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Multitime quantum correlation functions are central objects in physical science, offering a direct link between the experimental observables and the dynamics of an underlying model. While experiments such as 2D spectroscopy and quantum control can now measure such quantities, the accurate simulation of such responses remains computationally expensive and sometimes impossible, depending on the system's complexity. A natural tool to employ is the generalized quantum master equation (GQME), which can offer computational savings by extending reference dynamics at a comparatively trivial cost. However, dynamical methods that can tackle chemical systems with atomistic resolution, such as those in the semiclassical hierarchy, often suffer from poor accuracy, limiting the credence one might lend to their results. By combining work on the accuracy-boosting formulation of semiclassical memory kernels with recent work on the multitime GQME, here we show for the first time that one can exploit a multitime semiclassical GQME to dramatically improve both the accuracy of coarse mean-field Ehrenfest dynamics and obtain orders of magnitude efficiency gains. [ABSTRACT FROM AUTHOR]

Machine-learning (ML)-based interatomic potentials are increasingly popular in material modeling, enabling highly accurate simulations with thousands and millions of atoms. However, the performance of machine-learned potentials depends strongly on the choice of hyperparameters—that is, of those parameters that are set before the model encounters data. This problem is particularly acute where hyperparameters have no intuitive physical interpretation and where the corresponding optimization space is large. Here, we describe an openly available Python package that facilitates hyperparameter optimization across different ML potential fitting frameworks. We discuss methodological aspects relating to the optimization itself and to the selection of validation data, and we show example applications. We expect this package to become part of a wider computational framework to speed up the mainstream adaptation of ML potentials in the physical sciences. [ABSTRACT FROM AUTHOR]

Imaging and spectroscopy at terahertz (THz) frequencies have become key methods for fundamental studies across the physical sciences. With the emergence of nanoscale materials and devices, holding great promise for photonics, electronics, and communication technologies, the search for THz analysis at the nanoscale arises. Detectorless THz near-field nanoscopy emerged as a versatile method for hyperspectral mapping of light–matter interaction phenomena in bi-dimensional materials and systems. However, it is strongly limited by the weak scattering efficiencies of atomic force microscope (AFM) tips. Here, we experimentally evaluate the performance of unconventional AFM tip shapes to enhance the scattering efficiency, at three frequencies, namely, 2.0, 3.0, and 4.6 THz. The impact of tip geometry is corroborated by numerical simulations. The shorter shank length of the evaluated tips provides a very compelling alternative to commercial tips at frequencies >2 THz. [ABSTRACT FROM AUTHOR]

Modern trends in the development of experimental research methods imply not only an increase in the accuracy of a specific technique but also the possibility of combining diverse measurements in the course of one experiment. While optical spectroscopy remains one of the most powerful tools used in the chemical and physical sciences to study the structure of a wide range of materials, it is impossible to imagine a single study of local mechanical properties without instrumental indentation. A powerful investigation technique is the in situ combination of these two methods within one experiment. This can be made by focusing the laser either through the transparent sample or through the transparent indenter tip of the special geometry preventing the total internal reflection in diamond. This Tutorial discusses the preparation and characterization of such a transparent diamond indenter. The obtained experimental results and promising application areas of simultaneous measurement of optical spectra during indentation are considered. [ABSTRACT FROM AUTHOR]

The problem of computing the rate of diffusion-aided activated barrier crossings between metastable states is one of broad relevance in physical sciences. The transition path formalism aims to compute the rate of these events by analysing the statistical properties of the transition path between the two metastable regions concerned. In this paper, we show that the transition path process is a unique solution to an associated stochastic differential equation, with a discontinuous and singular drift term. The singularity arises from a local time contribution, which accounts for the fluctuations at the boundaries of the metastable regions. The presence of fluctuations at the local time scale calls for an excursion theoretic consideration of barrier crossing events. We show that the rate of such events, as computed from excursion theory, factorizes into a local time term and an excursion measure term, which bears empirical similarity to the transition state theory rate expression. Since excursion theory makes no assumption about the presence of a transition state in the potential energy landscape, the mathematical structure underlying this factorization ought to be general. We hence expect excursion theory (and local times) to provide some physical and mathematical insights in generic barrier crossing problems. [ABSTRACT FROM AUTHOR]

