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Laser-induced ultrafast demagnetization (UD) in ferromagnetic metals opens a new frontier at the intersection between laser technology and materials sciences. However, a complete understanding is still missing even in simple 3 d metals. Prior studies have often concentrated on the effect of laser fluence on UD, but whether and how the wavelength affects UD remain under explored. Here, we propose a new perspective that is based on laser wavelength. We show, via the example of fcc Ni, that without intraband transitions, wavelength has a significant impact on UD but the spin moment reduction is small. With the intraband transition, UD weakly depends on wavelength, but with a large spin reduction. The time-resolved electron and spin density of states reveals that electrons around the Fermi energy are largely responsible for strong demagnetization, which almost wipes out the imprint of the photon energy on demagnetization, explaining the experimental observation. A significant spin reduction is found when a large portion of the unoccupied minority states slightly above the Fermi level becomes occupied. [ABSTRACT FROM AUTHOR]

Atom probe tomography is a ubiquitous method in materials science and engineering capable of revealing the atomic-level three-dimensional composition of a plethora of materials. Beside the nature of atoms forming the analyzed material, atom probe data are also known to contain information on the crystallography. In particular, remnants of the atomic plane sets forming on the surface of the tip-shaped samples are commonly found in atom probe data sets of crystalline metallic materials. The plane remnants can be utilized to correlate the nano-scale chemical analysis that atom probe tomography provides with the crystallographic structure on the same scale. We describe a protocol to reveal and track the atomic planes systematically from raw atom probe data. We demonstrate for both metals and semiconductors that the extracted crystallographic can be used to calibrate a dynamic reconstruction of the respective data set acquired in atom probe tomography. Furthermore, we utilize the crystal planes to make precise measurements of layer thicknesses in atom probe data of semiconductor heterostructures. [ABSTRACT FROM AUTHOR]

We introduce an efficient method for simulating Coulomb systems confined by conducting planar surfaces. The new approach is suitable for both coarse-grained models and all-atom simulations of ionic liquids between polarizable metal electrodes. To demonstrate its efficiency, we use the new method to study the differential capacitance of an ionic liquid. Our technique is at least two orders of magnitude faster than traditional Ewald-based methods for non-polarizable surfaces, when calculating the electrostatic energy between two ions. This advancement has significant potential to enhance understanding in fields such as materials science and electrochemistry, enabling efficient large-scale simulations of Coulomb systems confined by polarizable metal electrodes. [ABSTRACT FROM AUTHOR]

Interband and intraband transitions are fundamental concepts in the study of electronic properties of materials, particularly semiconductors and nanomaterials. These transitions involve the movement of electrons between distinct energy states or bands within a material. In addition, charge mobility is also a critical parameter in materials science and electronics. A thorough understanding of these transitions and mobility is critical for the development and optimization of advanced electronic and optoelectronic devices. In this study, we investigate the influence of external periodic drivings on interband and intraband transitions, as well as charge mobility, within a driven two-band model that includes electron–phonon coupling. These external periodic drivings can include a periodic laser field, a time-varying magnetic or electric field, or an alternating current voltage source. We have developed the Floquet surface hopping and Floquet mean field methods to simulate electronic dynamics under various drivings in both real and reciprocal spaces. Our findings demonstrate that periodic drivings can enhance interband transitions while suppressing intraband transitions. In addition, charge mobility is restrained by these external periodic drivings in the driven two-band model. [ABSTRACT FROM AUTHOR]

