Hasil untuk "Physical and theoretical chemistry"

C. Vogt, B. Weckhuysen

E. Vogler

Yuting Wang, Changhong Wang, Mengyang Li et al.

Excessive nitrate ions in the environment break the natural nitrogen cycle and become a significant threat to human health. So far, many physical, chemical, and biological techniques have been developed for nitrate remediation, but most of them require high post-processing costs and rigorous treatment conditions. In contrast, nitrate electroreduction is promising because it utilizes green electrons as reductants under ambient conditions. The recognition and mastering of the nitrate reaction mechanism is the premise for the design and synthesis of efficient electrocatalysts for the selective reduction of nitrate. In this regard, this review aims to provide an insight into the electrocatalytic mechanism of nitrate reduction, especially combined with in situ electrochemical characterization and theoretical calculations over different kinds of materials. Moreover, the performance evaluation parameters and standard test methods for nitrate electroreduction are summarized to screen efficient materials. Finally, an outlook on the current challenges and promising opportunities in this research area is discussed. This review provides a guide for development of electrocatalysts for selective nitrate reduction with a fascinating performance and accelerates the development of sustainable nitrogen chemistry and engineering.

M. Nisoli, P. Decleva, F. Calegari et al.

Hsin-Yuan Huang, R. Kueng, G. Torlai et al.

Classical machine learning (ML) provides a potentially powerful approach to solving challenging quantum many-body problems in physics and chemistry. However, the advantages of ML over traditional methods have not been firmly established. In this work, we prove that classical ML algorithms can efficiently predict ground-state properties of gapped Hamiltonians after learning from other Hamiltonians in the same quantum phase of matter. By contrast, under a widely accepted conjecture, classical algorithms that do not learn from data cannot achieve the same guarantee. We also prove that classical ML algorithms can efficiently classify a wide range of quantum phases. Extensive numerical experiments corroborate our theoretical results in a variety of scenarios, including Rydberg atom systems, two-dimensional random Heisenberg models, symmetry-protected topological phases, and topologically ordered phases. Description Learning many-body behavior Predicting the properties of strongly interacting many-body quantum systems is notoriously difficult. One approach is to use quantum computers, but at the current stage of the technology, the most interesting problems are still out of reach. Huang et al. explored a different technique: using classical machine learning to learn from experimental data and then applying that knowledge to predict physical properties or classify phases of matter for specific types of many-body problems. The authors show that under certain conditions, the algorithm is computationally efficient. —JS A classical machine learning algorithm is proven to be computationally efficient for some many-body problems. INTRODUCTION Solving quantum many-body problems, such as finding ground states of quantum systems, has far-reaching consequences for physics, materials science, and chemistry. Classical computers have facilitated many profound advances in science and technology, but they often struggle to solve such problems. Scalable, fault-tolerant quantum computers will be able to solve a broad array of quantum problems but are unlikely to be available for years to come. Meanwhile, how can we best exploit our powerful classical computers to advance our understanding of complex quantum systems? Recently, classical machine learning (ML) techniques have been adapted to investigate problems in quantum many-body physics. So far, these approaches are mostly heuristic, reflecting the general paucity of rigorous theory in ML. Although they have been shown to be effective in some intermediate-size experiments, these methods are generally not backed by convincing theoretical arguments to ensure good performance. RATIONALE A central question is whether classical ML algorithms can provably outperform non-ML algorithms in challenging quantum many-body problems. We provide a concrete answer by devising and analyzing classical ML algorithms for predicting the properties of ground states of quantum systems. We prove that these ML algorithms can efficiently and accurately predict ground-state properties of gapped local Hamiltonians, after learning from data obtained by measuring other ground states in the same quantum phase of matter. Furthermore, under a widely accepted complexity-theoretic conjecture, we prove that no efficient classical algorithm that does not learn from data can achieve the same prediction guarantee. By generalizing from experimental data, ML algorithms can solve quantum many-body problems that could not be solved efficiently without access to experimental data. RESULTS We consider a family of gapped local quantum Hamiltonians, where the Hamiltonian H(x) depends smoothly on m parameters (denoted by x). The ML algorithm learns from a set of training data consisting of sampled values of x, each accompanied by a classical representation of the ground state of H(x). These training data could be obtained from either classical simulations or quantum experiments. During the prediction phase, the ML algorithm predicts a classical representation of ground states for Hamiltonians different from those in the training data; ground-state properties can then be estimated using the predicted classical representation. Specifically, our classical ML algorithm predicts expectation values of products of local observables in the ground state, with a small error when averaged over the value of x. The run time of the algorithm and the amount of training data required both scale polynomially in m and linearly in the size of the quantum system. Our proof of this result builds on recent developments in quantum information theory, computational learning theory, and condensed matter theory. Furthermore, under the widely accepted conjecture that nondeterministic polynomial-time (NP)–complete problems cannot be solved in randomized polynomial time, we prove that no polynomial-time classical algorithm that does not learn from data can match the prediction performance achieved by the ML algorithm. In a related contribution using similar proof techniques, we show that classical ML algorithms can efficiently learn how to classify quantum phases of matter. In this scenario, the training data consist of classical representations of quantum states, where each state carries a label indicating whether it belongs to phase A or phase B. The ML algorithm then predicts the phase label for quantum states that were not encountered during training. The classical ML algorithm not only classifies phases accurately, but also constructs an explicit classifying function. Numerical experiments verify that our proposed ML algorithms work well in a variety of scenarios, including Rydberg atom systems, two-dimensional random Heisenberg models, symmetry-protected topological phases, and topologically ordered phases. CONCLUSION We have rigorously established that classical ML algorithms, informed by data collected in physical experiments, can effectively address some quantum many-body problems. These rigorous results boost our hopes that classical ML trained on experimental data can solve practical problems in chemistry and materials science that would be too hard to solve using classical processing alone. Our arguments build on the concept of a succinct classical representation of quantum states derived from randomized Pauli measurements. Although some quantum devices lack the local control needed to perform such measurements, we expect that other classical representations could be exploited by classical ML with similarly powerful results. How can we make use of accessible measurement data to predict properties reliably? Answering such questions will expand the reach of near-term quantum platforms. Classical algorithms for quantum many-body problems. Classical ML algorithms learn from training data, obtained from either classical simulations or quantum experiments. Then, the ML algorithm produces a classical representation for the ground state of a physical system that was not encountered during training. Classical algorithms that do not learn from data may require substantially longer computation time to achieve the same task.