With the concept of topological order of electronic states in crystals being proposed, the pursuit of topological phases in various systems has become an important subject of modern physical science. The existence of antisymmetric Dzyaloshinskii–Moriya interaction caused by spin–orbit coupling in magnetic systems leads to the non-reciprocal propagation of magnons, which is a key factor to realize the magnonic analog of an electronic topological insulator (TI). Here, a two-dimensional magnetic skyrmion crystal (SkX) is introduced as a platform for realizing magnonic TI. It is numerically demonstrated that nonchiral and chiral edge states could exist in different magnonic bandgaps. The nonchiral edge magnons can propagate to both directions simultaneously along the boundary of the SkX with different wavelengths and group velocities. By contrast, chiral edge magnons are topologically protected, resulting in the unidirectional propagation along the boundaries and the robustness against defects or disorders. Furthermore, the chirality and the presence of edge magnons can be manipulated by the polarity of skyrmions and the width of the second magnonic bandgap via the magnetic field. Our findings could provide a skyrmionic paradigm for investigating topological magnonics and even quantum magnonics. [ABSTRACT FROM AUTHOR]

Although physics does not often veer into financial concerns, it is an illuminating exercise to try estimating the value of a barren planet at market prices for dirt, rock, water, copper, aluminum, silver, gold, etc. Energy perspectives Physics "owns" energy (and space and time if we're being greedy), so energy and power domains offer rich opportunities for gaining new understanding. The thermodynamic consequences of this absurdity can also be examined, by which it is revealed that waste heat from all this energy production on Earth would rival climate change in a century and be 10 times worse a century after that, eventually boiling Earth's oceans in about 400 yr.[3] It can be tricky to convey to students that this is not a I prediction i of what I will i - or even I can i - happen, but a tool to understand I limits i to what is possible and what I cannot i transpire. [Extracted from the article]

In my context, such a class might be a general physics course for science majors (pre-calculus- or calculus-based) or an algebra-based, mostly conceptual survey course for non-science majors, with between 8 and 30 students per class. Homework assignments, reflections, surveys, and biweekly anonymous feedback from 2017 onward (excepting 2020) from general physics courses for science majors and for non-science majors indicate enhanced interest in physics, greater cognitive understanding of climate science basics, increased appreciation of the value of climate stories and justice issues, ability to think critically about proposed climate solutions, and, for the majority of students (in some classes greater than 80%), a desire to take meaningful action for climate justice. Today, I still teach the basic science of climate change as it aligns with physics topics in my general physics courses. [Extracted from the article]

One of the primary goals of the course that was ultimately developed was that it would also meet the requirements of the physical science elective for general education students and would be open to students from any major. A review of courses at other institutions found that many schools offered physical science courses with an environmental component,[[1]] including a course developed previously by the author on energy and the environment. [Extracted from the article]

Bayesian analysis enables flexible and rigorous definition of statistical model assumptions with well-characterized propagation of uncertainties and resulting inferences for single-shot, repeated, or even cross-platform data. This approach has a strong history of application to a variety of problems in physical sciences ranging from inference of particle mass from multi-source high-energy particle data to analysis of black-hole characteristics from gravitational wave observations. The recent adoption of Bayesian statistics for analysis and design of high-energy density physics (HEDP) and inertial confinement fusion (ICF) experiments has provided invaluable gains in expert understanding and experiment performance. In this Review, we discuss the basic theory and practical application of the Bayesian statistics framework. We highlight a variety of studies from the HEDP and ICF literature, demonstrating the power of this technique. Due to the computational complexity of multi-physics models needed to analyze HEDP and ICF experiments, Bayesian inference is often not computationally tractable. Two sections are devoted to a review of statistical approximations, efficient inference algorithms, and data-driven methods, such as deep-learning and dimensionality reduction, which play a significant role in enabling use of the Bayesian framework. We provide additional discussion of various applications of Bayesian and machine learning methods that appear to be sparse in the HEDP and ICF literature constituting possible next steps for the community. We conclude by highlighting community needs, the resolution of which will improve trust in data-driven methods that have proven critical for accelerating the design and discovery cycle in many application areas. [ABSTRACT FROM AUTHOR]