The calculation of the interfacial free energy between two thermodynamic phases is crucial across various fields, including materials science, chemistry, and condensed matter physics. In this study, we apply an existing thermodynamic approach, the Gibbs–Cahn integration method, to determine the interfacial free energy under different coexistence conditions, relying on data from a single-state calculation at specified pressure and temperature. This approach developed by Laird et al. [J. Chem. Phys. 131, 114110 (2009)] reduces the computational demand and enhances efficiency compared to methods that require separate measurements at each thermodynamic state. The integration scheme computes the excess interfacial free energy using unbiased constant volume, temperature, and number of particle simulations (NVT), where the two phases coexist, to provide input for the calculations. We apply this method to the Lennard-Jones and mW water models for liquid–solid interfaces, as well as the Lennard-Jones and TIP4P/2005 models for liquid–vapor interfaces. Our results demonstrate the accuracy and effectiveness of this integration route for estimating the interfacial free energy along a coexistence line. [ABSTRACT FROM AUTHOR]

This work determines a new set of EUV/x-ray optical constants for aluminum (Al), one of the most important materials in science and technology. Absolute photoabsorption (transmittance) measurements in the 17–1300 eV spectral range were performed on freestanding Al films protected by carbon (C) layers, to prevent oxidation. The dispersive portion of the refractive index was obtained via the Kramers–Kronig transformation. Our data provide significant improvements in accuracy compared to previously tabulated values and reveal fine structure in the Al L1 and L2,3 regions, with photon energy step sizes as small as 0.02 eV. The implications of this work in the successful realization of EUV/x-ray instruments and in the validation of atomic and molecular physics models are also discussed. [ABSTRACT FROM AUTHOR]

The editorial in the Journal of Applied Physics discusses the importance of defects in semiconductor materials for designing semiconductor-based devices. It covers various types of defects, their impact on device performance, and advancements in defect modeling and experimentation. The document also highlights research areas such as defects in oxides, III-V semiconductors, silicon carbide, 2D materials, and detector/absorber materials. The study emphasizes the ongoing research in understanding defects in semiconductors and their implications for fields like quantum information science, power electronics, and energy storage and conversion. [Extracted from the article]

Simple and stable generation of monodispersed droplets with volume from picolitre to nanoliter is one of the key factors in high-throughput quantitative microreactors for chemical and biomedical applications. In this work, an efficient method that could realize simple manipulating microflow with a broad operation window for preparing monodispersed droplets with controllable diameter is developed. The microfluidic device is constructed by inserting a capillary with an oblique angle (α) into the continuous phase, named a floating capillary-based open microfluidic device (FCOMD). The transition of droplet-generating mode between dripping and jetting can be achieved by changing capillary number and α. A computational model based on the volume-of-fluid/continuum-surface-force method to explain the controllability of α on the droplet formation regime and droplet breakage, verifying the synergistic effect of ΔP and Fb, facilitates the droplet pinching. A descending order of Pn of capillary with different α is that 45° > 30° > 15° > 60° > 75°, leading to the same order of generated droplet's D. When compared with the traditional capillary co-flow device, the generating throughput of the integrated FCOMD obtained by integrating different numbers of capillaries is at least ten times. Moreover, water in oil, oil in water double-emulsion, colloidal dispersed droplets, and liquid crystal droplets with diameters ranging from 25 to 800 μm are prepared on-demand by the FCOMD, indicating the universality of the microfluidic device. Thus, the FCOMD shows the features of simplicity, practicability, and flexibility, offering valuable guidance for generating controllable droplets with wide size change and showing a great potential application in material science, foods, pharmaceuticals, and cosmetics. [ABSTRACT FROM AUTHOR]

We introduce a methodological framework coupling machine-learning potentials, ring polymer molecular dynamics (RPMD), and kinetic Monte Carlo (kMC) to draw a comprehensive physical picture of the collective diffusion of hydrogen atoms on metal surfaces. For the benchmark case of hydrogen diffusion on a Ni(100) surface, the hydrogen adsorption and diffusion energetics and its dependence on the local coverage is described via a neural-network potential, where the training data are computed via periodic density functional theory (DFT) and include all relevant optimized diffusion and desorption paths, sampled by nudged elastic band optimizations and molecular dynamics simulations. Nuclear quantum effects, being crucial for processes involving hydrogen at low temperatures, are treated by RPMD. The diffusion rate constants are calculated with a combination of umbrella samplings employed to map the free energy profile and separate samplings of recrossing trajectories to obtain the transmission coefficient. The calculated diffusion rates for different temperatures and local environments are then combined and fitted into a kMC model allowing access to larger time and length scales. Our results demonstrate an outstanding performance for the trained neural network potential in reproducing reference DFT energies and forces. We report the effective diffusion rates for different temperatures and hydrogen surface coverages obtained via this recipe in good agreement with the experimental results. The method combination proposed in this study can be instrumental for a wide range of applications in materials science. [ABSTRACT FROM AUTHOR]

