Stress and Curvature Effects in Layered 2D Ferroelectric CuInP2S6.

Nanoscale ferroelectric 2D materials offer the opportunity to investigate curvature and strain effects on materials functionalities. Among these, CuInP2S6 (CIPS) has attracted tremendous research interest in recent years due to combination of room temperature ferroelectricity, scalability to a few layers thickness, and ferrielectric properties due to coexistence of 2 polar sublattices. Here, we explore the local curvature and strain effect on polarization in CIPS via piezoresponse force microscopy and spectroscopy. To explain the observed behaviors and decouple the curvature and strain effects in 2D CIPS, we introduce the finite element Landau-Ginzburg-Devonshire model, revealing strong changes in hysteresis characteristics in regions subjected to tensile and compressive strain. The piezoresponse force microscopy (PFM) results show that bending induces ferrielectric domains in CIPS, and the polarization-voltage hysteresis loops differ in bending and nonbending regions. These studies offer insights into the fabrication of curvature-engineered nanoelectronic devices.

Design of high polarization low switching barrier hybrid improper ferroelectric perovskite oxide superlattices.

Hybrid improper ferroelectricity is a useful tool to design ABO3/A'BO3 polar superlattices from non polar building blocks. In this study, we have designed high polarization-low switching barrier hybrid improper ferroelectric superlattices with efficient polarization, and polarization-magnetization switching properties above room temperature, using density functional theory and ab initio molecular dynamics simulations. Superlattices with a chemical formula of (AAlO3)m/(A'AlO3)n, where m/n = 1/1, 1/3, 3/1, 1/5 and 5/1, A, A' = Lanthanide and Y cations are considered to outline the design principles behind polarization switching and (LaFeO3)3/(CeFeO3)1 is investigated for polarization-magnetization switching. We find that the unconventional switching paths via out-of-phase rotation QR- (a0a0c-) and tilt precession QTP always yield lower switching barrier compared to those via in-phase rotation QR+ (a0a0c+) and tilt QT (a-a-c0) of BO6 octahedra. Results from ab initio molecular dynamics simulations estimate the temperature at which the lowest energy barrier can be overcome. It is possible to tune the polarization switching barrier by tuning the tolerance factor, A,A' cation radius mismatch and super lattice periodicity. For switching via QR-, the switching barrier varies exponentially with rotation angle, indicating how high switching barrier is expected for systems, away from cubic symmetry. We provide a recipe to overcome such a bottleneck by tuning superlattice periodicity. Finally, we have proposed the multiferroic device application concept through a proposed polarization-temperature hysteresis loop and magnetization switching.

Probing Electron Beam Induced Transformations on a Single-Defect Level via Automated Scanning Transmission Electron Microscopy.

A robust approach for real-time analysis of the scanning transmission electron microscopy (STEM) data streams, based on ensemble learning and iterative training (ELIT) of deep convolutional neural networks, is implemented on an operational microscope, enabling the exploration of the dynamics of specific atomic configurations under electron beam irradiation via an automated experiment in STEM. Combined with beam control, this approach allows studying beam effects on selected atomic groups and chemical bonds in a fully automated mode. Here, we demonstrate atomically precise engineering of single vacancy lines in transition metal dichalcogenides and the creation and identification of topological defects in graphene. The ELIT-based approach facilitates direct on-the-fly analysis of the STEM data and engenders real-time feedback schemes for probing electron beam chemistry, atomic manipulation, and atom by atom assembly.

Exploring electron beam induced atomic assembly via reinforcement learning in a molecular dynamics environment.

Atom-by-atom assembly of functional materials and devices is perceived as one of the ultimate targets of nanotechnology. Recently it has been shown that the beam of a scanning transmission electron microscope can be used for targeted manipulation of individual atoms. However, the process is highly dynamic in nature rendering control difficult. One possible solution is to instead train artificial agents to perform the atomic manipulation in an automated manner without need for human intervention. As a first step to realizing this goal, we explore how artificial agents can be trained for atomic manipulation in a simplified molecular dynamics environment of graphene with Si dopants, using reinforcement learning. We find that it is possible to engineer the reward function of the agent in such a way as to encourage formation of local clusters of dopants under different constraints. This study shows the potential for reinforcement learning in nanoscale fabrication, and crucially, that the dynamics learned by agents encode specific elements of important physics that can be learned.