Detection of the microwave (MW) field with high accuracy is very important in the physical science and engineering fields. Herein, an atomic Rabi resonance-based MW magnetic field sensor with a high-dynamic-range is reported, where α and β Rabi resonances are used to measure MW fields. In MW measurement experiments, the sensor successfully measured a magnetic field of about 10 nT at 9.2 GHz using the α Rabi resonance line on the cesium clock transition and continuously detected the MW magnetic field in the X-band over a high dynamic power range of >60 dB from the β Rabi resonance. Finally, the MW power frequency shift and power broadening are investigated to support more sensitive field measurements. The proposed MW detection method can be extended to cover a higher dynamic range and a wider frequency band by applying stronger excitations and exploring non-clock atomic transitions, respectively. In addition to MW magnetic field sensing, other potential application of the proposed method can be explored, including SI-traceable MW calibration and atomic communication. [ABSTRACT FROM AUTHOR]

On the left is one, ca. 1910, that is in my collection of early physics teaching apparatus.[3] Two light bulbs illuminate a postcard that serves as an object for the lens, and an image is projected on a white sheet. 1 has an objective lens with a focal length of 15 cm and a second lens with a focal length of 7 cm. The second lens is placed 5 cm inside a smaller sliding box. In this note, I will describe five pieces of apparatus: a telescope, a microscope, a galvanometer, an opaque projector, and an air table that I built for my boys to bring them into the worlds of physical science. The image formed by the first lens was inverted, and the magnified image obtained with the second lens was similarly inverted. [Extracted from the article]

The absorption of electromagnetic radiation by fluids or solids results in a temperature change, which, in turn, causes a change in volume resulting in the generation of a mechanical wave. When the emitted acoustic waves are recorded, the effect is referred to as a photoacoustic or optoacoustic effect. Many of the modern thermal measurement methods rely on photothermal and photoacoustic effects, including thermoreflectance, Raman scattering, Brillouin light scattering, and thermal radiation. Using two different photothermal techniques, micro-Raman thermometry and scanning thermal microscopy, Massoud I et al. i [6] measured the thermal conductivity of porous silicon, showing that both methods yielded consistent results. [Extracted from the article]

Yang I et al. i discovered that VO SB I x i sb doping in a NeFe layer coated on a porous nickel cobalt phosphide (NiCoP) electrocatalyst supported by a titanium mesh would reduce the overpotential for both OER and HER.[12] This modified electrocatalyst could be employed as a low-cost bifunctional catalyst to construct both the positive and negative electrodes in an electroyzer.[12] The doping with VO SB I x i sb made the NeFe surface superhydrophilic. While alkaline and PEM electrolyzers are used for commercial-scale hydrogen production, solid-oxide electrolyzers are in the demonstration/pilot-plant stage. In a two-step cycle, a redox material, typically a binary oxide, is reduced to a metallic or oxygen deficient (non-stoichiometric) state at high temperatures and then exposed to water vapor or carbon dioxide or both where the catalyst gets oxidized with the formation of hydrogen and CO. "Global warming of 1.5 °C to 2 °C will be exceeded during the 21st century unless deep reductions in CO SB 2 sb and other greenhouse gas emissions occur in the coming decades" states the United Nations Intergovernmental Panel on Climate Change (IPCC) in its Sixth Assessment Report.[1] In the recent years, IPCC published several reports and presented undisputable evidence associating greenhouse gas accumulation in the atmosphere with global average surface temperature increase and unprecedented changes in the weather patterns and the Earth's ecosystems. [Extracted from the article]

Khotkevych willingly changed his profile of education and in 1935, having graduated from the Faculty of Physics and Mechanics, he received a diploma of "physicist-researcher." These works, to a certain extent, reflect the versatility of the scientific interests of V. G. Khotkevych as a materials scientist, a physicist-experimentalist, and an outstanding organizer of Ukrainian physical science. The development of these materials is carried out at the School of Physics founded by V. G. Khotkevych within the V. N. Karazin Kharkiv National University (KhNU), where Khotkevych was its first dean. [Extracted from the article]