Machine learning interatomic potentials (MLIPs) provide an optimal balance between accuracy and computational efficiency and allow studying problems that are hardly solvable by traditional methods. For metallic alloys, MLIPs are typically developed based on density functional theory with generalized gradient approximation (GGA) for the exchange–correlation functional. However, recent studies have shown that this standard protocol can be inaccurate for calculating the transport properties or phase diagrams of some metallic alloys. Thus, optimization of the choice of exchange–correlation functional and specific calculation parameters is needed. In this study, we address this issue for Al–Cu alloys, in which standard Perdew–Burke–Ernzerhof (PBE)-based MLIPs cannot accurately calculate the viscosity and melting temperatures at Cu-rich compositions. We have built MLIPs based on different exchange–correlation functionals, including meta-GGA, using a transfer learning strategy, which allows us to reduce the amount of training data by an order of magnitude compared to a standard approach. We show that r2SCAN- and PBEsol-based MLIPs provide much better accuracy in describing thermodynamic and transport properties of Al–Cu alloys. In particular, r2SCAN-based deep machine learning potential allows us to quantitatively reproduce the concentration dependence of dynamic viscosity. Our findings contribute to the development of MLIPs that provide quantum chemical accuracy, which is one of the most challenging problems in modern computational materials science. [ABSTRACT FROM AUTHOR]

The interview with Bob Rosner, a former chair of the Bulletin's Science and Security Board, explores his skepticism about fusion energy becoming a commercial energy source in the near future. Rosner discusses the complexities of fusion research, focusing on the challenges faced by tokamaks and stellarators in achieving fusion ignition and stability. He also touches on the issues related to tritium supply, neutron embrittlement, and the potential applications of fusion research beyond energy production. Rosner emphasizes the importance of fusion research for national defense and nonproliferation efforts, highlighting the role of facilities like NIF in advancing scientific knowledge and preventing nuclear weapons testing. [Extracted from the article]

The document discusses the integration of machine learning (ML) into thermal transport research, highlighting its transformative impact on understanding and controlling heat transfer processes. It features 31 papers categorizing ML applications into machine learning potentials, predicting thermal properties, design and optimization, data analysis, and tutorials. ML has enabled accurate simulations, precise property predictions, innovative system designs, and efficient data analysis in thermal transport research, showcasing the potential for further advancements in the field. Despite challenges like model transferability, data scarcity, and interpretability, the document emphasizes the promising future of ML in advancing thermal science and engineering. [Extracted from the article]

Phase separation is ubiquitous at the interface between two distinct phases. Physical transformation during phase separation often plays a crucial role in many important mechanisms, such as lipid phase separation, which is fundamental for transport through biological membranes. Phase separation can be complex, involving changes in the physical state and the reorganization of molecular structures, influencing the behavior and function of materials and biological systems. Surface-sensitive vibrational sum frequency generation (VSFG) spectroscopy provides a powerful tool for investigating these interfacial processes. As a non-linear optical technique, VSFG spectroscopy is sensitive to changes in molecular orientation and interactions at interfaces, making it an ideal method for studying phase separation processes. Here, we review the molecular interaction mechanisms underlying phase separation. We also explore the application of VSFG spectroscopy in studying phase separation processes at different interfaces. In particular, we focus on oil–water interfaces, which are relevant in environmental and industrial contexts; polymer and lipid surfaces, important for materials science and biological membranes; and intrinsically disordered protein systems, which play key roles in cellular function and disease. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue in the Journal of Applied Physics on semiconductor physics. The special issue focuses on the research of new semiconductor materials, structures, and devices, as well as the development of characterization methods based on plasma, thermal, elastic, acoustic, and related effects. The articles cover various topics such as photothermal methods, theoretical and experimental models, modern materials like thin films and heterostructures, and imaging techniques. The research presented in this special issue has applications in microelectronics, optoelectronics, solar cells, medical instrumentation, and quantum computing. [Extracted from the article]

