Unraveling the Correlation between the Interface Structures and Tunable Magnetic Properties of LaSrCoO/LaSrMnO Bilayers Using Deep Learning Models.

Perovskite oxides are gaining significant attention for use in next-generation magnetic and ferroelectric devices due to their exceptional charge transport properties and the opportunity to tune the charge, spin, lattice, and orbital degrees of freedom. Interfaces between perovskite oxides, exemplified by LaSrCoO/LaSrMnO (LSCO/LSMO) bilayers, exhibit unconventional magnetic exchange switching behavior, offering a pathway for innovative designs in perovskite oxide-based devices. However, the precise atomic-level stoichiometric compositions and chemophysical properties of these interfaces remain elusive, hindering the establishment of surrogate design principles.

Observation of a single protein by ultrafast X-ray diffraction.

The idea of using ultrashort X-ray pulses to obtain images of single proteins frozen in time has fascinated and inspired many. It was one of the arguments for building X-ray free-electron lasers. According to theory, the extremely intense pulses provide sufficient signal to dispense with using crystals as an amplifier, and the ultrashort pulse duration permits capturing the diffraction data before the sample inevitably explodes.

Graph-EAM: An Interpretable and Efficient Graph Neural Network Potential Framework.

The development of deep learning interatomic potentials has enabled efficient and accurate computations in quantum chemistry and materials science, circumventing computationally expensive calculations. However, the huge number of learnable parameters in deep learning models and their complex architectures hinder physical interpretability and affect the robustness of the derived potential. In this work, we propose graph-EAM, a lightweight graph neural network (GNN) inspired by the empirical embedded atom method to model the interatomic potential of single-element structures.

Time-Resolved Single-Particle X-ray Scattering Reveals Electron-Density Gradients As Coherent Plasmonic-Nanoparticle-Oscillation Source.

Dynamics of optically excited plasmonic nanoparticles are presently understood as a series of scattering events involving the initiation of nanoparticle breathing oscillations. According to established models, these are caused by statistical heat transfer from thermalized electrons to the lattice. An additional contribution by hot-electron pressure accounts for phase mismatches between theory and experimental observations.

Accurate parameterization of the kinetic energy functional.

The absence of a reliable formulation of the kinetic energy density functional has hindered the development of orbital free density functional theory. Using the data-aided learning paradigm, we propose a simple prescription to accurately model the kinetic energy density of any system. Our method relies on a dictionary of functional forms for local and nonlocal contributions, which have been proposed in the literature, and the appropriate coefficients are calculated via a linear regression framework.

Accurate parameterization of the kinetic energy functional for calculations using exact-exchange.

Electronic structure calculations based on Kohn-Sham density functional theory (KSDFT) that incorporate exact-exchange or hybrid functionals are associated with a large computational expense, a consequence of the inherent cubic scaling bottleneck and large associated prefactor, which limits the length and time scales that can be accessed. Although orbital-free density functional theory (OFDFT) calculations scale linearly with system size and are associated with a significantly smaller prefactor, they are limited by the absence of accurate density-dependent kinetic energy functionals. Therefore, the development of accurate density-dependent kinetic energy functionals is important for OFDFT calculations of large realistic systems.

Optimizing the geometry of aerodynamic lens injectors for single-particle coherent diffractive imaging of gold nanoparticles.

Single-particle X-ray diffractive imaging (SPI) of small (bio-)nanoparticles (NPs) requires optimized injectors to collect sufficient diffraction patterns to allow for the reconstruction of the NP structure with high resolution. Typically, aerodynamic lens-stack injectors are used for NP injection. However, current injectors were developed for larger NPs (>100 nm), and their ability to generate high-density NP beams suffers with decreasing NP size.

Quantized Grain Boundary States Promote Nanoparticle Alignment During Imperfect Oriented Attachment.

