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Polarized light microscopy reveals twin domains and symmetry of the phases in CH 3 NH 3 PbBr 3 crystal; domain structure remains unresponsive to electric field but changes under external stress, confirming ferroelasticity while ruling out ferroelectricity.
We have studied the magnetotransport properties and strain release mechanisms in ferroelastic La 0.9 Sr 0.1 MnO 3 (LSMO) epitaxial thin films on SrTiO 3 (STO)(001) substrates with different miscut angles. The substrate miscut angle plays a critical role in releasing shear strain and has a huge impact on the properties of the films. The strain relaxes by monoclinic distortion for films on low miscut substrates and for higher miscut substrates, the strain relaxation causes the formation of periodic twin domains with larger periodicities. Furthermore, we observe that the Curie temperature (T C ) decreases systematically, and magnetoresistance (MR) increases with increasing the miscut angle. Such changes in the magnetic and transport properties could be due to the increased density of phase boundaries (PBs) with the increase of miscut angle. This work provides a way to tailor film microstructures and subsequent functional properties of other complex oxide films on miscut substrates with symmetry mismatch.
Recent experimental studies show that co-sputtering solutes with Al, together, can refine columnar grain size around few tens of nanometers and promote the formation and enhance the stability of planar defects such as stacking faults (SFs) and grain boundaries (GBs) in Al alloys. These crystal defects and fine columnar grains result in high strength, enhanced strain hardening and thermal stability of Al alloys. Using first-principles density-functional theory (DFT) calculations, we studied the role of eleven solutes in tailoring kinetics and energetics of adatoms and clusters on Al {111} surface, stable and unstable stacking fault energies, and kinetic energy barriers for the migration of defects. The calculations show that most solutes can effectively refine columnar grain size by decreasing the diffusivity of adatoms and surface clusters. These solutes do not necessarily decrease the stacking fault energy of Al alloys, but reduce the formation energy of faulted surface clusters and increase the energy barriers for the recovery of faulted surface clusters. Correspondingly, the formation of SFs is kinetically promoted during sputtering. Furthermore, solutes are segregated into the core of Shockley partial dislocations and play a pinning effect on SFs, SF arrays and twin boundaries, enhancing the thermal stability of these crystal defects. These findings provide insights into the design of high-strength Al alloys for high-temperature applications.
The development of structural hierarchy on various length scales during the crystallization process is ubiquitous in biological systems and minerals and is common in synthetic nanomaterials. The driving forces for the formation of complex architectures range from local interfacial interactions, that modify interfacial speciation, local supersaturation, and nucleation barriers, to macroscopic interparticle forces. Although it is enticing to interpret the formation of hierarchical architectures as the assembly of independently nucleated building blocks, crystallization pathways often follow monomer-by-monomer addition with structural complexity arising from interfacial chemical coupling and strongly correlated fluctuation dynamics in the electric double layers. Here, we show that the development of structural hierarchy through heterogeneous nucleation is driven by dipolar and solvation forces. Specifically, coupled simulations and experimental studies revealed that dipole build-up along the slow growth direction can trigger twinning and the development of branched architectures. Enthalpic solvation interactions were shown to either enhance or reduce the dipole moment of the nanoparticles and, thereby, control crystal morphology and architecture. The systematic studies of chemical coupling between different solvents and undercoordinated surface atoms of the growing nanocrystals revealed the mechanism of dimensionality control and the development of structural hierarchy without ligands or structure-directing agents.
When two parallel coplanar dislocations with opposite Burgers vectors are brought into collision by the action of stress in solid crystals, they annihilate each other. In cholesterics with pitch p, under certain conditions, colliding dislocations with Burgers vectors b = p and b = − p are immune against annihilation and form stable twin-like pairs known as Lehmann clusters. We point out that this immunity is due to the escape of the director field into the third dimension. We show that from a topological point of view, Lehmann clusters can be seen as skyrmion tubes embedded in a helicoidal background, and their extremities have the topology of monopoles.