Biphasic interfaces are complex but fascinating regimes that display a number of properties distinct from those of the bulk. The CO2–H2O interface, in particular, has been the subject of a number of studies on account of its importance for the carbon life cycle as well as carbon capture and sequestration schemes. Despite this attention, there remain a number of open questions on the nature of the CO2–H2O interface, particularly concerning the interfacial tension and phase behavior of CO2 at the interface. In this paper, we seek to address these ambiguities using ab initio-quality simulations. Harnessing the benefits of machine-learned potentials and enhanced statistical sampling methods, we present an ab initio-level description of the CO2–H2O interface. Interfacial tensions are predicted from 1 to 500 bars and found to be in close agreement with experiment at pressures for which experimental data are available. Structural analyses indicate the buildup of an adsorbed, saturated CO2 film forming at a low pressure (20 bars) with properties similar to those of the bulk liquid, but preferential perpendicular alignment with respect to the interface. The CO2 monolayer buildup coincides with a reduced structuring of water molecules close to the interface. This study highlights the predictive nature of machine-learned potentials for complex macroscopic properties of biphasic interfaces, and the mechanistic insight obtained into carbon dioxide aggregation at the water interface is of high relevance for geoscience, climate research, and materials science. [ABSTRACT FROM AUTHOR]

Biphasic interfaces are complex but fascinating regimes that display a number of properties distinct from those of the bulk. The CO2–H2O interface, in particular, has been the subject of a number of studies on account of its importance for the carbon life cycle as well as carbon capture and sequestration schemes. Despite this attention, there remain a number of open questions on the nature of the CO2–H2O interface, particularly concerning the interfacial tension and phase behavior of CO2 at the interface. In this paper, we seek to address these ambiguities using ab initio-quality simulations. Harnessing the benefits of machine-learned potentials and enhanced statistical sampling methods, we present an ab initio-level description of the CO2–H2O interface. Interfacial tensions are predicted from 1 to 500 bars and found to be in close agreement with experiment at pressures for which experimental data are available. Structural analyses indicate the buildup of an adsorbed, saturated CO2 film forming at a low pressure (20 bars) with properties similar to those of the bulk liquid, but preferential perpendicular alignment with respect to the interface. The CO2 monolayer buildup coincides with a reduced structuring of water molecules close to the interface. This study highlights the predictive nature of machine-learned potentials for complex macroscopic properties of biphasic interfaces, and the mechanistic insight obtained into carbon dioxide aggregation at the water interface is of high relevance for geoscience, climate research, and materials science. [ABSTRACT FROM AUTHOR]

The elasticities of double-stranded (ds) DNA and RNA, which are critical to their biological functions and applications in materials science, can be significantly modulated by solution conditions such as ions and temperature. However, there is still a lack of a comprehensive understanding of the role of solvents in the elasticities of dsRNA and dsDNA in a comparative way. In this work, we explored the effect of ethanol solvent on the elasticities of dsRNA and dsDNA by magnetic tweezers and all-atom molecular dynamics simulations. We found that the bending persistence lengths and contour lengths of dsRNA and dsDNA decrease monotonically with the increase in ethanol concentration. Furthermore, the addition of ethanol weakens the positive twist–stretch coupling of dsRNA, while promotes the negative twist–stretch coupling of dsDNA. Counter-intuitively, the lower dielectric environment of ethanol causes a significant re-distribution of counterions and enhanced ion neutralization, which overwhelms the enhanced repulsion along dsRNA/dsDNA, ultimately leading to the softening in bending for dsRNA and dsDNA. Moreover, for dsRNA, ethanol causes slight ion-clamping across the major groove, which weakens the major groove-mediated twist–stretch coupling, while for dsDNA, ethanol promotes the stretch–radius correlation due to enhanced ion binding and consequently enhances the helical radius-mediated twist–stretch coupling. [ABSTRACT FROM AUTHOR]

