Artificial intelligence (AI) and data science are transforming chemical research, yet few formal courses are tailored to synthetic and experimental chemists, who often face steep entry barriers due to limited coding experience and lack of chemistry-specific examples. We present the design and implementation of AI4CHEM, an introductory data-driven chem-istry course created for students on the synthetic chemistry track with no prior programming background. The curricu-lum emphasizes chemical context over abstract algorithms, using an accessible web-based platform to ensure zero-install machine learning (ML) workflow development practice and in-class active learning. Assessment combines code-guided homework, literature-based mini-reviews, and collaborative projects in which students build AI-assisted workflows for real experimental problems. Learning gains include increased confidence with Python, molecular property prediction, reaction optimization, and data mining, and improved skills in evaluating AI tools in chemistry. All course materials are openly available, offering a discipline-specific, beginner-accessible framework for integrating AI into synthetic chemistry training.
Text-to-audio-video (T2AV) generation is central to applications such as filmmaking and world modeling. However, current models often fail to produce physically plausible sounds. Previous benchmarks primarily focus on audio-video temporal synchronization, while largely overlooking explicit evaluation of audio-physics grounding, thereby limiting the study of physically plausible audio-visual generation. To address this issue, we present PhyAVBench, the first benchmark that systematically evaluates the audio-physics grounding capabilities of T2AV, image-to-audio-video (I2AV), and video-to-audio (V2A) models. PhyAVBench offers PhyAV-Sound-11K, a new dataset of 25.5 hours of 11,605 audible videos collected from 184 participants to ensure diversity and avoid data leakage. It contains 337 paired-prompt groups with controlled physical variations that drive sound differences, each grounded with an average of 17 videos and spanning 6 audio-physics dimensions and 41 fine-grained test points. Each prompt pair is annotated with the physical factors underlying their acoustic differences. Importantly, PhyAVBench leverages paired text prompts to evaluate this capability. We term this evaluation paradigm the Audio-Physics Sensitivity Test (APST) and introduce a novel metric, the Contrastive Physical Response Score (CPRS), which quantifies the acoustic consistency between generated videos and their real-world counterparts. We conduct a comprehensive evaluation of 17 state-of-the-art models. Our results reveal that even leading commercial models struggle with fundamental audio-physical phenomena, exposing a critical gap beyond audio-visual synchronization and pointing to future research directions. We hope PhyAVBench will serve as a foundation for advancing physically grounded audio-visual generation. Prompts, ground-truth, and generated video samples are available at https://phyavbench.pages.dev/.
Artificial chemistry simulations produce many intriguing emergent behaviors, but they are often difficult to steer or control. This paper proposes a method for steering the dynamics of a classic artificial chemistry model, known as AlChemy (Algorithmic Chemistry), which is based on untyped lambda calculus. Our approach leverages features that are endogenous to AlChemy without constructing an explicit external fitness function or building learning into the dynamics. We demonstrate the approach by synthesizing non-trivial lambda functions, such as Church addition and succession, from simple primitives. The results provide insight into the possibility of endogenous selection in diverse systems such as autocatalytic chemical networks and software systems.
Large Language Model (LLM)-based agents have demonstrated the ability to improve performance in chemistry-related tasks by selecting appropriate tools. However, their effectiveness remains limited by the inherent prediction errors of chemistry tools. In this paper, we take a step further by exploring how LLMbased agents can, in turn, be leveraged to reduce prediction errors of the tools. To this end, we propose ChemHAS (Chemical Hierarchical Agent Stacking), a simple yet effective method that enhances chemistry tools through optimizing agent-stacking structures from limited data. ChemHAS achieves state-of-the-art performance across four fundamental chemistry tasks, demonstrating that our method can effectively compensate for prediction errors of the tools. Furthermore, we identify and characterize four distinct agent-stacking behaviors, potentially improving interpretability and revealing new possibilities for AI agent applications in scientific research. Our code and dataset are publicly available at https: //anonymous.4open.science/r/ChemHAS-01E4/README.md.
In real-world human-robot systems, it is essential for a robot to comprehend human objectives and respond accordingly while performing an extended series of motor actions. Although human objective alignment has recently emerged as a promising paradigm in the realm of physical human-robot interaction, its application is typically confined to generating simple motions due to inherent theoretical limitations. In this work, our goal is to develop a general formulation to learn manipulation functional modules and long-term task goals simultaneously from physical human-robot interaction. We show the feasibility of our framework in enabling robots to align their behaviors with the long-term task objectives inferred from human interactions.