C. Wöll

Xuchen Zheng, Siao Chen, Jinze Li et al.

Graphdiyne (GDY), a rising star of carbon allotropes, features a two-dimensional all-carbon network with the cohybridization of sp and sp2 carbon atoms and represents a trend and research direction in the development of carbon materials. The sp/sp2-hybridized structure of GDY endows it with numerous advantages and advancements in controlled growth, assembly, and performance tuning, and many studies have shown that GDY has been a key material for innovation and development in the fields of catalysis, energy, photoelectric conversion, mode conversion and transformation of electronic devices, detectors, life sciences, etc. In the past ten years, the fundamental scientific issues related to GDY have been understood, showing differences from traditional carbon materials in controlled growth, chemical and physical properties and mechanisms, and attracting extensive attention from many scientists. GDY has gradually developed into one of the frontiers of chemistry and materials science, and has entered the rapid development period, producing large numbers of fundamental and applied research achievements in the fundamental and applied research of carbon materials. For the exploration of frontier scientific concepts and phenomena in carbon science research, there is great potential to promote progress in the fields of energy, catalysis, intelligent information, optoelectronics, and life sciences. In this review, the growth, self-assembly method, aggregation structure, chemical modification, and doping of GDY are shown, and the theoretical calculation and simulation and fundamental properties of GDY are also fully introduced. In particular, the applications of GDY and its formed aggregates in catalysis, energy storage, photoelectronic, biomedicine, environmental science, life science, detectors, and material separation are introduced.

V. Krishnamurthy, George K. Kaufman, Adam R Urbach et al.

E. Pensa, E. Cortés, Gastón Corthey et al.

Over the last three decades, self-assembled molecular films on solid surfaces have attracted widespread interest as an intellectual and technological challenge to chemists, physicists, materials scientists, and biologists. A variety of technological applications of nanotechnology rely on the possibility of controlling topological, chemical, and functional features at the molecular level. Self-assembled monolayers (SAMs) composed of chemisorbed species represent fundamental building blocks for creating complex structures by a bottom-up approach. These materials take advantage of the flexibility of organic and supramolecular chemistry to generate synthetic surfaces with well-defined chemical and physical properties. These films already serve as structural or functional parts of sensors, biosensors, drug-delivery systems, molecular electronic devices, protecting capping for nanostructures, and coatings for corrosion protection and tribological applications. Thiol SAMs on gold are the most popular molecular films because the resulting oxide-free, clean, flat surfaces can be easily modified both in the gas phase and in liquid media under ambient conditions. In particular, researchers have extensively studied SAMs on Au(111) because they serve as model systems to understand the basic aspects of the self-assembly of organic molecules on well-defined metal surfaces. Also, great interest has arisen in the surface structure of thiol-capped gold nanoparticles (AuNPs) because of simple synthesis methods that produce highly monodisperse particles with controllable size and a high surface/volume ratio. These features make AuNPs very attractive for technological applications in fields ranging from medicine to heterogeneous catalysis. In many applications, the structure and chemistry of the sulfur-gold interface become crucial since they control the system properties. Therefore, many researchers have focused on understanding of the nature of this interface on both planar and nanoparticle thiol-covered surfaces. However, despite the considerable theoretical and experimental efforts made using various sophisticated techniques, the structure and chemical composition of the sulfur-gold interface at the atomic level remains elusive. In particular, the search for a unified model of the chemistry of the S-Au interface illustrates the difficulty of determining the surface chemistry at the nanoscale. This Account provides a state-of-the-art analysis of this problem and raises some questions that deserve further investigation.

