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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 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.

Piezoelectric energy harvesting devices (PEHDs) for road installation are being researched as potentially low-emission electrical generators, but their life cycle greenhouse gas (GHG) emissions must be assessed to guide their design and implementation toward minimizing their carbon footprint. Parametric life cycle assessments (LCAs) of a PEHD, its generator, and the piezoelectric material were performed using data from a pilot-scale PEHD installation. The GHG emissions of the lead zirconate titanate (PZT) piezoelectric stack were determined to be lower than previously reported in the literature at 10.81-15.90 kg CO2eq/kg piezoelectric. The GHG emissions of electricity generated by a PEHD range from 48.65-588.63 g CO2eq/kWh, spanning from similar to renewables to slightly higher than electricity from natural gas. Sensitivity analysis indicated that spatial parameters and the device lifetime had the largest influence on the LCA results. PEHDs installed on roads show potential to be low-GHG emission energy systems.

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]

In this study, we observed natural methane (CH4) hydrate sediments, which are a type of unconventional natural gas resources, using x-ray computed tomography (CT). Because CH4 hydrates are formed by hydrogen bonding of water molecules with CH4, material decomposition becomes challenging when CH4 hydrates coexist with liquid or solid water in natural sediments. Tri-contrast (absorption, refraction, and scattering) imaging was performed via diffraction enhanced x-ray CT optics using monochromatic synchrotron x rays. The quantitative characterization of the contrast changes successfully enabled the decomposition of CH4 hydrates coexisting with frozen seawater (ice) in natural sediments obtained from the Okhotsk Sea. This study reveals complementary structural information about the microtexture and spatial relation among CH4 hydrates, ice, and pores by utilizing the distinct physical properties of x rays when passing through the materials. These results highlight the exceptional capabilities of high-resolution multicontrast x-ray tomography in materials science and geoscience applications. [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]

The dynamics of a soft particle suspended in a viscous fluid can be changed by the presence of an elastic boundary. Understanding the mechanisms and dynamics of soft–soft surface interactions can provide valuable insights into many important research fields, including biomedical engineering, soft robotics development, and materials science. This work investigates the anomalous transport properties of a soft nanoparticle near a visco-elastic interface, where the particle consists of a polymer assembly in the form of a micelle and the interface is represented by a lipid bilayer membrane. Mesoscopic simulations using a dissipative particle dynamics model are performed to examine the impact of micelle's proximity to the membrane on its Brownian motion. Two different sizes are considered, which correspond to ≈ 10 − 20 n m in physical units. The wavelengths typically seen by the largest micelle fall within the range of wavenumbers where the Helfrich model captures fairly well the bilayer mechanical properties. Several independent simulations allowed us to compute the micelle trajectories during an observation time smaller than the diffusive time scale (whose order of magnitude is similar to the membrane relaxation time of the largest wavelengths), this time scale being hardly accessible by experiments. From the probability density function of the micelle normal position with respect to the membrane, it is observed that the position remains close to the starting position during ≈ 0.05 τ d (where τd corresponds to the diffusion time), which allowed us to compare the negative excess of mean-square displacement (MSD) to existing theories. In that time range, the MSD exhibits different behaviors along parallel and perpendicular directions. When the micelle is sufficiently close to the bilayer (its initial distance from the bilayer equals approximately twice its gyration radius), the micelle motion becomes quickly subdiffusive in the normal direction. Moreover, the temporal evolution of the micelle MSD excess in the perpendicular direction follows that of a nanoparticle near an elastic membrane. However, in the parallel direction, the MSD excess is rather similar to that of a nanoparticle near a liquid interface. [ABSTRACT FROM AUTHOR]

