Condensed Matter

The sub-bandgap absorption and ultrafast relaxation in a GaAs/ErAs/GaAs heterostructure are reported. The infrared absorption and 1550 nm-excited ultrafast photo-response are studied by Fourier transform infrared (FTIR) spectrometry and time-domain pump-probe technique. The two absorption peaks located at 2.0 um (0.62 eV) and 2.7 um (0.45 eV) are originated from the ErAs/GaAs interfacial Schottky states and sub-bandgap transition within GaAs, respectively. The photo-induced carrier lifetime, excited using 1550 nm light, is measured to be as low as 190 fs for the GaAs/ErAs/GaAs heterostructure, making it a promising material for 1550-nm-technology-compatible, high critical-breakdown-field THz devices. The relaxation mechanism is proposed and the functionality of ErAs is revealed.

Liquid marbles refer to liquid droplets that are covered with a layer of non-wetting particles. They are observed in nature and have practical significance. However, a generalized framework for analyzing liquid marbles as they inflate or deflate is unavailable. The present study fills this gap by developing an analytical framework based on liquid-particle and particle-particle interactions. We demonstrate that the potential final states of evaporating liquid marbles are characterized by one of the following: (I) constant surface area, (II) particle ejection, or (III) multilayering. Based on these insights, a single-parameter evaporation model for liquid marbles is developed. Model predictions are in excellent agreement with experimental evaporation data for water liquid marbles of particle sizes ranging from 7 nanometers to 300 micrometers (over four orders of magnitude) and chemical compositions ranging from hydrophilic to superhydrophobic. These findings lay the groundwork for the rational design of liquid marble applications.

The halide perovskites have truly emerged as efficient optoelectronic materials and show the promise of exhibiting nontrivial topological phases. Since the bandgap is the deterministic factor for these quantum phases, here we present a comprehensive electronic structure study using first-principle methods by considering nine inorganic halide perovskites CsBX 3 (B = Ge, Sn, Pb; X = Cl, Br, I) in their three structural polymorphs (cubic, tetragonal and orthorhombic). A series of exchange-correlations (XC) functionals are examined towards accurate estimation of the bandgap. Furthermore, while thirteen orbitals are active in constructing the valence and conduction band spectrum, here we establish that a four orbital based minimal basis set is sufficient to build the Slater-Koster tight-binding model (SK-TB), which is capable of reproducing the bulk and surface electronic structure in the vicinity of the Fermi level. Therefore, like the Wannier based TB model, the presented SK-TB model can also be considered as an efficient tool to examine the bulk and surface electronic structure of halide family of compounds. As estimated by comparing the model study and DFT band structure, the dominant electron coupling strengths are found to be nearly independent of XC functionals, which further establishes the utility of the SK-TB model.

Single molecule science and techniques have received increasing attention in recent years. A very interesting subject in this field is "single-molecule mixture", which contains a mixture of molecules that have molecularly different structures, that surprisingly remain unexplored both theoretically and experimentally. A major barrier to investigate single-molecule mixture lies in how to generate a structural space that contain sufficient huge number of chemical structures to enable single-molecule mixture to exist in macroscopic quantities and how to efficiently prepare it. In this article, a theoretical approach that combined model construction, thought experiment, and mathematical analysis was developed to study this elusive form of molecules. A possible route for the preparation of single molecule mixture was also provided.

We develop a continuum model for the dynamics of grain boundaries in three dimensions that incorporates the motion and reaction of the constituent dislocations. The continuum model is based on a simple representation of densities of curved dislocations on the grain boundary. Illposedness due to nonconvexity of the total energy is fixed by a numerical treatment based on a projection method that maintains the connectivity of the constituent dislocations. An efficient simulation method is developed, in which the critical but computationally expensive long-range interaction of dislocations is replaced by another projection formulation that maintains the constraint of equilibrium of the dislocation structure described by the Frank's formula. This continuum model is able to describe the grain boundary motion and grain rotation due to both coupling and sliding effects, to which the classical motion by mean curvature model does not apply. Comparisons with atomistic simulation results show that our continuum model is able to give excellent predictions of evolutions of low angle grain boundaries and their dislocation structures.