M. Ruggenthaler, N. Tancogne-Dejean, Johannes Flick et al.

S. Shinde, Nayantara K. Wagh, Sung-Hae Kim et al.

Solid‐state batteries (SSBs) have received significant attention due to their high energy density, reversible cycle life, and safe operations relative to commercial Li‐ion batteries using flammable liquid electrolytes. This review presents the fundamentals, structures, thermodynamics, chemistries, and electrochemical kinetics of desirable solid electrolyte interphase (SEI) required to meet the practical requirements of reversible anodes. Theoretical and experimental insights for metal nucleation, deposition, and stripping for the reversible cycling of metal anodes are provided. Ion transport mechanisms and state‐of‐the‐art solid‐state electrolytes (SEs) are discussed for realizing high‐performance cells. The interface challenges and strategies are also concerned with the integration of SEs, anodes, and cathodes for large‐scale SSBs in terms of physical/chemical contacts, space‐charge layer, interdiffusion, lattice‐mismatch, dendritic growth, chemical reactivity of SEI, current collectors, and thermal instability. The recent innovations for anode interface chemistries developed by SEs are highlighted with monovalent (lithium (Li+), sodium (Na+), potassium (K+)) and multivalent (magnesium (Mg2+), zinc (Zn2+), aluminum (Al3+), calcium (Ca2+)) cation carriers (i.e., lithium‐metal, lithium‐sulfur, sodium‐metal, potassium‐ion, magnesium‐ion, zinc‐metal, aluminum‐ion, and calcium‐ion batteries) compared to those of liquid counterparts.

Buttard, Floris, Besset, Tatiana

In the quest for molecular complexity, the direct and selective functionalization of C(sp3) centers is of paramount importance. As sulfur- and/or fluorine-containing molecules are particularly important, the development of methods to (regio)selectively introduce moieties containing these atoms on a C(sp3) center by direct C–H bond functionalization or decarboxylative reaction has garnered our interest in the last few years. Given the challenges that the direct functionalization of C(sp3) centers represents, this account summarizes our recent contributions thanks to strategies based on transition metal catalysis and photochemistry.

Shinya KISHIOKA

A voltabsorptmetric study of redox properties of methyl viologen in an optically transparent thin-layer electrode cell was performed. The obtained UV-visible absorption spectra were analyzed using a higher-order derivative conversion (HODC) method. The superimposed absorption bands assigned to the radical cation of the reduced form (MV+·) and its dimer ((MV+·)2) were resolved using the HODC technique. The HODC method was shown to be effective for the analysis of the electrode reaction of the compound with viologen unit.

Sergei Manzhos, Johann Lüder, Pavlo Golub et al.

Machine learning (ML) of kinetic energy functionals (KEF) for orbital-free density functional theory (DFT) holds the promise of addressing an important bottleneck in large-scale ab initio materials modeling where sufficiently accurate analytic KEFs are lacking. However, ML models are not as easily handled as analytic expressions; they need to be provided in the form of algorithms and associated data. Here, we bridge the two approaches and construct an analytic expression for a KEF guided by interpretative ML of crystal cell-averaged kinetic energy densities ( ${\bar{\tau}}$ ) of several hundred materials. A previously published dataset including multiple phases of 433 unary, binary, and ternary compounds containing Li, Al, Mg, Si, As, Ga, Sb, Na, Sn, P, and In was used for training, including data at the equilibrium geometry as well as strained structures. A hybrid Gaussian process regression—neural network method was used to understand the type of functional dependence of $\overline\tau$ on the features which contained cell-averaged terms of the 4th order gradient expansion and the product of the electron density and Kohn–Sham (KS) effective potential. Based on this analysis, an analytic model is constructed that can reproduce KS DFT energy–volume curves with sufficient accuracy (pronounced minima that are sufficiently close to the minima of the Kohn–Sham DFT-based curves and with sufficiently close curvatures) to enable structure optimizations and elastic response calculations.