Automated and Autonomous Experiments in Electron and Scanning Probe Microscopy.

Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics to self-driving cars to organic and inorganic synthesis are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiments (AE) in imaging. Here, we aim to analyze the major pathways toward AE in imaging methods with sequential image formation mechanisms, focusing on scanning probe microscopy (SPM) and (scanning) transmission electron microscopy ((S)TEM). We argue that automated experiments should necessarily be discussed in a broader context of the general domain knowledge that both informs the experiment and is increased as the result of the experiment. As such, this analysis should explore the human and ML/AI roles prior to and during the experiment and consider the latencies, biases, and prior knowledge of the decision-making process. Similarly, such discussion should include the limitations of the existing imaging systems, including intrinsic latencies, non-idealities, and drifts comprising both correctable and stochastic components. We further pose that the role of the AE in microscopy is not the exclusion of human operators (as is the case for autonomous driving), but rather automation of routine operations such as microscope tuning, etc., prior to the experiment, and conversion of low latency decision making processes on the time scale spanning from image acquisition to human-level high-order experiment planning. Overall, we argue that ML/AI can dramatically alter the (S)TEM and SPM fields; however, this process is likely to be highly nontrivial and initiated by combined human-ML workflows and will bring challenges both from the microscope and ML/AI sides. At the same time, these methods will enable opportunities and paradigms for scientific discovery and nanostructure fabrication.

Surface structure and energetics of low index facets of bismuth ferrite.

Bismuth ferrite (BiFeO3) is a multiferroic material that has received significant interest due to its functional properties which could lead to potential novel applications in microelectronics, spintronics, and controlled catalytic reactions. Here, we provide the results of an extensive theoretical study to understand the surface structure and describe the energetics of differently terminated BiFeO3 surfaces. We specifically evaluate low index crystal facets and surface level atomic terminations via density functional theory and ab initio thermodynamics techniques. Our findings indicate that surface stability with varying terminations is strongly dependent on the oxygen partial pressure and chemical potentials of bismuth and iron. In oxygen rich environments, the results suggest that (100)-O and (110)-O and terminated surfaces are more stable compared to other surface terminations and facets. On the other hand, in a relatively oxygen poor environments, we observe that (110)-Bi and (110)-Fe are more stable. The calculations also show that the majority of BFO surfaces exhibit metallic behavior with the exception of the O-terminated (100) and (110) surfaces.

Electronic and Magnetic Properties of Lanthanum and Strontium Doped Bismuth Ferrite: A First-Principles Study.

While bismuth ferrite BiFeO3 (BFO) is a well studied multiferroic material, its electronic and magnetic properties in the presence of A-site dopants have not been explored widely. Here we report the results of a systematic study of the local electronic structure, spontaneous polarization, and magnetic properties of lanthanum (La) and strontium (Sr) doped rhombohedral bismuth ferrite within density functional theory. An enhanced ferroelectric polarization of 122.43 µC/cm2 is predicted in the uniformly doped BiLaFe2O6. We find that substitution of Sr in the A-site drives the system into a metallic state. The nature of magnetism arises mainly from the B-site Fe exhibiting a G-type antiferromagnetic ordering. Our study finds that upon dopant substitution, the local magnetic moment is decreased and its magnitude is dependent on the distance between the Fe and the dopant atom. The correlation between the local moment and the distance between the Fe and the dopant atom is discussed.

First-principles studies of spontaneous polarization in mixed poly(vinylidene fluoride)/2,3,3,3-tetrafluoropropene polymer crystals.

Spontaneous polarization P of mixed polymer crystals based on ß poly(vinylidene fluoride) (PVDF, -CH2-CF2-) and 2,3,3,3-tetrafluoropropene (TFP, -CH2-CF(CF3)-) was evaluated for ß-PVDF/iso-PTFP, ß-PVDF/P(VDF-alt-iso-TFP) and ß-PVDF/syndio-PTFP. A plane-wave-based density-functional theory (DFT) approach, combined with the Modern Theory of Polarization formalism utilizing maximally-localized Wannier functions for calculating P, indicates that all systems exhibit similarly high or even slightly larger polarization than that of perfectly crystalline ß-PVDF (0.18 C m-2). These properties stem from the substantial dipole moment of the TFP unit, which is estimated to be ∼2.3 D in an isolated chain, but is enhanced to ∼2.8 D in the crystal.