Hasil untuk "Physical and theoretical chemistry"

Azeem Ghulam Nabi, Maryam Hayat, Shahbaz Khan et al.

The optical properties of doped SrTiO3 are crucial for solar energy conversion due to their correlation with their efficacy to absorb and convert sunlight to energy. In this study, the impact of La, Co, Cr, Sc, and Ir substitutions on the structural, optical, electrical, and photocatalytic properties of SrTiO3 were investigated by a series density functional theory (DFT) calculation. Analyses primarily initially focused on the effects of doping and co-doping with Lanthanum (La) followed by systematic investigations of the impact of transition metal (TM) doping with Scandium Chromium, Cobalt and Iridium (Sc, Cr, Co, Ir) an finally co-doping with La and the TM elements. Co-doping leads to a reduction in the bandgap energy and a shift in the bandgap region, making the material more suitable for photo-catalysis. Structures singly-substituted with La, Sc, Cr, Co, and Ir primarily absorbed light in the ultraviolet region, which limits their use in light-based devices. However, SrTiO₃ systems co-doped with La-Ir exhibited significant absorption in the visible region (∼400–750 nm). The co-doped SrTiO₃ maximizes solar light utilization, making it well-suited for applications such as solar cells. Our study sheds light into the optical properties of doped SrTiO₃, highlighting its potential for practical use in solar energy conversion.

Guohong SHEN, Shinji KONDOU, Hiroki NAKAGAKI et al.

Surfactants possess unique properties in bulk solutions and at interfaces, naturally forming self-assembled structures. Herein, cetyltrimethylammonium trifluoroacetate (CTATFA) was incorporated into aqueous electrolytes as a cationic surfactant to enhance their ionic conductivity and electrochemical stability. The presence of CTATFA widened the electrochemical stability windows of both Li-based and Zn-based electrolytes. The Zn-based electrolyte exhibited high ionic conductivity and low viscosity in the bulk solution. In a Zn symmetric cell, the electrolyte containing 1 M Zn(TFA)2-0.5 M CTATFA demonstrated excellent Zn plating/stripping reversibility for over 800 h at 1 mAh cm−2 and 1 mA cm−2. A Zn-Cu cell with 1 M Zn(TFA)2-0.5 M CTATFA exhibited excellent reversibility, achieving over 300 plating/stripping cycles at 5 mA cm−2 and 5 mAh cm−2. The Zn/MnO2 cell using the Zn-based electrolyte also demonstrated a specific capacity of 105 mAh g−1 over 750 cycles at a current density of 0.5 A g−1. This study provides insight into the design of high-performance aqueous electrolytes based on the self-assembly and surface adsorption of cationic surfactants.

Daniel J. H. Chung, Zhiqi Gao, Yurii Kvasiuk et al.

We introduce a benchmark to evaluate the capability of AI to solve problems in theoretical physics, focusing on high-energy theory and cosmology. The first iteration of our benchmark consists of 57 problems of varying difficulty, from undergraduate to research level. These problems are novel in the sense that they do not come from public problem collections. We evaluate our data set on various open and closed language models, including o3-mini, o1, DeepSeek-R1, GPT-4o and versions of Llama and Qwen. While we find impressive progress in model performance with the most recent models, our research-level difficulty problems are mostly unsolved. We address challenges of auto-verifiability and grading, and discuss common failure modes. While currently state-of-the art models are still of limited use for researchers, our results show that AI assisted theoretical physics research may become possible in the near future. We discuss the main obstacles towards this goal and possible strategies to overcome them. The public problems and solutions, results for various models, and updates to the data set and score distribution, are available on the website of the dataset tpbench.org.