Mariana B. M. S. Medeiros, Josenilton N. Sousa, Manuela S. Arruda et al.

Zhong Yang, Xianglin Xiang, Jian Yang et al.

High-entropy oxides (HEOs), with their multi-principal-element compositional diversity, have emerged as promising candidates in the realm of energy materials. This review encapsulates the progress in harnessing HEOs for energy conversion and storage applications, encompassing solar cells, electrocatalysis, photocatalysis, lithium-ion batteries, and solid oxide fuel cells. The critical role of theoretical calculations and simulations is underscored, highlighting their contribution to elucidating material stability, deciphering structure-activity relationships, and enabling performance optimization. These computational tools have been instrumental in multi-scale modeling, high-throughput screening, and integrating artificial intelligence for material design. Despite their promise, challenges such as fabrication complexity, cost, and theoretical computational hurdles impede the broad application of HEOs. To address these, this review delineates future research perspectives. These include the innovation of cost-effective synthesis strategies, employment of in situ characterization for micro-chemical insights, exploration of unique physical phenomena to refine performance, and enhancement of computational models for precise structure-performance predictions. This review calls for interdisciplinary synergy, fostering a collaborative approach between materials science, chemistry, physics, and related disciplines. Collectively, these efforts are poised to propel HEOs towards commercial viability in the new energy technologies, heralding innovative solutions to pressing energy and environmental challenges.

O. Smits, C. E. Düllmann, Paul Indelicato et al.

Siyuan He

The competitive pressures in China's primary and secondary education system have persisted despite decades of policy interventions aimed at reducing academic burdens and alleviating parental anxiety. This paper develops a game-theoretic model to analyze the strategic interactions among families in this system, revealing how competition escalates into a socially irrational "education arms race." Through equilibrium analysis and simulations, the study demonstrates the inherent trade-offs between education equity and social welfare, alongside the policy failures arising from biased social cognition. The model is further extended using Spence's signaling framework to explore the inefficiencies of the current system and propose policy solutions that address these issues.

Ronald Katende

We establish a unified theoretical framework addressing the stability, consistency, and convergence of neural networks under realistic training conditions, specifically, in the presence of non-IID data, geometric constraints, and embedded physical laws. For standard supervised learning with dependent data, we derive uniform stability bounds for gradient-based methods using mixing coefficients and dynamic learning rates. In federated learning with heterogeneous data and non-Euclidean parameter spaces, we quantify model inconsistency via curvature-aware aggregation and information-theoretic divergence. For Physics-Informed Neural Networks (PINNs), we rigorously prove perturbation stability, residual consistency, Sobolev convergence, energy stability for conservation laws, and convergence under adaptive multi-domain refinements. Each result is grounded in variational analysis, compactness arguments, and universal approximation theorems in Sobolev spaces. Our theoretical guarantees are validated across parabolic, elliptic, and hyperbolic PDEs, confirming that residual minimization aligns with physical solution accuracy. This work offers a mathematically principled basis for designing robust, generalizable, and physically coherent neural architectures across diverse learning environments.

Javiera K. Díaz-Berríos, Viviana V. Guzmán, Catherine Walsh et al.

Most stars are born in stellar clusters and their protoplanetary disks, which are the birthplaces of planets, can therefore be affected by the radiation of nearby massive stars. However, little is known about the chemistry of externally irradiated disks, including whether or not their properties are similar to the so-far better-studied isolated disks. Motivated by this question, we present ALMA Band 6 observations of two irradiated Class II protoplanetary disks in the outskirts of the Orion Nebula Cluster (ONC) to explore the chemical composition of disks exposed to (external) FUV radiation fields: the 216-0939 disk and the binary system 253-1536A/B, which are exposed to radiation fields of $10^2-10^3$ times the average interstellar radiation field. We detect lines from CO isotopologues, HCN, H$_2$CO, and C$_2$H toward both protoplanetary disks. Based on the observed disk-integrated line fluxes and flux ratios, we do not find significant differences between isolated and irradiated disks. The observed differences seem to be more closely related to the different stellar masses than to the external radiation field. This suggests that these disks are far enough away from the massive Trapezium stars, that their chemistry is no longer affected by external FUV radiation. Additional observations towards lower-mass disks and disks closer to the massive Trapezium stars are required to elucidate the level of external radiation required to make an impact on the chemistry of planet formation in different kinds of disks.