Early results from the JWST-MIRI guaranteed time programs on protostars (JOYS) and disks (MINDS) are presented. Thanks to the increased sensitivity, spectral and spatial resolution of the MIRI spectrometer, the chemical inventory of the planet-forming zones in disks can be investigated with unprecedented detail across stellar mass range and age. Here data are presented for five disks, four around low-mass stars and one around a very young high-mass star. The mid-infrared spectra show some similarities but also significant diversity: some sources are rich in CO2, others in H2O or C2H2. In one disk around a very low-mass star, booming C2H2 emission provides evidence for a ``soot'' line at which carbon grains are eroded and sublimated, leading to a rich hydrocarbon chemistry in which even di-acetylene (C4H2) and benzene (C6H6) are detected (Tabone et al. 2023). Together, the data point to an active inner disk gas-phase chemistry that is closely linked to the physical structure (temperature, snowlines, presence of cavities and dust traps) of the entire disk and which may result in varying CO2/H2O abundances and high C/O ratios >1 in some cases. Ultimately, this diversity in disk chemistry will also be reflected in the diversity of the chemical composition of exoplanets.
Evgeny Posenitskiy, Vijay Gopal Chilkuri, Abdallah Ammar
et al.
TREXIO is an open-source file format and library developed for the storage and manipulation of data produced by quantum chemistry calculations. It is designed with the goal of providing a reliable and efficient method of storing and exchanging wave function parameters and matrix elements, making it an important tool for researchers in the field of quantum chemistry. In this work, we present an overview of the TREXIO file format and library. The library consists of a front-end implemented in the C programming language and two different back-ends: a text back-end and a binary back-end utilizing the HDF5 library which enables fast read and write operations. It is compatible with a variety of platforms and has interfaces for the Fortran, Python, and OCaml programming languages. In addition, a suite of tools has been developed to facilitate the use of the TREXIO format and library, including converters for popular quantum chemistry codes and utilities for validating and manipulating data stored in TREXIO files. The simplicity, versatility, and ease of use of TREXIO make it a valuable resource for researchers working with quantum chemistry data.
Over the last decades, excellent computational chemistry tools have been developed. Integrating them into a single platform with enhanced accessibility could help reaching their full potential by overcoming steep learning curves. Recently, large-language models (LLMs) have shown strong performance in tasks across domains, but struggle with chemistry-related problems. Moreover, these models lack access to external knowledge sources, limiting their usefulness in scientific applications. In this study, we introduce ChemCrow, an LLM chemistry agent designed to accomplish tasks across organic synthesis, drug discovery, and materials design. By integrating 18 expert-designed tools, ChemCrow augments the LLM performance in chemistry, and new capabilities emerge. Our agent autonomously planned and executed the syntheses of an insect repellent, three organocatalysts, and guided the discovery of a novel chromophore. Our evaluation, including both LLM and expert assessments, demonstrates ChemCrow's effectiveness in automating a diverse set of chemical tasks. Surprisingly, we find that GPT-4 as an evaluator cannot distinguish between clearly wrong GPT-4 completions and Chemcrow's performance. Our work not only aids expert chemists and lowers barriers for non-experts, but also fosters scientific advancement by bridging the gap between experimental and computational chemistry.
Two-dimensional (2D) nanomaterial-reinforced polymer composites exhibit superior properties and multifunctional applications. Compared to lower dimensional nanomaterials such as nanotubes and nanoparticles, 2D nanomaterials show a larger surface area. The large surface area makes 2D nanomaterials more effectively restrict the mobility of polymer chains and yields better reinforcing efficiency than the lower-dimensional nanomaterials. To gain an in-depth understanding and extend the applications of polymer composites reinforced with 2D nanomaterials, this paper reviews the progress in the fundamentals of synthesis and applications of such composites. The motivation and improvement of adding 2D nanomaterials to polymer materials are introduced first, followed by the synthesis approaches and the properties of typical 2D nanomaterials, including graphene, boron nitride nanosheet, and molybdenum disulfide nanosheet. Based on the properties of 2D nanomaterials, polymer composites reinforced with different types of 2D nanomaterials are designed for structural application, thermal dissipation application, tribological application, three-dimensional printing composite structures, and strain sensing application. Afterwards, the significance of reinforcement–matrix interaction and its improving approach are reviewed. The current progress envisions that polymer composites reinforced with 2D nanomaterials can be used in the fields of aviation and aerospace for improving radiation shielding capacity and nanomedical engineering.
The detection of neutron-antineutron oscillation will be a discovery of fundamental importance in particle physics and cosmology. In models discussed widely in literature, the process is generated through heavy New Physics with weak, perturbative, coupling at the scale of the experiment. Acknowledging the fact that Nature has been quite evasive regarding the strength and scale of New Physics, we discuss a new mechanism, generated by confining New Physics, at $\sim 2$ GeV, resulting in low-energy baryon number violating effects. The mechanism predicts baryon number violating processes like neutron disappearance, neutron-neutron annihilation and neutron-anti neutron oscillation generated through the condensation of the linear moose.
