We introduce ChemPro, a progressive benchmark with 4100 natural language question-answer pairs in Chemistry, across 4 coherent sections of difficulty designed to assess the proficiency of Large Language Models (LLMs) in a broad spectrum of general chemistry topics. We include Multiple Choice Questions and Numerical Questions spread across fine-grained information recall, long-horizon reasoning, multi-concept questions, problem-solving with nuanced articulation, and straightforward questions in a balanced ratio, effectively covering Bio-Chemistry, Inorganic-Chemistry, Organic-Chemistry and Physical-Chemistry. ChemPro is carefully designed analogous to a student's academic evaluation for basic to high-school chemistry. A gradual increase in the question difficulty rigorously tests the ability of LLMs to progress from solving basic problems to solving more sophisticated challenges. We evaluate 45+7 state-of-the-art LLMs, spanning both open-source and proprietary variants, and our analysis reveals that while LLMs perform well on basic chemistry questions, their accuracy declines with different types and levels of complexity. These findings highlight the critical limitations of LLMs in
To enhance large language models (LLMs) for chemistry problem solving, several LLM-based agents augmented with tools have been proposed, such as ChemCrow and Coscientist. However, their evaluations are narrow in scope, leaving a large gap in understanding the benefits of tools across diverse chemistry tasks. To bridge this gap, we develop ChemToolAgent, an enhanced chemistry agent over ChemCrow, and conduct a comprehensive evaluation of its performance on both specialized chemistry tasks and general chemistry questions. Surprisingly, ChemToolAgent does not consistently outperform its base LLMs without tools. Our error analysis with a chemistry expert suggests that: For specialized chemistry tasks, such as synthesis prediction, we should augment agents with specialized tools; however, for general chemistry questions like those in exams, agents' ability to reason correctly with chemistry knowledge matters more, and tool augmentation does not always help.
The spatial distribution of the chemical reservoirs in protoplanetary disks is key to elucidate the composition of planets, especially habitable ones. However, the partitioning of the main elements among the refractory and volatile phases is still elusive. Key parameters such as the carbon-to-oxygen C/O elemental ratio and the ionization fraction remain poorly constrained, with the latter potentially orders of magnitude lower than in the interstellar medium. Moreover, the thermal structure of the gas is also poorly known, despite its deep influence on gas-phase chemistry. In this context, ortho-to-para ratios could provide selective and sensitive probes. Recent ALMA observations have measured the spatially resolved column density of ortho-and para-H2CO in the transition disk orbiting TW Hya and derived the radial profile of the ortho-to-para ratio. Yet, current disk models do not include the nuclear-spin-resolved chemistry required to interpret these observations. The present work aims to fill this gap, by combining a parametric disk physical model of TW Hya with the UGAN network, updated to include a comprehensive description of the nuclear-spin-resolved chemistry of formaldehyde.
Multimodal scientific reasoning remains a significant challenge for large language models (LLMs), particularly in chemistry, where problem-solving relies on symbolic diagrams, molecular structures, and structured visual data. Here, we systematically evaluate 40 proprietary and open-source multimodal LLMs, including GPT-5, o3, Gemini-2.5-Pro, and Qwen2.5-VL, on a curated benchmark of Olympiad-style chemistry questions drawn from over two decades of U.S. National Chemistry Olympiad (USNCO) exams. These questions require integrated visual and textual reasoning across diverse modalities. We find that many models struggle with modality fusion, where in some cases, removing the image even improves accuracy, indicating misalignment in vision-language integration. Chain-of-Thought prompting consistently enhances both accuracy and visual grounding, as demonstrated through ablation studies and occlusion-based interpretability. Our results reveal critical limitations in the scientific reasoning abilities of current MLLMs, providing actionable strategies for developing more robust and interpretable multimodal systems in chemistry. This work provides a timely benchmark for measuring progress in
Accelerated materials discovery is critical for addressing global challenges. However, developing new laboratory workflows relies heavily on real-world experimental trials, and this can hinder scalability because of the need for numerous physical make-and-test iterations. Here we present MATTERIX, a multiscale, graphics processing unit-accelerated robotic simulation framework designed to create high-fidelity digital twins of chemistry laboratories, thus accelerating workflow development. This multiscale digital twin simulates robotic physical manipulation, powder and liquid dynamics, device functionalities, heat transfer and basic chemical reaction kinetics. This is enabled by integrating realistic physics simulation and photorealistic rendering with a modular graphics processing unit-accelerated semantics engine, which models logical states and continuous behaviors to simulate chemistry workflows across different levels of abstraction. MATTERIX streamlines the creation of digital twin environments through open-source asset libraries and interfaces, while enabling flexible workflow design via hierarchical plan definition and a modular skill library that incorporates learning-based