Nikolay V. Golubev, Mohammed Th. Hassan

How quantum electron and nuclei motions affect biomolecular chemical reactions remains a central challengeable question at the interface of quantum chemistry and biology. Ultrafast charge migration in deoxyribonucleic acid (DNA) has long been hypothesized to play a critical role in photochemistry, genome stability, and long-range biomolecular signaling, however, direct real-time observation of these electronic processes has remained elusive. Here, we present a theoretical investigation and propose the concept of future experimental measurements of laser-driven charge dynamics in the canonical DNA nucleobase pairs thymine_adenine and cytosine_guanine. Attosecond-resolved simulations employing high-level ab initio methods reveal base-dependent ionization mechanisms, directional charge migration pathways, and electronic coherences that govern sub-femtosecond redistribution of electron density across hydrogen-bonded nucleobase interfaces. Accordingly, we propose the concept of a quantum attosecond scanning electron microscope, termed the quantum attomicroscope (Q-attomicroscope), a capable of imaging photoinduced quantum chemistry reactions in attosecond temporal resolution and sub-nanometer spatial precision. As a proof of principle, we propose to image the charge migrations dynamics in DNA which we studied theoretically. Together, our preceptive bridges theory, instrumentation, and control, outlining a pathway toward laser mediated manipulation of DNA structure with implications for repair processes, chemical reactivity, and future personalized medicine.

Pratyush Tiwary, Lukas Herron, Richard John et al.

The recent surge in Generative Artificial Intelligence (AI) has introduced exciting possibilities for computational chemistry. Generative AI methods have made significant progress in sampling molecular structures across chemical species, developing force fields, and speeding up simulations. This Perspective offers a structured overview, beginning with the fundamental theoretical concepts in both Generative AI and computational chemistry. It then covers widely used Generative AI methods, including autoencoders, generative adversarial networks, reinforcement learning, flow models and language models, and highlights their selected applications in diverse areas including force field development, and protein/RNA structure prediction. A key focus is on the challenges these methods face before they become truly predictive, particularly in predicting emergent chemical phenomena. We believe that the ultimate goal of a simulation method or theory is to predict phenomena not seen before, and that Generative AI should be subject to these same standards before it is deemed useful for chemistry. We suggest that to overcome these challenges, future AI models need to integrate core chemical principles, especially from statistical mechanics.

Vincent Fan, Yujie Qian, Alex Wang et al.

Information extraction from chemistry literature is vital for constructing up-to-date reaction databases for data-driven chemistry. Complete extraction requires combining information across text, tables, and figures, whereas prior work has mainly investigated extracting reactions from single modalities. In this paper, we present OpenChemIE to address this complex challenge and enable the extraction of reaction data at the document level. OpenChemIE approaches the problem in two steps: extracting relevant information from individual modalities and then integrating the results to obtain a final list of reactions. For the first step, we employ specialized neural models that each address a specific task for chemistry information extraction, such as parsing molecules or reactions from text or figures. We then integrate the information from these modules using chemistry-informed algorithms, allowing for the extraction of fine-grained reaction data from reaction condition and substrate scope investigations. Our machine learning models attain state-of-the-art performance when evaluated individually, and we meticulously annotate a challenging dataset of reaction schemes with R-groups to evaluate our pipeline as a whole, achieving an F1 score of 69.5%. Additionally, the reaction extraction results of \ours attain an accuracy score of 64.3% when directly compared against the Reaxys chemical database. We provide OpenChemIE freely to the public as an open-source package, as well as through a web interface.

Ben Van Dusen, Jayson Nissen, Odis Johnson

The success of collaborative instruction in helping students achieve higher grades in introductory science, technology, engineering, and mathematics (STEM) courses has led many educators and researchers to assume these methods also address inequities. However, little evidence tests this assumption. Structural inequities in our society have led to the chronic underrepresentation of Black, Hispanic, women, and first-generation students in STEM disciplines. Broadening participation from underrepresented groups in biology, chemistry, and physics would reduce social inequalities while harnessing diversity's economic impact on innovation and workforce expansion. We leveraged data on content knowledge from 18,791 students in 305 introductory courses using collaborative instruction at 45 institutions. We modeled student outcomes across the intersections of gender, race, ethnicity, and first-generation college status within and across science disciplines. Using these models, we examine the educational debts society owes college science students prior to instruction and whether instruction mitigates, perpetuates, or exacerbates those debts. The size of these educational debts and the extent to which courses added to or repaid these debts varied across disciplines. Across all three disciplines, society owed Black and Hispanic women and first-generation Black men the largest educational debts. Collaborative instructional strategies were not sufficient to repay society's educational debts.