Quantum computational chemistry has emerged as an important application of quantum computing. Hybrid quantum-classical computing methods, such as variational quantum eigensolvers (VQE), have been designed as promising solutions to quantum chemistry problems, yet challenges due to theoretical complexity and experimental imperfections hinder progress in achieving reliable and accurate results. Experimental works for solving electronic structures are consequently still restricted to nonscalable (hardware efficient) or classically simulable (Hartree-Fock) ansatz, or limited to a few qubits with large errors. The experimental realisation of scalable and high-precision quantum chemistry simulation remains elusive. Here, we address the critical challenges {associated with} solving molecular electronic structures using noisy quantum processors. Our protocol presents significant improvements in the circuit depth and running time, key metrics for chemistry simulation. Through systematic hardware enhancements and the integration of error mitigation techniques, we push forward the limit of experimental quantum computational chemistry and successfully scale up the implementation of VQE with an optimised unitary coupled-cluster ansatz to 12 qubits. We produce high-precision results of the ground-state energy for molecules with error suppression by around two orders of magnitude. We achieve chemical accuracy for H$_2$ at all bond distances and LiH at small bond distances in the experiment, even beyond the two recent concurrent works. Our work demonstrates a feasible path towards a scalable solution to electronic structure calculation, validating the key technological features and identifying future challenges for this goal.
Wrya O. KARIM, Jamil A. JUMA, Khalid M. OMER
et al.
This report presents influence of water and copper salt on the anodic dissolution of metallic copper in a eutectic solvent of choline chloride and ethylene glycol (DES) in a 1 : 2 molar ratio. The mechanism of copper dissolution anodically was investigated using anodic linear sweep voltammetry (LSV) and electrochemical impedance spectroscopy (EIS). Atomic force microscope (AFM) was used to examine the morphology and topography of the surface after electrochemical dissolution course. The addition of 1, 4, 8, 16 and 20 vol% of water cause pitting and has no significant impact on the electrochemical behavior, in particular the shape of anodic linear sweep voltammetry remains unchanged. The more profound effect was seen from the microscopic analysis. The addition of 0.1 and 0.81 M CuCl2 into this eutectic solvent resulted in relatively high resistance at the interfacial region where charge transfer occurs during anodic dissolution of metallic copper using impedance responses. The results confirmed that water will not affect anodic dissolution behavior and the chemistry of dissolution in the deep eutectic solvent.
The reduced mass of the effective quantum particle for the inversion mode $ν_2$ of phosphine molecule ${\rm{PH_3}}$ is known to be a position dependent one. In the present article the inversion spectrum of ${\rm{PH_3}}$ is considered with the help of the Schrödinger equation (SE) with position dependent mass and corresponding modified double-well potential. The SE is shown to be exactly solvable. The results are used for the analysis of the pertinent experimental data available in literature. We are based on the reliable value $ν_2=E_2-E_0=992.1\ {\rm cm^{-1}}$ ($2ν_2=E_4-E_0=1972.5\ {\rm cm^{-1}}$; $3ν_2=E_6-E_0=2940.8\ {\rm cm^{-1}}$; $4ν_2=E_8-E_0=3895.9\ {\rm cm^{-1}}$) obtained by \v Spirko et al. Also we use the value for the barrier height $E_b=12300 \ {\rm cm^{-1}}$ that seems to be commonly accepted at present and the hypothetical value for the energy splitting of the 11-th doublet of the $10ν_2$ band $s_{10}=E_{21}-E_{20}\approx 7.2\ {\rm cm^{-1}}$ suggested in the literature. Definite predictions are derived for the energy splitting of the 4-th doublet of the $3ν_2$ band $s_3=E_{7}-E_{6}$ that is a test one for the observation in the experiment of Okuda et al. SE with position dependent mass provides self-consistently the required values of $\{ν_2; E_b;s_{10}\}$ yielding $s_3= 6.21\cdot 10^{-12}\ {\rm cm^{-1}}$.