Phase transformations play an important role in the mechanical behavior of materials subjected to extreme loading conditions. A series of shock-reshock experiments were fielded to determine whether the phase transitions in materials are significantly enhanced or inhibited by preexisting microstructural features. Polycrystalline zirconium samples were shock loaded using gas-gun plate impact and soft recovered to examine microstructure using electron backscatter diffraction (EBSD). Drive conditions were varied to study the (hcp) alpha to (hexagonal) omega solidsolid phase transformation. Recovered samples were then subjected to a second shock loading event to determine the change in material behavior as a function of pre-shock microstructure. Crystallography of phase fragments in the final microstructure showed that prior twinning (formed during the shock to a peak stress below the transition threshold) appeared to suppress omega formation/retention after reshock. Conversely, when a material was initially shocked into the omega phase field, retained-omega was not found to have a large impact on subsequent omega formation during reshock. This suggests that nucleation and growth of omega phase are important processes, and the relative activity of nucleation vs. growth processes is modified by a pre-existing substructure. Additionally, orientation relationships reveal a reverse transformation pathway (omega to alpha) dominates the final microstructure, suggesting significant grain growth in the omega phase field is possible even for dynamic timescales.
Generating a digital twin of any complex system requires modeling and computational approaches that are efficient, accurate, and modular. Traditional reduced order modeling techniques are targeted at only the first two, but the novel nonintrusive approach we present in this study is an attempt at taking all three into account effectively compared to their traditional counterparts. Based on dimensionality reduction using proper orthogonal decomposition (POD), we introduce a long short-term memory neural network architecture together with a principal interval decomposition (PID) framework as an enabler to account for localized modal deformation. As an effective partitioning tool for breaking the Kolmogorov barrier, our PID framework, therefore, can be considered a key element in the accurate reduced order modeling of convective flows. Our applications for convection-dominated systems governed by Burgers, Navier-Stokes, and Boussinesq equations demonstrate that the proposed approach yields significantly more accurate predictions than the POD-Galerkin method and could be a key enabler toward near real-time predictions of unsteady flows.
We report the in situ , direct epitaxial synthesis of (0001)-oriented PdCoO 2 thin films on c-plane sapphire using ozone-assisted molecular-beam epitaxy. The resulting films have smoothness, structural perfection, and electrical characteristics that rival the best in situ grown PdCoO 2 thin films in the literature. Metallic conductivity is observed in PdCoO 2 films as thin as ~2.0 nm. The PdCoO 2 films contain 180° in-plane rotation twins. Scanning transmission electron microscopy reveals that the growth of PdCoO 2 on the (0001) surface of Al 2 O 3 begins with the CoO 2 layer.
PySCF is a Python-based general-purpose electronic structure platform that supports first-principles simulations of molecules and solids as well as accelerates the development of new methodology and complex computational workflows. Here, we explain the design and philosophy behind PySCF that enables it to meet these twin objectives. With several case studies, we show how users can easily implement their own methods using PySCF as a development environment. We then summarize the capabilities of PySCF for molecular and solid-state simulations. Finally, we describe the growing ecosystem of projects that use PySCF across the domains of quantum chemistry, materials science, machine learning, and quantum information science.
Reduced rank nonlinear filters are increasingly utilized in data assimilation of geophysical flows, but often require a set of ensemble forward simulations to estimate forecast covariance. On the other hand, predictor-corrector type nudging approaches are still attractive due to their simplicity of implementation when more complex methods need to be avoided. However, optimal estimate of nudging gain matrix might be cumbersome. In this paper, we put forth a fully nonintrusive recurrent neural network approach based on a long short-term memory (LSTM) embedding architecture to estimate the nudging term, which plays a role not only to force the state trajectories to the observations but also acts as a stabilizer. Furthermore, our approach relies on the power of archival data and the trained model can be retrained effectively due to power of transfer learning in any neural network applications. In order to verify the feasibility of the proposed approach, we perform twin experiments using Lorenz 96 system. Our results demonstrate that the proposed LSTM nudging approach yields more accurate estimates than both extended Kalman filter (EKF) and ensemble Kalman filter (EnKF) when only sparse observations are available. With the availability of emerging AI-friendly and modular hardware technologies and heterogeneous computing platforms, we articulate that our simplistic nudging framework turns out to be computationally more efficient than either the EKF or EnKF approaches.