Point Group (PG) symmetries play a fundamental role in many aspects of theoretical chemistry and computational materials science. With the objective to automatize the search of PG symmetry operations of generic atomic clusters, we present a new algorithm called Symmetry Operation FInder (SOFI). SOFI addresses the problem of identifying PG symmetry by framing it as a degenerate shape-matching problem, where the multiple solutions correspond to distinct symmetry operations. The developed algorithm is compared against three other algorithms dedicated to PG identification on a large set of atomic clusters. The results, along with some illustrative use cases, showcase the effectiveness of SOFI. The SOFI algorithm is released as part of the iterative rotations and assignments library, accessible at https://github.com/mammasmias/IterativeRotationsAssignments. [ABSTRACT FROM AUTHOR]

The Rashba spin–orbit coupling effect, primarily arising from structural-inversion asymmetry in periodic crystals, has garnered considerable attention due to its tunability and potential applications in spintronics. Its capability to manipulate electron spin without an external magnetic field opens new avenues for spintronic device design, particularly in semiconductor technology. Within this framework, 2D Rashba materials hold special interest due to their inherent characteristics, which facilitate miniaturization and engineering capabilities. In this Perspective article, we provide an overview of recent advancements in the research of 2D Rashba materials, aiming to offer a comprehensive understanding of the diverse manifestations and multifaceted implications of the Rashba effect in material science. Rather than merely presenting a list of materials, our approach involves synthesizing various viewpoints, assessing current trends, and addressing challenges within the field. Our objective is to bridge the gap between fundamental research and practical applications by correlating each material with the necessary advancements required to translate theoretical concepts into tangible technologies. Furthermore, we highlight promising avenues for future research and development, drawing from insights gleaned from the current state of the field. [ABSTRACT FROM AUTHOR]

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Computationally hard tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their resources for simulation, analysis, and data processing. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.

A pressure-induced triclinic-to-monoclinic phase transition has been caught `in the act over a wider series of high-pressure synchrotron diffraction experiments conducted on a large, photoluminescent organo-gold(I) compound. Here, we describe the mechanism of this single-crystal-to-single-crystal phase transition, the onset of which occurs at ∼0.6 GPa, and we report a high-quality structure of the new monoclinic phase, refined using aspherical atomic scattering factors. Our case illustrates how conducting a fast series of diffraction experiments, enabled by modern equipment at synchrotron facilities, can lead to overestimation of the actual pressure of a phase transition due to slow transformation kinetics.

We present extensive new ab initio path integral Monte Carlo (PIMC) results for a variety of structural properties of warm dense hydrogen and beryllium. To deal with the fermion sign problem—an exponential computational bottleneck due to the antisymmetry of the electronic thermal density matrix—we employ the recently proposed [Y. Xiong and H. Xiong, J. Chem. Phys. 157, 094112 (2022); T. Dornheim et al., J. Chem. Phys. 159, 164113 (2023)] ξ-extrapolation method and find excellent agreement with the exact direct PIMC reference data where available. This opens up the intriguing possibility of studying a gamut of properties of light elements and potentially material mixtures over a substantial part of the warm dense matter regime, with direct relevance for astrophysics, material science, and inertial confinement fusion research. [ABSTRACT FROM AUTHOR]