Lithium-sulfur (Li-S) batteries have been regarded as a competitive candidate for next generation electrochemical energy-storage technologies. However, the insulation of charge and discharge products (sulfur and lithium sulfide) and the shuttle efforts of lithium polysulfides (LiPSs), result in not only a series of phase conversion but also sluggish redox kinetics in Li-S electrochemistry. Herein, we firstly designed a flexible carbon cloth/molybdenum selenide (CC/MoSe2) by growing ultra-thin MoSe2 nanosheets on CC as binder-free electrode to understand the regulation mechanism in Li-S battery. With systematic electrochemical investigation of in-situ deposition Li2S8 in CC/MoSe2, it is found that CC/MoSe2 exhibits high LiPSs chemical adsorption and electrocatalytic activity, which large enhances the LiPSs conversion. The dynamic regulation of LiPSs change the nucleation and growth of Li2S, resulting in high uniform distribution on CC/MoSe2 electrode. Thus, it obtains high sulfur redox kinetics and utilization, which achieves initial capacity of 1142 mAh g-1 with low capacity fade of only 0.038 % per cycle over 500 cycles at 1 C. Even at high S loading (4 mg cm-2) and extremely low electrolyte/S (E/S) ratio of 6.2 μL mg-1, it still delivers 1204 mAh g−1 after 100 cycles at 0.2C with 93.3% capacity maintain.
Industrial electrochemistry, Physical and theoretical chemistry
The inhibitory strength of the two soluble polymer compounds namely, poloxamer (PLX) and pectin (PEC) towards the corrosion of carbon steel in 1.0 M HCl solution was elucidated utilizing four techniques. The inhibition efficacy augments with the concentration of the tested polymer and with reducing temperature. The outcome of the corrosion parameter obtained from all utilized measurements emphasizes the inhibiting vigor of these two compounds. The inhibition was interpreted by the formation of an adsorbing coated layer that isolates the steel surface from aggressive solutions. The adsorption of PLX and PEC on the steel surface follows Freundlich isotherm. The adsorption type is a combination of physical and chemical adsorption. Also, PLX and PEC compounds inhibit the pitting corrosion of C-steel in Cl- ions including the solutions by moving the pitting potential in the positive direction. The inhibition efficacy of PET more than PLX due to higher molar mass of PET resulting in greater surface coverage on C-steel surface. There is a clear harmony on the values of the inhibition efficacy resulting from the different techniques.
Industrial electrochemistry, Physical and theoretical chemistry
Catalytic properties of the nanocluster iron-molybdenum polyoxometalate {Mo72Fe30} in solutions during the oxidation of iodide ions by persulfate have been studied. Automated installation based on spectrophotometric method of analysis controlled by computer was used. Comparison of literary data and kinetic datа on the oxidation of iodide with persulfate showed that keplerate {Mo72Fe30} is a heterogeneous catalyst. These data allowed to assume a non-radical mechanism for persulfate decomposition on the surface of cluster molecules at the first stage of the potassium iodide oxidation reaction. The value of the effective activation energy of the process indicates the catalytic effect of {Mo72Fe30} at the intermediate stage of the decomposition of the hydrogen peroxide.
In this work we study the spectral responses of thin films solar cells of heterojunctions based on CuInSe2 and CuInS2.
Four-layer structures are studied according to the n+n/pp+ model. First we consider the structure ZnO(n+
)/CdS(n)/CuInS2(p)/CuInSe2(p+) where CuInS2 represents the base and CuInSe2 the substrate in this model. Secondly we
consider the structure ZnO(n+)/CdS(n)/CuInSe2(p)/CuInS2(p+
), for this model CuInSe2 represents the base and CuInS2 the
substrate. ZnO and CdS are used as window layers in each structure. Using the continuity equation that governs transport of carriers in semiconductor material, models for calculating spectral responses are proposed for heterojunctions type n+n/pp+ based on CuInSe2 and CuInS2. For each structure we have presented the energy band diagram based on the Anderson model [1] and
determined the expression of the photocurrent. The theoretical results obtained allow to compare the performances of these two models by optimizing the different parameters of each structure (base thickness, diffusion length, recombination velocity at the
interface, etc.) in order to improve the overall efficiency of the collection of carriers.
Crystallography, Physical and theoretical chemistry
MoS2, Co-MoS2 and rGO-Co-MoS2 were synthesized successfully by a simple hydrothermal method. microstructures and Raman spectra of the samples were investigated by X-ray diffraction analysis and Raman spectroscopy. The result indicate that the peaks at 14.4°, 33.0° and 58.4° may be assigned to (002), (100) and (110) plane of pure MoS2, respectively. The crystallinity of MoS2 may be restrained by cobalt doping. D band and G band of amorphous carbon can be seen in the rGO-Co-MoS2. XPS result indicated that the rGO-10-Co-MoS2 is mainly consisted of S, Mo, Co, C and O, respectively. The electrochemical performance test results show that rGO-10-Co-MoS2 presents the lowest overpotentials of 205 mV at a current density of 10 mA cm-2 and the smallest Tafel slope, indicating cobalt doping and rGO hybrid can improve electrocatalytic activity for HER. In addition, The sample also exhibit good electrocatalytic stability in the acid condition.
Industrial electrochemistry, Physical and theoretical chemistry