Efficient chemical kinetic model inference and application in combustion are challenging due to large ODE systems and widely separated time scales. Machine learning techniques have been proposed to streamline these models, though strong nonlinearity and numerical stiffness combined with noisy data sources make their application challenging. Here, we introduce ChemKANs, a novel neural network framework with applications both in model inference and simulation acceleration for combustion chemistry. ChemKAN's novel structure augments the generic Kolmogorov Arnold Network Ordinary Differential Equations (KAN-ODEs) with knowledge of the information flow through the relevant kinetic and thermodynamic laws. This chemistry-specific structure combined with the expressivity and rapid neural scaling of the underlying KAN-ODE algorithm instills in ChemKANs a strong inductive bias, streamlined training, and higher accuracy predictions compared to standard benchmarks, while facilitating parameter sparsity through shared information across all inputs and outputs. In a model inference investigation, we benchmark the robustness of ChemKANs to sparse data containing up to 15% added noise, and superfl
Planets form and obtain their compositions from the leftover material present in protoplanetary disks of dust and gas surrounding young stars. The chemical make-up of a disk influences every aspect of planetary composition including their overall chemical properties, volatile content, atmospheric composition, and potential for habitability. This Review discusses our knowledge of the chemical and isotopic composition of Solar System materials and how this information can be used to place constraints on the formation pathways of terrestrial planets. We conclude that planetesimal formation by the streaming instability followed by rapid accretion of drifting pebbles within the protoplanetary disk lifetime reproduces most of the chemical and isotopic observables in Solar System. This finding has important implications for planetary habitability beyond the Solar System because in pebble accretion, volatiles important for life are accreted during the main growth phase of rocky planets as opposed to the late-stage. Finally, we explore how bulk chemical inventories and masses of planetary bodies control the composition of their primordial atmospheres and their potential to develop habitable
Three-body recombination, or ternary association, is a termolecular reaction in which three particles collide, forming a bound state between two, whereas the third escapes freely. Three-body recombination reactions play a significant role in many systems relevant to physics and chemistry. In particular, they are relevant in cold and ultracold chemistry, quantum gases, astrochemistry, atmospheric physics, physical chemistry, and plasma physics. As a result, three-body recombination has been the subject of extensive work during the last 50 years, although primarily from an experimental perspective. Indeed, a general theory for three-body recombination remains elusive despite the available experimental information. Our group recently developed a direct approach based on classical trajectory calculations in hyperspherical coordinates for three-body recombination to amend this situation, leading to a first principle explanation of ion-atom-atom and atom-atom-atom three-body recombination processes. This review aims to summarize our findings on three-body recombination reactions and identify the remaining challenges in the field.
Erratum to the article: Charge transfer to solvent identified using dark channel fluorescence-yield L-edge spectroscopy, NATURE CHEMISTRY 2 (2010) 853
In modern collider experiments, the quest to explore fundamental interactions between elementary particles has reached unparalleled levels of precision. Signatures from particle physics detectors are low-level objects (such as energy depositions or tracks) encoding the physics of collisions (the final state particles of hard scattering interactions). The complete simulation of them in a detector is a computational and storage-intensive task. To address this computational bottleneck in particle physics, alternative approaches have been developed, introducing additional assumptions and trade off accuracy for speed.The field has seen a surge in interest in surrogate modeling the detector simulation, fueled by the advancements in deep generative models. These models aim to generate responses that are statistically identical to the observed data. In this paper, we conduct a comprehensive and exhaustive taxonomic review of the existing literature on the simulation of detector signatures from both methodological and application-wise perspectives. Initially, we formulate the problem of detector signature simulation and discuss its different variations that can be unified. Next, we classify
Planets form in disks of gas and dust around young stars. The disk molecular reservoirs and their chemical evolution affect all aspects of planet formation, from the coagulation of dust grains into pebbles, to the elemental and molecular compositions of the mature planet. Disk chemistry also enables unique probes of disk structures and dynamics, including those directly linked to ongoing planet formation. Here we review the protoplanetary disk chemistry of the volatile elements HOCNSP, the associated observational and theoretical methods, and the links between disk and planet chemical compositions. Three takeaways from this review are: (1) The disk chemical composition, including the organic reservoirs, is set by both inheritance and in situ chemistry. (2) Disk gas and solid O/C/N/H elemental ratios often deviate from stellar values due to a combination of condensation of molecular carriers, chemistry, and dynamics. (3) Chemical, physical, and dynamical processes in disks are closely linked, which complicates disk chemistry modeling, but these links also present an opportunity to develop chemical probes of different aspects of disk evolution and planet formation.
We present two open-source implementations of the Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) algorithm to find a few eigenvalues and eigenvectors of large, possibly sparse matrices. We then test LOBPCG for various quantum chemistry problems, encompassing medium to large, dense to sparse, wellbehaved to ill-conditioned ones, where the standard method typically used is Davidson's diagonalization. Numerical tests show that, while Davidson's method remains the best choice for most applications in quantum chemistry, LOBPCG represents a competitive alternative, especially when memory is an issue, and can even outperform Davidson for ill-conditioned, non diagonally dominant problems.