Low-crosslinked polymer networks have recently been found to behave auxetically when subjected to small tensions, that is, their Poisson's ratio ν becomes negative. In addition, for specific state points, numerical simulations revealed that diamond-like networks reach the limit of mechanical stability, exhibiting values of ν = −1, a condition that we define as hyper-auxeticity. This behavior is interesting per se for its consequences in materials science but is also appealing for fundamental physics because the mechanical instability is accompanied by evidence of criticality. In this work, we deepen our understanding of this phenomenon by performing a large set of equilibrium and stress–strain simulations in combination with phenomenological elasticity theory. The two approaches are found to be in good agreement, confirming the above results. We also extend our investigations to disordered polymer networks and find that the hyper-auxetic behavior also holds in this case, still manifesting a similar critical-like behavior as in the diamond one. Finally, we highlight the role of the number density, which is found to be a relevant control parameter determining the elastic properties of the system. The validity of the results under disordered conditions paves the way for an experimental investigation of this phenomenon in real systems, such as hydrogels. [ABSTRACT FROM AUTHOR]

Interfacial water is ubiquitous on Earth, playing a crucial role in biology, chemistry, physics, materials science, and environmental science. Multiscale, hierarchical water motions on the surface of different materials under different conditions (temperature, hydration extent, pressure, etc.) and the coupling of this motion with the substrate/solute dynamics and the influence of these couplings on the material functions are complex, long lasting, interdisciplinary research topics. We herein focus on the coupling between the picosecond dynamical onset of substrates and their surface water at temperatures lower than the freezing point (273 K) and discuss the recent progress in the study of its molecular mechanisms. [ABSTRACT FROM AUTHOR]

The characteristic x-ray emission direction of a material indicates the direction of the bonding orbitals and spatial symmetry of the electron orbitals. Accordingly, the intensity of x-ray emission, which varies with the direction of emission and crystal orientation, provides crucial information regarding anisotropic electronic structures. This study utilized angle-resolved soft x-ray emission spectroscopy (SXES) on a layered material, NaAlSi, to ascertain the spatial distribution of the valence electrons. Distinct alterations in the spectral intensity distributions were observed in the Al–L2,3 and Si–L2,3 spectra with respect to the emission angle. To interpret the anisotropic SXES spectra, the spatial distribution of each valence electronic state was simulated using first-principle calculations. Although the anisotropic emission intensity could not explain the symmetry of the spatial distributions of the isolated s and d atomic orbitals, the anisotropy of the SXES spectra could be interpreted as the spatial distribution of these orbitals when hybridized with p orbitals. Furthermore, the spectral structure corresponding to the electronic states near the Fermi level reflected the characteristics of the d orbitals. Therefore, angle-resolved SXES measurements can effectively discern the spatial distribution of hybridized electron orbitals with specific energy levels, which could enhance techniques related to electron distribution analysis, with potential applications in material science and electronic structure characterization. [ABSTRACT FROM AUTHOR]

Embodied emissions from the production of building materials account for 17% of Chinas carbon dioxide (CO2) emissions and are important to focus on as China aims to achieve its carbon neutrality goals. However, there is a lack of systematic assessments on embodied emissions reduction potential of building materials that consider both the heterogeneous industrial characteristics as well as the Chinese buildings sector context. Here, we developed an integrated model that combines future demand of building materials in China with the strategies to reduce CO2 emissions associated with their production, using, and recycling. We found that measures to improve material efficiency in the value-chain has the largest CO2 mitigation potential before 2030 in both Low Carbon and Carbon Neutrality Scenarios, and continues to be significant through 2060. Policies to accelerate material efficiency practices, such as incorporating embodied emissions in building codes and conducting robust research, development, and demonstration (RD&D) in carbon removal are critical.

Machine-learned interatomic potentials are fast becoming an indispensable tool in computational materials science. One approach is the ephemeral data-derived potential (EDDP), which was designed to accelerate atomistic structure prediction. The EDDP is simple and cost-efficient. It relies on training data generated in small unit cells and is fit using a lightweight neural network, leading to smooth interactions which exhibit the robust transferability essential for structure prediction. Here, we present a variety of applications of EDDPs, enabled by recent developments of the open-source EDDP software. New features include interfaces to phonon and molecular dynamics codes, as well as deployment of the ensemble deviation for estimating the confidence in EDDP predictions. Through case studies ranging from elemental carbon and lead to the binary scandium hydride and the ternary zinc cyanide, we demonstrate that EDDPs can be trained to cover wide ranges of pressures and stoichiometries, and used to evaluate phonons, phase diagrams, superionicity, and thermal expansion. These developments complement continued success in accelerated structure prediction. [ABSTRACT FROM AUTHOR]