Simulation and data analysis have evolved into powerful methods for discovering and understanding molecular modes of action and designing new compounds to exploit these modes. The combination provides a strong impetus to create and exploit new tools and techniques at the interfaces between physics, biology, and data science as a pathway to new scientific insight and accelerated discovery. In this context, we explore the rational design of novel antimicrobial peptides (short protein sequences exhibiting broad activity against multiple species of bacteria). We show how datasets can be harvested to reveal features which inform new design concepts. We introduce new analysis and visualization tools: a graphical representation of the k-mer spectrum as a fundamental property encoded in antimicrobial peptide databases and a data-driven representation to illustrate membrane binding and permeation of helical peptides. [ABSTRACT FROM AUTHOR]

Recognized as one of the most beautiful experiments of all time, the oil drop experiment performed by Robert Millikan and his graduate students (primarily Harvey Fletcher) is a standard in the repertoire of experiments performed by undergraduate physics students. However, "as a teaching lab it does not enjoy a good reputation for three reasons: eyestrain, tedium, and poor, unconvincing results." Several have attempted to make this experiment more student-friendly by improving the optics and replacing the stopwatch with a computer, replacing the eye with a video camera, and utilizing video analysis tools. Currently available versions of the oil drop apparatus for high school and college students have incorporated a number of these features. On the other hand, others are ready to replace the experiment with interactive computer-based simulations. There is another alternative: Have students analyze pre-recorded videos of the Millikan experiment. Besides the "Millikan Movies" produced at the California State University at Chico, the collection "Physics: CINEMA CLASSICS" ("PCC") contains the essential nuggets from a Physical Science Study Committee (PSSC) educational film made in 1959 sufficient to compute the elementary electric charge. [ABSTRACT FROM AUTHOR]

The study of drag and lift forces on natural or manmade physical objects in confined domains owns both practical and theoretical importance in the field of general sciences, which particularly includes physical science. In this regard, this work is the numerical effort to examine drag and lift coefficients for the moving liquid stream at a low Reynolds number in the Convergent–Divergent (CD) channel. The liquid stream flows with the parabolic profile from the inlet and interacts with the case-wise infinite cylinders as barriers in the right vicinity of CD throat. The differential formulation is designed toward CD channel for the flow area, and the numerical solution is stated using the hybrid meshed finite element method. The contour and line graph analysis was conducted for a greater understanding of the involved flow variables. The comparative analysis is enclosed by setting a fixed Reynolds number for the hydrodynamic forces against mounted obstacles in a CD throat. It is seen that the square-shaped cylinder experienced the greater drag force as compared to the circular obstruction. [ABSTRACT FROM AUTHOR]

This work incorporates symbolic regression to propose simple and accurate expressions that fit to material datasets. The incorporation of symbolic regression in physical sciences opens the way to replace "black-box" machine learning techniques with representations that carry the physical meaning and can reveal the underlying mechanism in a purely data-driven approach. The application here is the extraction of analytical equations for the self-diffusion coefficient of the Lennard-Jones fluid by exploiting widely incorporating data from the literature. We propose symbolic formulas of low complexity and error that achieve better or comparable results to well-known microscopic and empirical expressions. Results refer to the material state space both as a whole and in distinct gas, liquid, and supercritical regions. [ABSTRACT FROM AUTHOR]

The article contributed by Dass I et al. i reports the development of blown-powder DED system for I operando i synchrotron x-ray diffraction experiments.[10] In this system, the powder nozzle and the heating laser are decoupled and controlled independently. The article contributed by Zhang I et al. i reviews the history of applying lab-based and synchrotron x-ray imaging techniques for the I in situ i study of the laser welding process.[1] Modern fusion-based AM techniques can, in fact, find their root in welding. The recent surge in I operando i synchrotron experiments on AM started only about five years ago,[[12], [14]] yet there are now more than 20 apparatuses being used at almost all major synchrotron facilities by an expanding research community. [Extracted from the article]