Oriented attachment (OA) has become a well-recognized mechanism for the growth of metal, ceramic, and biomineral crystals. While many computational and experimental studies of OA have shown that particles can attach with some misorientation then rotate to remove adjoining grain boundaries, the underlying atomistic pathways for this "imperfect OA" process remain the subject of debate. In this study, molecular dynamics and in situ transmission electron microscopy (TEM) are used to probe the crystallographic evolution of up to 30 gold nanoparticles during aggregation.

Controlled beams of shock-frozen, isolated, biological and artificial nanoparticles.

X-ray free-electron lasers promise diffractive imaging of single molecules and nanoparticles with atomic spatial resolution. This relies on the averaging of millions of diffraction patterns of identical particles, which should ideally be isolated in the gas phase and preserved in their native structure. Here, we demonstrated that polystyrene nanospheres and granulovirus can be transferred into the gas phase, isolated, and very quickly shock-frozen, i.

Light-sheet imaging for the recording of transverse absolute density distributions of gas-phase particle-beams from nanoparticle injectors.

Imaging biological molecules in the gas-phase requires novel sample delivery methods, which generally have to be characterized and optimized to produce high-density particle beams. A non-destructive characterization method of the transverse particle beam profile is presented. It enables the characterization of the particle beam in parallel to the collection of, for instance, x-ray-diffraction patterns.

Representing local atomic environment using descriptors based on local correlations.

Statistical learning of material properties is an emerging topic of research and has been tremendously successful in areas such as representing complex energy landscapes as well as in technologically relevant areas, like identification of better catalysts and electronic materials. However, analysis of large data sets to efficiently learn characteristic features of a complex energy landscape, for example, depends on the ability of descriptors to effectively screen different local atomic environments. Thus, discovering appropriate descriptors of bulk or defect properties and the functional dependence of such properties on these descriptors remains a difficult and tedious process.

The thermodynamics of a liquid-solid interface at extreme conditions: A model close-packed system up to 100 GPa.

The first experimental insight into the nature of the liquid-solid interface occurred with the pioneering experiments of Turnbull, which simultaneously demonstrated both that metals could be deeply undercooled (and therefore had relatively large barriers to nucleation) and that the inferred interfacial free energy was linearly proportional to the enthalpy of fusion [D. Turnbull, J. Appl.

Rapid sample delivery for megahertz serial crystallography at X-ray FELs.

Liquid microjets are a common means of delivering protein crystals to the focus of X-ray free-electron lasers (FELs) for serial femtosecond crystallography measurements. The high X-ray intensity in the focus initiates an explosion of the microjet and sample. With the advent of X-ray FELs with megahertz rates, the typical velocities of these jets must be increased significantly in order to replenish the damaged material in time for the subsequent measurement with the next X-ray pulse.

Predicting phase behavior of grain boundaries with evolutionary search and machine learning.

The study of grain boundary phase transitions is an emerging field until recently dominated by experiments. The major bottleneck in the exploration of this phenomenon with atomistic modeling has been the lack of a robust computational tool that can predict interface structure. Here we develop a computational tool based on evolutionary algorithms that performs efficient grand-canonical grain boundary structure search and we design a clustering analysis that automatically identifies different grain boundary phases.

Descriptor-Based Approach for the Prediction of Cation Vacancy Formation Energies and Transition Levels.

Point defects largely determine the observed optical and electrical properties of a given material, yet the characterization and identification of defects has remained a slow and tedious process, both experimentally and theoretically. We demonstrate a computationally-cheap model that can reliably predict the formation energies of cation vacancies as well as the location of their electronic states in a large set of II-VI and III-V materials using only parameters obtained from the bulk primitive unit cell (2-4 atoms). We apply our model to ordered alloys within the CdZnSeTe, CdZnS, and ZnMgO systems and predict the positions of cation vacancy charge-state transition levels with a mean absolute error of < 0.

Antagonistic Interplay Between an Intermolecular CH···O and an Intramolecular OH···O Hydrogen Bond in a 1:1 Complex Between 1,2-Cyclohexanedione and Chloroform: A Combined Matrix Isolation Infrared and Quantum Chemistry Study.