Pressure-induced phase transformation in iron and its alloys is a classic research topic in solid-state physics, material science, and geophysics. The crystal structure of iron undergoes a phase transformation at a hydrostatic pressure of 13 GPa, changing from a body-centered cubic system to a hexagonal close-packed system. Although extensive research has been carried out on the transformation in iron by using molecular dynamics simulations, there is very limited literature that focuses on the contribution of parent phase orientations, system size, and impurities to the phase evolution. In this work, classic molecular dynamics simulations have been employed to investigate the effects of system size, lattice orientation, and impurity concentration on the pressure-induced phase transformation of iron and iron alloys for the first time. Our results show that the lattice orientation has a strong influence on the phase transition behavior, while the influence of carbon is small. The phase transition is slightly delayed with increasing carbon content, whereas the transition pressure increases from [001] to [011] to [111] orientation. The amount of twinning and stacking faults highly depends on the orientation. It is easiest for solitary waves to travel through [111] lattice orientation. The addition of carbon has a slow-down effect on shock velocities, and this effect increases with carbon content and lattice orientation of the samples from [001] to [011] to [111]. [ABSTRACT FROM AUTHOR]

In a recent work, we have briefly introduced a new structural index for water that, unlike previous indicators, was devised specifically for generic contexts beyond bulk conditions, making it suitable for hydration and nanoconfinement settings. In this work, we shall study this metric in detail, demonstrating its ability to reveal the existence of a fine-tuned interplay between the local structure and energetics in liquid water. This molecular principle enables the establishment of an extended hydrogen bond network, while simultaneously allowing for the existence of network defects by compensating for uncoordinated sites. By studying different water models and different temperatures encompassing both the normal liquid and the supercooled regime, this molecular mechanism will be shown to underlie the two-state behavior of bulk water. In addition, by studying functionalized self-assembled monolayers and diverse graphene-like surfaces, we shall show that this principle is also operative at hydration and nanoconfinement conditions, thus generalizing the validity of the two-liquid scenario of water to these contexts. This approach will allow us to define conditions for wettability, providing an accurate measure of hydrophobicity and a reliable predictor of filling and drying transitions. Hence, it might open the possibility of elucidating the active role of water in the broad fields of biophysics and materials science. As a preliminary step, we shall study the hydration structure and hydrophilicity of graphene-like systems (parallel graphene sheets and carbon nanotubes) as a function of the confinement dimensionality. [ABSTRACT FROM AUTHOR]

This document is a tribute to Dr. Wladyslaw Walukiewicz, a renowned scientist in the field of compound semiconductors, who passed away in 2022. Dr. Walukiewicz made significant contributions to multiple scientific topics throughout his career and brought together a global community of researchers. His work focused on understanding defects in semiconductors and their impact on material and device performance. The document highlights some of his key achievements, including his research on carrier mobilities, doping limits, band structures, and the discovery of the narrow band gap of InN. The document also mentions a special topic in the Journal of Applied Physics dedicated to Dr. Walukiewicz, which includes various papers that build on his concepts and insights. The authors of the document express their fond memories of Dr. Walukiewicz and his contributions to the scientific community. [Extracted from the article]

The enigmatic mechanism underlying unconventional high-temperature superconductivity, especially the role of lattice dynamics, has remained a subject of debate. Theoretical insights have long been hindered due to the lack of an accurate first-principles description of the lattice dynamics of cuprates. Recently, using the r2SCAN meta-generalized gradient approximation (meta-GGA) functional, we have been able to achieve accurate phonon spectra of an insulating cuprate YBa2Cu3O6 and discover significant magnetoelastic coupling in experimentally interesting Cu–O bond stretching optical modes [Ning et al., Phys. Rev. B 107, 045126 (2023)]. We extend this work by comparing Perdew–Burke–Ernzerhof and r2SCAN performances with corrections from the on-site Hubbard U and the D4 van der Waals (vdW) methods, aiming at further understanding on both the materials science side and the density functional side. We demonstrate the importance of vdW and self-interaction corrections for accurate first-principles YBa2Cu3O6 lattice dynamics. Since r2SCAN by itself partially accounts for these effects, the good performance of r2SCAN is now more fully explained. In addition, the performances of the Tao–Mo series of meta-GGAs, which are constructed in a different way from the strongly constrained and appropriately normed (SCAN) meta-GGA and its revised version r2SCAN, are also compared and discussed. [ABSTRACT FROM AUTHOR]