Molecular dynamics simulations are employed to examine the relation between ejecta production and shock-breakout pressure for single crystal Pb subjected to a decaying shockwave loading. To better understand the physical mechanism of ejecta formation, a surface with multiple triangular grooves representing the imperfections left from machining finish is taken into consideration. It is found that the ejecta volume density distribution displays a smooth nature and the amount of ejecta increases significantly after melting on release or shock. Additionally, the ejecta particle mass distribution is captured by a power law scaling, revealing the self-similarity. These results are in reasonable agreement with the characteristics of experimentally diagnosed findings. [ABSTRACT FROM AUTHOR]

The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements as well as the relationships among multiple properties to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates: (i) prediction using only a material's composition, (ii) learning and exploitation of correlations among target properties in multi-target regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 three-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data are available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with transfer learning [H-CLMP(T)] wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well suited for multi-target regression across the physical sciences. [ABSTRACT FROM AUTHOR]

Recent applications of machine learning, in particular deep learning, motivate the need to address the generalizability of the statistical inference approaches in physical sciences. In this Letter, we introduce a modular physics guided machine learning framework to improve the accuracy of such data-driven predictive engines. The chief idea in our approach is to augment the knowledge of the simplified theories with the underlying learning process. To emphasize their physical importance, our architecture consists of adding certain features at intermediate layers rather than in the input layer. To demonstrate our approach, we select a canonical airfoil aerodynamic problem with the enhancement of the potential flow theory. We include the features obtained by a panel method that can be computed efficiently for an unseen configuration in our training procedure. By addressing the generalizability concerns, our results suggest that the proposed feature enhancement approach can be effectively used in many scientific machine learning applications, especially for the systems where we can use a theoretical, empirical, or simplified model to guide the learning module. [ABSTRACT FROM AUTHOR]

Oxidation state is a powerful concept that is widely used in chemistry and materials physics, although the concept itself is arguably ill-defined quantum mechanically. In this work, we present impartial comparison of four, well-recognized theoretical approaches based on Lowdin atomic orbital projection, Bader decomposition, maximally localized Wannier function, and occupation matrix diagonalization, for assessing how well transition metal oxidation states can be characterized. Here, we study a representative molecular complex, tris(bipyridine)ruthenium. We also consider the influence of water solvation through first-principles molecular dynamics as well as the improved electronic structure description for strongly correlated d-electrons by including Hubbard correction in density functional theory calculations. [ABSTRACT FROM AUTHOR]

We report a theoretical study of nanobubble stabilization on a substrate by contact line pinning. In particular, we predict the magnitude of the pinning force required to stabilize a nanobubble and the threshold values of the pinning force that the substrate can provide. We show that the substrate chemistry and the local structures of substrate heterogeneity together determine whether or not surface nanobubbles are stable. We find that for stable nanobubbles, the contact angles are independent of substrate chemistry as its effects are cancelled out by the pinning effect. This prediction is in agreement with available experimental data. [ABSTRACT FROM AUTHOR]

Since the discovery of graphene, two-dimensional materials with atomic level thickness have rapidly grown to be a prosperous field of physical science with interdisciplinary interest for their fascinating properties and broad applications. Very recently, the experimental observation of ferromagnetism in a Cr2Ge2Te6 bilayer and a CrI3 monolayer opened a door to pursue long-absent intrinsic magnetic orders in twodimensional materials. Meanwhile, the ferroelectricity was also experimentally found in a SnTe monolayer and CuInP2S6 few layers. The emergence of these ferroic orders in the two-dimensional limit not only brings new challenges to our physical knowledge but also provides more functionalities for potential applications. Among various two-dimensional ferroic ordered materials, transition/rare-earth metal halides and their derivants are very common. In this Research Update, based on transition/rare-earth metal halides, the physics of various ferroic orders in two-dimensional materials will be illustrated. The potential applications based on their magnetic and polar properties will also be discussed. [ABSTRACT FROM AUTHOR]