S. Maes, F. De Ceuster, M. Van de Sande et al.

The chemistry of an astrophysical environment is closely coupled to its dynamics, the latter often found to be complex. Hence, to properly model these environments a 3D context is necessary. However, solving chemical kinetics within a 3D hydro simulation is computationally infeasible for a even a modest parameter study. In order to develop a feasible 3D hydro-chemical simulation, the classical chemical approach needs to be replaced by a faster alternative. We present mace, a Machine learning Approach to Chemistry Emulation, as a proof-of-concept work on emulating chemistry in a dynamical environment. Using the context of AGB outflows, we have developed an architecture that combines the use of an autoencoder (to reduce the dimensionality of the chemical network) and a set of latent ordinary differential equations (that are solved to perform the temporal evolution of the reduced features). Training this architecture with an integrated scheme makes it possible to successfully reproduce a full chemical pathway in a dynamical environment. mace outperforms its classical analogue on average by a factor 26. Furthermore, its efficient implementation in PyTorch results in a sub-linear scaling with respect to the number of hydrodynamical simulation particles.

Yu.A. Kuznetsov, M.N. Lapushkin

For the first time, the adsorption of germanium atoms on the (100) face of tungsten was calculated using the density functional theory. The tungsten substrate was made as a 2D layer. The W 2D layer was modeled by a W(100) 2×2×2 supercell. The calculation of the electron density of state and the adsorption energy of a Ge atom was carried out for three adsorption sites of the Ge atom: in the hollow position, in the bridge position between surface W atoms, and above the surface W atom: one Ge atom per 8 surface W atoms (most preferably adsorption of a germanium atom in hollow position). The adsorption energy is significant: 6,38 eV. The adsorption of Ge atoms leads to an insignificant reconstruction of the W surface: the maximum shift of W atoms does not exceed 0,15 Å. The valence band of the W(100) 2D layer is formed mainly by W 5d electrons, with an insignificant contribution of W 6s electrons. The Ge band is formed by Ge 4p electrons and Ge 4s electrons.

L.A. Bobreva, N.V. Sidorov, M.N. Palatnikov et al.

LiNbO3:Tb (0,1 wt.%), LiNbO3:Tb (0,48 wt.%), and LiNbO3:Tb (2,21 wt.%) crystals were studied by the infrared absorption spectroscopy in the area of valence vibrations of OH--groups. These crystals were grown by Czochralski method employing direct alloying of blend of the congruent composition. It was found that when the concentration of point defect centers of the cationic sublattice VLi was higher, than the concentration of impurity point defects TbLi, an absorption band with the frequency of 3487 cm-1 was registered in the IR spectrum. This absorption band is associated with the violation of stoichiometry and the formation of a complex defect (VLi - OН) in the LiNbO3:Tb (0,1 wt.%), and LiNbO3:Tb (0,48 wt.%) crystals. A further increase in the concentration of the alloying impurity leads to a change in the O-O bond length, which affects the OH-bond length and the appearance of a new absorption band with a frequency of 3493 cm-1, which corresponds to the complex defect (TbLi - OН) in the LiNbO3 crystal. Due to non-uniform admixture in the LiNbO3:Tb crystal, clusters are formed to which the absorption bands with frequencies in the range of from 3100-3403 cm-1 to 3510-3580 cm-1 in the spectrum.

Jakob Günther, Alberto Baiardi, Markus Reiher et al.

Quantum computation is one of the most promising new paradigms for the simulation of physical systems composed of electrons and atomic nuclei, with applications in chemistry, solid-state physics, materials science, and molecular biology. This requires a truncated representation of the electronic structure Hamiltonian using a finite number of orbitals. While it is, in principle, obvious how to improve on the representation by including more orbitals, this is usually unfeasible in practice (e.g., because of the limited number of qubits available) and severely compromises the accuracy of the obtained results. Here, we propose a quantum algorithm that improves on the representation of the physical problem by virtue of second-order perturbation theory. In particular, our quantum algorithm evaluates the second-order energy correction through a series of time-evolution steps under the unperturbed Hamiltonian. An important application is to go beyond the active-space approximation, allowing to include corrections of virtual orbitals, known as multireference perturbation theory. Here, we exploit that the unperturbed Hamiltonian is diagonal for virtual orbitals and show that the number of qubits is independent of the number of virtual orbitals. This gives rise to more accurate energy estimates without increasing the number of qubits. Moreover, we demonstrate numerically for realistic chemical systems that the total runtime has highly favorable scaling in the number of virtual orbitals compared to previous work. Numerical calculations confirm the necessity of the multireference perturbation theory energy corrections to reach accurate ground state energy estimates. Our perturbation theory quantum algorithm can also be applied to symmetry-adapted perturbation theory. As such, we demonstrate that perturbation theory can help to reduce the quantum hardware requirements for quantum chemistry.

