is <1%
that of BH-BH binaries, hence making the dynamical formation of NS-BH systems much less likely than that of binary BHs.

We update the capabilities of the open-knowledge software instrument Modules for Experiments in Stellar Astrophysics (MESA). RSP is a new functionality in MESAstar that models the non-linear radial stellar pulsations that characterize RR Lyrae, Cepheids, and other classes of variable stars. We significantly enhance numerical energy conservation capabilities, including during mass changes. For example, this enables calculations through the He flash that conserve energy to better than 0.001 %. To improve the modeling of rotating stars in MESA, we introduce a new approach to modifying the pressure and temperature equations of stellar structure, and a formulation of the projection effects of gravity darkening. A new scheme for tracking convective boundaries yields reliable values of the convective-core mass, and allows the natural emergence of adiabatic semiconvection regions during both core hydrogen- and helium-burning phases. We quantify the parallel performance of MESA on current generation multicore architectures and demonstrate improvements in the computational efficiency of radiative levitation. We report updates to the equation of state and nuclear reaction physics modules. We briefly discuss the current treatment of fallback in core-collapse supernova models and the thermodynamic evolution of supernova explosions. We close by discussing the new MESA Testhub software infrastructure to enhance source-code development.

I give formulas for the accuracy to which a selection function must be measured via Monte-Carlo injections in order to have un-biased population inference. The number of found injections scales linearly with the number of objects in the population; the coefficient in front of the linear term depends on both the distribution of injections and the inferred population distribution.

We present a density-matrix embedding theory (DMET) study of the one-dimensional Hubbard–Holstein model, which is paradigmatic for the interplay of electron–electron and electron–phonon interactions. Analyzing the single-particle excitation gap, we find a direct Peierls insulator to Mott insulator phase transition in the adiabatic regime of slow phonons in contrast to a rather large intervening metallic phase in the anti-adiabatic regime of fast phonons. We benchmark the DMET results for both on-site energies and excitation gaps against density-matrix renormalization group (DMRG) results and find good agreement of the resulting phase boundaries. We also compare the full quantum treatment of phonons against the standard Born–Oppenheimer (BO) approximation. The BO approximation gives qualitatively similar results to DMET in the adiabatic regime but fails entirely in the anti-adiabatic regime, where BO predicts a sharp direct transition from Mott to Peierls insulator, whereas DMET correctly shows a large intervening metallic phase. This highlights the importance of quantum fluctuations in the phononic degrees of freedom for metallicity in the one-dimensional Hubbard–Holstein model.

We present an ab initio algorithm for quantum dynamics simulations that reformulates the traditional “curse of dimensionality” that plagues all state-of-the-art techniques for solving the time-dependent Schrödinger equation. Using a stochastic wave-function ansatz that is based on a set of interacting single-particle conditional wave functions, we show that the difficulty of the problem becomes dominated by the number of trajectories needed to describe the process, rather than simply the number of degrees of freedom involved. This highly parallelizable technique achieves quantitative accuracy for situations in which mean-field theory drastically fails to capture qualitative aspects of the dynamics, such as quantum decoherence or the reduced nuclear probability density, using orders of magnitude fewer trajectories than a mean-field simulation. We illustrate the performance of this method for two fundamental nonequilibrium processes: a photoexcited proton-coupled electron transfer problem, and nonequilibrium dynamics in a cavity bound electron-photon system in the ultrastrong-coupling regime.

A monolayer of WTe2 has been shown to display quantum spin Hall (QSH) edge modes persisting up to 100 K in transport experiments. Based on density-functional theory calculations and symmetry-based model building including the role of correlations and substrate support, we develop an effective electronic model for
WTe2 that fundamentally differs from other prototypical QSH settings: we find a remarkably strong transverse localization of QSH edge modes in WTe2 related to the glide symmetry due to which the topological gap opens away from high-symmetry points in momentum space. While the indirect bulk gap is much smaller, a large direct gap of up to 1 eV in the Brillouin zone region of the dispersing edge modes determines their properties.

Cosmological surveys aim at answering fundamental questions about our Universe, including the nature of dark matter or the reason of unexpected accelerated expansion of the Universe. In order to answer these questions, two important ingredients are needed: 1) data from observations and 2) a theoretical model that allows fast comparison between observation and theory. Most of the cosmological surveys observe galaxies, which are very difficult to model theoretically due to the complicated physics involved in their formation and evolution; modeling realistic galaxies over cosmological volumes requires running computationally expensive hydrodynamic simulations that can cost millions of CPU hours. In this paper, we propose to use deep learning to establish a mapping between the 3D galaxy distribution in hydrodynamic simulations and its underlying dark matter distribution. One of the major challenges in this pursuit is the very high sparsity in the predicted galaxy distribution. To this end, we develop a two-phase convolutional neural network architecture to generate fast galaxy catalogues, and compare our results against a standard cosmological technique. We find that our proposed approach either outperforms or is competitive with traditional cosmological techniques. Compared to the common methods used in cosmology, our approach also provides a nice trade-off between time-consumption (comparable to fastest benchmark in the literature) and the quality and accuracy of the predicted simulation. In combination with current and upcoming data from cosmological observations, our method has the potential to answer fundamental questions about our Universe with the highest accuracy.

The Mott insulator Ca2RuO4 is the subject of much recent attention following reports of emergent nonequilibrium steady states driven by applied electric fields or currents. In this paper, we carry out infrared nano-imaging and optical-microscopy measurements on bulk single crystal Ca2RuO4 under conditions of steady current flow to obtain insight into the current-driven insulator-to-metal transition. We observe macroscopic growth of the current-induced metallic phase, with nucleation regions for metal and insulator phases determined by the polarity of the current flow. A remarkable metal-insulator-metal microstripe pattern is observed at the phase front separating metal and insulator phases. The microstripes have orientations tied uniquely to the crystallographic axes, implying a strong coupling of the electronic transition to lattice degrees of freedom. Theoretical modeling further illustrates the importance of the current density and confirms a submicron-thick surface metallic layer at the phase front of the bulk metallic phase. Our work confirms that the electrically induced metallic phase is nonfilamentary and is not driven by Joule heating, revealing remarkable new characteristics of electrically induced insulator-metal transitions occurring in functional correlated oxides.

It was established by Thouless, Kohmoto, Nightingale, and den Nijs in 1982 that the topology of the solid-state wavefunctions leads to quantization of transverse electrical conductivity of an insulator. This recognition has led to the development of the new field of topological materials characterized by symmetry-protected quantum numbers. Here, we propose a general and computationally efficient framework enabling one to unveil and predict materials-topological invariants in terms of physical observables, such as the bulk time-dependent current. We show how the quantized charge and spin Hall effect appears even for materials with a non-Abelian Berry phase. This dynamical approach is not necessarily restricted to density functional theory, but can be extended to other schemes and to other methods dealing with correlations explicitly.