Matrix isolation infrared spectra of a weak C-H···O hydrogen-bonded complex between the keto-enol form of 1,2-cyclohexanedione (HCHD) and chloroform have been measured. The spectra reveal that the intramolecular O-H···O H-bond of HCHD is weakened as a result of complex formation, manifesting in prominent blue shift (∼23 cm) of the ν band and red shifts (∼7 cm) of ν bands of the acceptor (HCHD). The ν band of donor CHCl undergoes a large red shift of ∼33 cm.

Vibrational Predissociation of the HCl-(HO) Tetramer.

The vibrational predissociation of the HCl-(HO) tetramer, the largest HCl-(HO) cluster for which HCl is not predicted to be ionized, is reported. This work focuses on the predissociation pathway giving rise to HO + HCl-(HO) following IR laser excitation of the H-bonded OH stretch fundamental. HO fragments are monitored state selectively by 2 + 1 resonance-enhanced multiphoton ionization (REMPI) combined with time-of-flight mass spectrometry (TOF-MS).

Exploring the free energy surface using ab initio molecular dynamics.

Efficient exploration of configuration space and identification of metastable structures in condensed phase systems are challenging from both computational and algorithmic perspectives. In this regard, schemes that utilize a set of pre-defined order parameters to sample the relevant parts of the configuration space [L. Maragliano and E.

Energetics and Predissociation Dynamics of Small Water, HCl, and Mixed HCl-Water Clusters.

This Review summarizes recent research on vibrational predissociation (VP) of hydrogen-bonded clusters. Specifically, the focus is on breaking of hydrogen bonds following excitation of an intramolecular vibration of the cluster. VP of the water dimer and trimer, HCl clusters, and mixed HCl-water clusters are the major topics, but related work on hydrogen halide dimers and trimers, ammonia clusters, and mixed dimers with polyatomic units are reviewed for completion and comparison.

Microscopic mechanisms of equilibrium melting of a solid.

The melting of a solid, like other first-order phase transitions, exhibits an intrinsic time-scale disparity: The time spent by the system in metastable states is orders of magnitude longer than the transition times between the states. Using rare-event sampling techniques, we find that melting of representative solids-here, copper and aluminum-occurs via multiple, competing pathways involving the formation and migration of point defects or dislocations. Each path is characterized by multiple barrier-crossing events arising from multiple metastable states within the solid basin.

Experimental and theoretical investigations of energy transfer and hydrogen-bond breaking in small water and HCl clusters.

Water is one of the most pervasive molecules on earth and other planetary bodies; it is the molecule that is searched for as the presumptive precursor to extraterrestrial life. It is also the paradigm substance illustrating ubiquitous hydrogen bonding (H-bonding) in the gas phase, liquids, crystals, and amorphous solids. Moreover, H-bonding with other molecules and between different molecules is of the utmost importance in chemistry and biology.

Order-parameter-aided temperature-accelerated sampling for the exploration of crystal polymorphism and solid-liquid phase transitions.

The problem of predicting polymorphism in atomic and molecular crystals constitutes a significant challenge both experimentally and theoretically. From the theoretical viewpoint, polymorphism prediction falls into the general class of problems characterized by an underlying rough energy landscape, and consequently, free energy based enhanced sampling approaches can be brought to bear on the problem. In this paper, we build on a scheme previously introduced by two of the authors in which the lengths and angles of the supercell are targeted for enhanced sampling via temperature accelerated adiabatic free energy dynamics [T.

Sampling saddle points on a free energy surface.

Many problems in biology, chemistry, and materials science require knowledge of saddle points on free energy surfaces. These saddle points act as transition states and are the bottlenecks for transitions of the system between different metastable states. For simple systems in which the free energy depends on a few variables, the free energy surface can be precomputed, and saddle points can then be found using existing techniques.