Accurate prediction of the stability of molecular crystals is a longstanding challenge, as often minuscule free energy differences between polymorphs are sensitively affected by the description of electronic structure, the statistical mechanics of the nuclei and the cell, and thermal expansion. The importance of these effects has been individually established, but rigorous free energy calculations, which simultaneously account for all terms, have been prevented by prohibitive computational costs. Here we reproduce the experimental stabilities of polymorphs of three prototypical compounds -- benzene, glycine, and succinic acid -- by computing rigorous ab initio Gibbs free energies, at a fraction of the cost of conventional harmonic approximations. This is achieved by a bottom-up approach, which involves generating machine-learning potentials to calculate surrogate free energies and subsequently calculating true ab initio free energies using inexpensive free energy perturbations. Accounting for all relevant physical effects is no longer a daunting task and provides the foundation for reliable structure predictions for more complex systems of industrial importance.

One of the most important aims of grain boundary modeling is to predict the evolution of a large collection of grains in phenomena such as abnormal grain growth, coupled grain boundary motion, and recrystallization that occur under extreme thermomechanical loads. A unified framework to study the coevolution of grain boundaries with bulk plasticity has recently been developed by Admal et al. (2018), which is based on modeling grain boundaries as continuum dislocations governed by an energy based on the Kobayashi--Warren--Carter (KWC) model (Kobayashi et al., 1998, 2000). While the resulting unified model demonstrates coupled grain boundary motion and polygonization (seen in recrystallization), it is restricted to grain boundary energies of the Read--Shockley type, which applies only to small misorientation angles. In addition, the implementation of the unified model using finite elements inherits the computational challenges of the KWC model that originate from the singular diffusive nature of its governing equations. The main goal of this study is to generalize the KWC functional to grain boundary energies beyond the Read--Shockley-type that respect the bicrystallography of grain boundaries. The computational challenges of the KWC model are addressed by developing a thresholding method that relies on a primal dual algorithm and the fast marching method, resulting in an O(NlogN) algorithm, where N is the number of grid points. We validate the model by demonstrating the Herring angle relation, followed by a study of the grain microstructure evolution in a two-dimensional face-centered cubic copper polycrystal with crystal symmetry-invariant grain boundary energy data obtained from the lattice matching method of Runnels et al. (2016a,b).

In the weak-coupling Peierls' view, charge density wave (CDW) transitions are metal-insulator transitions, creating a gap at the Fermi level. However, with strong electron-phonon coupling, theoretically the effects of the periodic lattice distortion could be spread throughout the electronic structure and give rise to CDW gaps away from the Fermi level. Here, using scanning tunneling microscopy and spectroscopy, we present experimental evidence of a full CDW gap residing in the unoccupied states of monolayer VS2. Our ab initio calculations show anharmonic coupling of transverse and longitudinal phonons to be essential for the formation of the CDW and the full gap above the Fermi level. The CDW induces a Lifshitz transition, i.e., a topological metal-metal instead of a Peierls metal-insulator transition. Additionally, x-ray magnetic circular dichroism reveals the absence of net magnetization in this phase, pointing to a coupled CDW-antiferromagnetic ground state.

The future of space exploration missions will rely on technologies increasing their endurance and self-sufficiency, including for manufacturing objects on-demand. We propose a process for handling and additively manufacturing powders that functions independently of the gravitational environment and with no restriction on feedstock powder flowability. Based on a specific sequence of boundary loads applied to the granular packing, powder is transported to the printing zone, homogenized and put under compression to increase the density of the final part. The powder deposition process is validated by simulations that show the homogeneity and density of deposition to be insensitive to gravity and cohesion forces within the DEM model. We further provide an experimental proof of concept of the process by successfully 3D printing parts on-ground and in weightlessness, on parabolic flight. Powders exhibiting high and low flowability are used as model feedstock material to demonstrate the versatility of the process, opening the way for additive manufacturing of recycled material.

The macroscopic properties of materials that we observe and exploit in engineering application result from complex interactions between physics at multiple length and time scales: electronic, atomistic, defects, domains etc. Multiscale modeling seeks to understand these interactions by exploiting the inherent hierarchy where the behavior at a coarser scale regulates and averages the behavior at a finer scale. This requires the repeated solution of computationally expensive finer-scale models, and often a priori knowledge of those aspects of the finer-scale behavior that affect the coarser scale (order parameters, state variables, descriptors, etc.). We address this challenge in a two-scale setting where we learn the fine-scale behavior from off-line calculations and then use the learnt behavior directly in coarse scale calculations. The approach draws from recent successes of deep neural networks, in combination with ideas from model reduction. The approach builds on the recent success of deep neural networks by combining their approximation power in high dimensions with ideas from model reduction. It results in a neural network approximation that has high fidelity, is computationally inexpensive, is independent of the need for a priori knowledge, and can be used directly in the coarse scale calculations. We demonstrate the approach on problems involving the impact of magnesium, a promising light-weight structural and protective material.