Some of the reasons for the underrepresentation of women in physics are evident in the reflections of two undergraduate women. Leia is a chemistry major who loves college-level physical chemistry and quantum mechanics but does not identify with the discipline of physics, partly because she has low self-efficacy as a physicist and has received very little recognition for her work and learning in physics. Paulette is a physics major who loves physics but feels isolated by the current physics learning environment. She reluctantly dropped an honors introductory lab after being snubbed by her male classmates, who partnered with one another, leaving her to work alone. Paulette's experiences with condescending male professors activated a stereotype threat about who can succeed in physics that caused her to disengage in class. In addition to these things, in this paper we discuss what these women felt has helped them so far and explore their suggestions for what would help women in physics courses as they pursue their quest for a physical science degree. [ABSTRACT FROM AUTHOR]

Nanomedicine aims to increase the effectiveness of therapeutic and diagnostic agents by exploiting unique properties that arise when operating at the nanoscale. Despite the recent growth of the field, nanomedicine has only managed modest improvements over conventional molecular drugs and imaging probes. To increase the efficacy of nanomedicine requires understanding fundamental processes that govern the ability of nanoparticles to locate and remain at diseased sites within the body. These processes include a nanoparticle's interactions with cells and proteins, margination in blood vessels, permeation into diseased tissues, and diffusive ability to reach all diseased cells within the tissue. Each of these processes is governed by phenomena commonly studied within fields of the physical sciences. By employing knowledge of colloidal interactions, fluid dynamics, kinetics, statistical mechanics, and thermodynamics, researchers can rationally design nanoparticles to overcome previous limitations of nanomedicine. This review will highlight recent and exemplary studies that present valuable insights into the physical phenomena of nanomedicine and present improved next-generation nanoparticle designs by applying these insights. [ABSTRACT FROM AUTHOR]

The binding energy and generalized stacking-fault energy (GSFE) are two critical interface properties of two dimensional layered materials, and it is still unclear how neighboring layers affect the interface energy of adjacent layers. Here, we investigate the effect of neighboring layers by comparing the differences of binding energy and GSFE between trilayer heterostructures (graphene/graphene/graphene, graphene/graphene/boron nitride, boron nitride/graphene/boron nitride) and bilayer heterostructures (graphene/graphene, graphene/boron nitride) using density functional theory. The binding energy of the adjacent layers changes from −2.3% to 22.55% due to the effect of neighboring layer, with a very small change of the interlayer distance. Neighboring layers also make a change from −2% to 10% change the GSFE, depending on the property of the interface between adjacent layers. In addition, a new simple expression is proven to describe the GSFE landscape of graphene-like structure with high accuracy. [ABSTRACT FROM AUTHOR]

In the canonical supermolecular approach, calculations of interaction energies for molecular clusters involve a calculation of the whole cluster, which becomes expensive as the cluster size increases. We propose a novel approach to this task by demonstrating that interaction energies of such clusters can be constructed from those of small subclusters with a much lower computational cost by applying progressively lower-level methods for subsequent terms in the many-body expansion. The efficiency of such 'stratified approximation' many-body approach (SAMBA) is due to the rapid convergence of the many-body expansion for typical molecular clusters. The method has been applied to water clusters (H2O)n, n = 6, 16, 24. For the hexamer, the best results that can be obtained with current computational resources in the canonical supermolecular method were reproduced to within about one tenth of the uncertainty of the canonical approach while using 24 times less computer time in the many-body expansion calculations. For (H2O)24, SAMBA is particularly beneficial and we report interaction energies with accuracy that is currently impossible to obtain with the canonical supermolecular approach. Moreover, our results were computed using two orders of magnitude smaller computer resources than used in the previous best calculations for this system. We also show that the basis-set superposition errors should be removed in calculations for large clusters. [ABSTRACT FROM AUTHOR]

The stability and work function of the (001), (110), and (111) surfaces of ordered TaCxN1-x crystals with various possible terminations were studied using density functional theory calculations. Among all surfaces considered, those with (001) orientations were found to be the most stable. The work function of these (001) stable surfaces increases monotonically from the pure TaN value of 3.3 eV to the pure TaC value of 3.8 eV. However, this variation is far surpassed by the strong dependence of the work function on the surface orientation and chemistry, regardless of the bulk alloy composition. [ABSTRACT FROM AUTHOR]