Density functional theory (DFT) stands as a cornerstone method in computational quantum chemistry and materials science due to its remarkable versatility and scalability. Yet, it suffers from limitations in accuracy, particularly when dealing with strongly correlated systems. To address these shortcomings, recent work has begun to explore how machine learning can expand the capabilities of DFT: an endeavor with many open questions and technical challenges. In this work, we present GradDFT a fully differentiable JAX-based DFT library, enabling quick prototyping and experimentation with machine learning-enhanced exchange–correlation energy functionals. GradDFT employs a pioneering parametrization of exchange–correlation functionals constructed using a weighted sum of energy densities, where the weights are determined using neural networks. Moreover, GradDFT encompasses a comprehensive suite of auxiliary functions, notably featuring a just-in-time compilable and fully differentiable self-consistent iterative procedure. To support training and benchmarking efforts, we additionally compile a curated dataset of experimental dissociation energies of dimers, half of which contain transition metal atoms characterized by strong electronic correlations. The software library is tested against experimental results to study the generalization capabilities of a neural functional across potential energy surfaces and atomic species, as well as the effect of training data noise on the resulting model accuracy. [ABSTRACT FROM AUTHOR]

The enigmatic mechanism underlying unconventional high-temperature superconductivity, especially the role of lattice dynamics, has remained a subject of debate. Theoretical insights have long been hindered due to the lack of an accurate first-principles description of the lattice dynamics of cuprates. Recently, using the r2SCAN meta-generalized gradient approximation (meta-GGA) functional, we have been able to achieve accurate phonon spectra of an insulating cuprate YBa2Cu3O6 and discover significant magnetoelastic coupling in experimentally interesting Cu–O bond stretching optical modes [Ning et al., Phys. Rev. B 107, 045126 (2023)]. We extend this work by comparing Perdew–Burke–Ernzerhof and r2SCAN performances with corrections from the on-site Hubbard U and the D4 van der Waals (vdW) methods, aiming at further understanding on both the materials science side and the density functional side. We demonstrate the importance of vdW and self-interaction corrections for accurate first-principles YBa2Cu3O6 lattice dynamics. Since r2SCAN by itself partially accounts for these effects, the good performance of r2SCAN is now more fully explained. In addition, the performances of the Tao–Mo series of meta-GGAs, which are constructed in a different way from the strongly constrained and appropriately normed (SCAN) meta-GGA and its revised version r2SCAN, are also compared and discussed. [ABSTRACT FROM AUTHOR]

The design of metallic glasses (MGs) with good properties is one of the long-standing bottlenecks in materials science and engineering, which has been relying mostly on far less efficient traditional trial-and-error methods. Even the currently popular machine learning-based forward designs, which use manual input to navigate high dimensional compositional space, often become inefficient with the increasing compositional complexity in MGs. Here, we developed an inverse design machine learning model, leveraging the variational autoencoder (VAE), to directly generate the MGs with good glass-forming ability (GFA). We demonstrate that our VAE with the property prediction model is not only an expressive generative model but also able to do accurate property prediction. Our model allows us to automatically generate novel MG compositions by performing simple operations in the latent space. After randomly generating 3000MG compositions using the model, a detailed analysis of four typical metallic alloys shows that unreported MG compositions with better glass-forming ability can be predicted. Moreover, our model facilitates the use of powerful optimization algorithms to efficiently guide the search for MGs with good GFA in the latent space. We believe that this is an efficient way to discover MGs with excellent properties. [ABSTRACT FROM AUTHOR]