Transparent conductive oxides (TCOs)—materials that have the twin desirable features of high optical transmission and electrical conductivity—play an increasingly significant role in the fields of photovoltaics and information technology. As an excellent TCO, Ta-doped anatase TiO 2 shows great promise for a wide range of applications. Here, terahertz time-domain spectroscopy is used to study the complex optical conductivity $\tilde{σ}$ (ω) of the TCO—heavily Ta-doped TiO 2 thin films with different Ta-doping concentrations, in the frequency range of 0.3–2.7 THz and the temperature range of 10–300 K. Fitting the complex optical conductivity to a Drude-like behavior allows us to extract the temperature dependence of the effective mass, which suggests the existence of many-body large polarons. Moreover, the carrier scattering rate of Ta-doped TiO 2 with different carrier concentrations agrees with the interacting polaron gas theory. Our results suggest that with increasing electron density in TiO 2 , the interaction between polarons is larger and electron–phonon coupling is smaller, which is beneficial for achieving high mobility and conductivity in TiO 2 .
Using epitaxial stabilization, we synthesized single-phase (001)-oriented thin films of DyFe 2 O 4+x on (111) MgAl 2 O 4 substrates by molecular-beam epitaxy. The metastable DyFe 2 O 4 polymorph formed is isostructural to known trigonal ferrimagnetic RFe 2 O 4 phases with space group $R\bar{3}m$, where R = Ho to Lu. The epitaxial DyFe 2 O 4 thin films have two in-plane orientation relationships: [100] DyFe 2 O 4 || [$2\bar{1}1$] MgAl 2 O 4 plus a twin variant related by a 60° in-plane rotation. DyFe 2 O 4 is not bulk stable and has never been synthesized before. Indeed, it has been predicted to be on the edge energetically of what may be possible to stabilize. The fact that the RFe 2 O 4 phase is stable for all elements leading up to dysprosium (Ho–Lu) leads us to believe that DyFe 2 O 4 could be a “remnant metastable phase,” one which, given the right thermodynamic conditions, could become the lowest free energy phase. We find that although we are able to get structurally very close to $R\bar{3}m$ DyFe 2 O 4 , the films are not stoichiometric as they have an increased c lattice parameter, indicative of extra oxygen as is sometimes seen in other RFe2O4 phases. The unintended surplus oxygen opens questions regarding what may be achievable using such tricks as epitaxial stabilization to access metastable phases and whether this indeed constitutes “remnant metastability.”
Temperature is known to affect deformation mechanisms in metallic alloys. As temperature decreases, the stacking-fault energy in many face-centered cubic (fcc) alloys decreases, resulting in a change of deformation mode from dislocation slip to deformation twinning. Such an impact of temperature can be more complex in compositionally heterogeneous microstructures that exhibit, for example, local concentration fluctuation such as that in multi-principal element alloys. In this work, we compare the dislocation behavior and mechanical properties of a fcc Cr 20 Mn 10 Fe 30 Co 30 Ni 10 high-entropy alloy at ambient and liquid-nitrogen temperatures. We find that a network of stacking faults is formed by uniformly extended dislocations at ambient temperatures with low stacking-fault energy, whereas at lower temperatures, uneven dissociation of dislocations becomes significant, which results in severe dislocation pile-ups together with their pronounced entanglement. Our findings indicate that as the stacking-fault energy decreases with decreasing temperature, the heterogeneity of the distribution of elements becomes more dominant in tuning the local variation of lattice resistance. As a result, the change in dislocation behavior at low temperatures strongly affects microstructural evolution and consequently leads to significantly more pronounced work hardening.