V. Barone, C. Puzzarini

Gas-phase molecular spectroscopy is a natural playground for accurate quantum-chemical computations. However, the molecular bricks of life (e.g., DNA bases or amino acids) are challenging systems because of the unfavorable scaling of quantum-chemical models with the molecular size (active electrons) and/or the presence of large-amplitude internal motions. From the theoretical point of view, both aspects prevent the brute force use of very accurate but very expensive state-of-the-art quantum-chemical methodologies. From the experimental point of view, both features lead to congested gas-phase spectra, whose assignment and interpretation are not at all straightforward. Based on these premises, this review focuses on the current status and perspectives of the fully a priori prediction of the spectral signatures of medium-sized molecules (containing up to two dozen atoms) in the gas phase with special reference to rotational and vibrational spectroscopy of some representative molecular bricks of life. Expected final online publication date for the Annual Review of Physical Chemistry, Volume 74 is April 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Hui Dong, Jinlong Pan, Shuke Huang et al.

Chemical polishing is an effective method to remove a subsurface damage layer with the advantages of no mechanical stress and no new subsurface damage. In this paper, we report a target polishing method that employs an anhydrous organic acid-ionic liquid-in-oil (OA-IL/O) microemulsion as the etching solution for chemical polishing of KDP crystals. OA-IL/O microemulsions were prepared with 1-butyl-3-methyl imidazolium bis [(trifluoromethyl) sulfonyl] imide ([Bmim]TF2N) and bis (trifluoromethane sulfonimide) (TF2NH) as the internal phase, castor oil as the external phase, TX-100 as the surfactant, and n-butanol as the co-surfactant. TF2NH irreversibly reacts with KDP when microemulsion micelles driven by Brownian motion collide with the KDP surface. The organic salt products are removed by the ionic liquid in the microemulsion, resulting in the effective elimination of KDP. Moreover, the organic acid-ionic liquid solution will preferentially diffuse to the high points of the KDP surface and react with the KDP to achieve the target polishing. As a new type of water-free surface polishing technology, OA-IL/O microemulsion not only has the advantages of traditional CMP, but also avoids the recrystallization that can occur with water-in-oil microemulsions and achieves target polishing of the KDP crystal.

Nhat M. Nguyen, Hieu T. Tran, Minh V. Duong et al.

We present a differentiable greenhouse simulation model based on physical processes whose parameters can be obtained by training from real data. The physics-based simulation model is fully interpretable and is able to do state prediction for both climate and crop dynamics in the greenhouse over very a long time horizon. The model works by constructing a system of linear differential equations and solving them to obtain the next state. We propose a procedure to solve the differential equations, handle the problem of missing unobservable states in the data, and train the model efficiently. Our experiment shows the procedure is effective. The model improves significantly after training and can simulate a greenhouse that grows cucumbers accurately.

Silvan Käser, Luis Itza Vazquez-Salazar, Markus Meuwly et al.

Artificial Neural Networks (ANN) are already heavily involved in methods and applications for frequent tasks in the field of computational chemistry such as representation of potential energy surfaces (PES) and spectroscopic predictions. This perspective provides an overview of the foundations of neural network-based full-dimensional potential energy surfaces, their architectures, underlying concepts, their representation and applications to chemical systems. Methods for data generation and training procedures for PES construction are discussed and means for error assessment and refinement through transfer learning are presented. A selection of recent results illustrates the latest improvements regarding accuracy of PES representations and system size limitations in dynamics simulations, but also NN application enabling direct prediction of physical results without dynamics simulations. The aim is to provide an overview for the current state-of-the-art NN approaches in computational chemistry and also to point out the current challenges in enhancing reliability and applicability of NN methods on larger scale.