Realizing an oxide thin film with proper stoichiometry is one of the most challenging objects in materials science. Owing to the growth dynamics as well as kinetics, the physical properties of thin films often differ from their bulk counterparts. Here, we report pulsed laser-deposited thin films of LaBO3 (B = Mn, Fe, Co) grown on a SrTiO3 (001) substrate under various thermodynamic conditions. Structural, magnetic, and optical studies have been carried out. The x-ray diffraction study confirms that an appropriate choice of growth thermodynamics may help one to realize epitaxially grown films on the SrTiO3 substrate with out-of-plane lattice parameters 3.976, 3.984, and 3.825 Å for LaMnO3 (LMO), LaFeO3 (LFO), and LaCoO3 (LCO), respectively. A mixed valence state of Mn2+, 3+, 4+ for LMO, a Fe3+ state for LFO, and a mixed state of Co2+, 3+ for LCO have been confirmed by x-ray photoelectron spectroscopy, which is in good agreement with the Ellingham diagram. The optical study showed a bandgap of 1.2, 2.5, and 1.5 eV for LMO, LFO, and LCO, respectively. Ultraviolet photoelectron spectroscopy (UPS) shows a glimpse of the valence band maximum and Fermi level position. UV, UPS, and photoconductive study simultaneously results in a type II band bending, i.e., staggered type bending is observed at these interfaces. Room temperature weak ferromagnetism along with the insulating nature and a sign of photovoltaic application of these thin films fascinate to carry forward rigorous study from fundamental as well as technological points of view. [ABSTRACT FROM AUTHOR]

Catalyzed by enormous success in the industrial sector, many research programs have been exploring data-driven, machine learning approaches. Performance can be poor when the model is extrapolated to new regions of chemical space, e.g., new bonding types, new many-body interactions. Another important limitation is the spatial locality assumption in model architecture, and this limitation cannot be overcome with larger or more diverse datasets. The outlined challenges are primarily associated with the lack of electronic structure information in surrogate models such as interatomic potentials. Given the fast development of machine learning and computational chemistry methods, we expect some limitations of surrogate models to be addressed in the near future; nevertheless spatial locality assumption will likely remain a limiting factor for their transferability. Here, we suggest focusing on an equally important effort—design of physics-informed models that leverage the domain knowledge and employ machine learning only as a corrective tool. In the context of material science, we will focus on semi-empirical quantum mechanics, using machine learning to predict corrections to the reduced-order Hamiltonian model parameters. The resulting models are broadly applicable, retain the speed of semiempirical chemistry, and frequently achieve accuracy on par with much more expensive ab initio calculations. These early results indicate that future work, in which machine learning and quantum chemistry methods are developed jointly, may provide the best of all worlds for chemistry applications that demand both high accuracy and high numerical efficiency. [ABSTRACT FROM AUTHOR]

Micelles of anionic or zwitterionic surfactants produced depletion interactions between nanofibrils leading to weak gels, while cationic surfactants adsorbed to the nanofibrils, screening repulsions leading to tough gels or phase separation. Are fundamentally and technologically interesting due to their diverse building blocks, network-supporting attractive forces, and gelation pathways. As described below, the 29 articles that appear in this issue shed new light on a wide variety of related topics, including limited valence networks, bridged colloidal networks and coagulant gels, depletant-mediated gels, I in situ i structural characterization of gel aging, mechanical and rheological properties of colloidal networks, hybrid and deformable particle assemblies, and nanocrystal gels with tunable optical response. Bridged networks and coagulant gels Colloidal networks formed by nonspecific bridging polymers represent another interesting class of gel formers, although it can be challenging to design dispersible systems with tunable polymer-colloid interactions. [Extracted from the article]