Here, we theoretically investigate the two-photon absorption signals of a three-band (g, e, f) system diagonally coupled to an over-damped Brownian oscillator bath, which induces random Gaussian modulations of energy levels with an arbitrary degree of correlation. For fast modulation, extra 2ω eg and 2ω fe peaks may obscure the g–f transitions in the classical two-photon absorption (CTPA) spectra for nearly resonant e states. These peaks arise from one-photon resonant g–e or e–f transitions. In the slow modulation limit, these peaks vanish because of the short tails of the Gaussian line shape. CTPA strongly depends on the correlations between energy fluctuations. In entangled two-photon absorption, the extra peaks are eliminated because of the broad one-photon but narrow two-photon spectrum of the twin photons. The variation of the coherences between f states with the correlation between energy fluctuations is explored.
Physics-based models have been mainstream in fluid dynamics for developing predictive models. In recent years, machine learning has offered a renaissance to the fluid community due to the rapid developments in data science, processing units, neural network based technologies, and sensor adaptations. So far in many applications in fluid dynamics, machine learning approaches have been mostly focused on a standard process that requires centralizing the training data on a designated machine or in a data center. In this article, we present a federated machine learning approach that enables localized clients to collaboratively learn an aggregated and shared predictive model while keeping all the training data on each edge device. We demonstrate the feasibility and prospects of such a decentralized learning approach with an effort to forge a deep learning surrogate model for reconstructing spatiotemporal fields. Our results indicate that federated machine learning might be a viable tool for designing highly accurate predictive decentralized digital twins relevant to fluid dynamics.
A combination of high strength and high ductility has been observed in multi-principal element alloys due to twin formation attributed to low stacking fault energy (SFE). In the pursuit of low SFE alloys, a key bottleneck is the lack of understanding of the composition–SFE correlations that would guide tailoring SFE via alloy composition. Using density functional theory (DFT), we show that dopant radius, which have been postulated as a key descriptor for SFE in dilute alloys, does not fully explain SFE trends across different host metals. Instead, charge density is a much more central descriptor. It allows us to (1) explain contrasting SFE trends in Ni and Cu host metals due to various dopants in dilute concentrations, (2) explain the large SFE variations observed in the literature even within a given alloy composition due to the nearest neighbor environments in “model” concentrated alloys, and (3) develop a machine learning model that can be used to predict SFEs in multi-elemental alloys. Further, this model opens a possibility to use charge density as a descriptor for predicting SFE in alloys.
Beta-phase gallium oxide (β-Ga 2 O 3 ) has attracted attention in recent years as a potentially low cost, large area substrate and active layer material for high power, high temperature power electronics and sensing devices. However, growth of β-Ga 2 O 3 crystals is complicated by easily activated (100) and (001) cleavage planes, the presence of low angle grain boundaries (LAGBs) and twins, and the potential formation of polycrystalline grains. In this study, β-Ga 2 O 3 crystals were grown by the edge-defined film-fed growth technique with an (010) principal face. Two crystals with apparently randomly formed high angle grain boundaries (HAGBs) were selected and analyzed by electron backscatter diffraction, electron channeling contrast imaging, and cathodoluminescence to investigate the nature of the LAGBs and the source of the HAGB formation. It was discovered that planar LAGBs lying parallel to the (010) plane exist in the region immediately preceding the start of an HAGB. Increased misorientation across the LAGB was observed, approaching the initiation of a new grain. We present multimodal microscopy characterization, correlating misorientation and variation in optoelectronic properties with LAGBs and the associated dislocations.
Microresonator Kerr optical frequency combs currently constitute a well-established research area in integrated, nonlinear, and quantum photonics. Furthermore, these systems have found a plethora of technological applications, while serving as an excellent platform to investigate fundamental scientific topics such as light–matter interactions, pattern formation in driven-dissipative systems, or entangled twin-photon generation. We here provide a brief overview of the topic, highlight some of the most recent advances, and discuss a few of the main challenges ahead in this field.