The integration of power electronics-based energy storage systems (PEESs) into power systems introduces potential instabilities. This study reviews efforts in dynamic analysis of both AC and DC power systems integrated with PEESs, covering dynamic modeling, analysis methods, and potential instability risks. Major conclusions are drawn as: 1) Simplified models of PEESs have been widely used for dynamic analysis of power systems. However, it may cause "error aggregation" as the scale of PEESs increases, leading to mistakes in results, which induces significant concerns. 2) Traditional stability mechanism analysis methods remain effective for single grid-connected PEES and large-scale PEESs with parallel and series connections. However, they are inadequate for PEESs with distributed connections. To fill in this gap, an idea of mechanism analysis based on "dynamic reconstruction" is proposed. 3) Potential instability risks caused by PEESs integration may differ from those caused by renewable energy integration due to differences in functional controls and bidirectional power flow. However, comprehensive investigations in this regard are lacking and require significant attention. To ens
Astrochemistry lies at the nexus of astronomy, chemistry, and molecular physics. On the basis of precise laboratory data, a rich collection of more than 200 familiar and exotic molecules have been identified in the interstellar medium, the vast majority by their unique rotational fingerprint. Despite this large body of work, there is scant evidence in the radio band for the basic building blocks of chemistry on earth -- five and six-membered rings -- despite long standing and sustained efforts during the past 50 years. In contrast, a peculiar structural motif, highly unsaturated carbon in a chain-like arrangement, is instead quite common in space. The recent astronomical detection of cyanobenzene, the simplest aromatic nitrile, in the dark molecular cloud TMC-1, and soon afterwards in additional pre-stellar, and possibly protostellar sources, establishes that aromatic chemistry is likely widespread in the earliest stages of star formation. The subsequent discovery of cyanocyclopentadienes and even cyanonapthlenes in TMC-1 provides further evidence that organic molecules of considerable complexity are readily synthesized in regions with high visual extinction but where the low tempe
Possible topological nature of Kondo and mixed valence insulators has been a recent topic of interest in condensed matter physics. Attention has focused on SmB6, which has long been known to exhibit low temperature transport anomaly, whose origin is of independent interest. We argue that it is possible to resolve the topological nature of surface states by uniquely accessing the surface electronic structure of the low temperature anomalous transport regime through combining state-of-the-art laser- and synchrotron-based angle-resolved photoemission spectroscopy (ARPES) with or without spin resolution. A combination of low temperature and ultra-high resolution (laser) which is lacking in previous ARPES studies of this compound is the key to resolve the possible existence of topological surface state in SmB6. Here we outline an experimental algorithm to systematically explore the topological versus trivial or mixed (topological and trivial surface state admixture as in the first 3D TI Bi$_{1-x}$Sb$_x$) nature of the surface states in Kondo and mixed valence insulators. We conclude based on this methodology that the observed topology of the surface Fermi surface in our low temperature
We present an extension of the code ProDiMo that allows for a modeling of processes pertinent to active galactic nuclei and to an ambient chemistry that is time dependent. We present a proof-of-concept and focus on a few astrophysically relevant species, e.g., H+, H2+ and H3+; C+ and N+; C and O; CO and H2O; OH+, H2O+ and H3O+; HCN and HCO+. We find that the freeze-out of water is strongly suppressed and that this affects the bulk of the oxygen and carbon chemistry occurring in AGN. The commonly used AGN tracer HCN/HCO+ is strongly time-dependent, with ratios that vary over orders of magnitude for times longer than 10^4 years. Through ALMA observations this ratio can be used to probe how the narrow-line region evolves under large fluctuations in the SMBH accretion rate. Strong evolutionary trends, on time scales of 10^4-10^8 years, are also found in species such as H3O+, CO, and H2O. These reflect, respectively, time dependent effects in the ionization balance, the transient nature of the production of molecular gas, and the freeze-out/sublimation of water.
Chemistry in Titan's N2-CH4 atmosphere produces complex organic aerosols. The chemical processes and the resulting organic compounds are still far from understood, although extensive observations, laboratory, and theoretical simulations have greatly improved physical and chemical constraints on Titan's atmosphere. Here, we conduct a series of Titan atmosphere simulation experiments with N2-CH4 gas mixtures and investigate the effect of initial CH4 ratio, pressure, and flow rate on the production rates and composition of the gas and solid products at a Titan relevant temperature (100 K) for the first time. We find that the production rate of the gas and solid products increases with increasing CH4 ratio. The nitrogen-containing species have much higher yield than hydrocarbons in the gas products, and the N-to-C ratio of the solid products appears to be the highest compared to previous plasma simulations with the same CH4 ratio. The greater degree of nitrogen incorporation in the low temperature simulation experiments suggests temperature may play an important role in nitrogen incorporation in Titan's cold atmosphere. We also find that H2 is the dominant gas product and serves as an
Water is observed throughout the universe, from diffuse interstellar clouds to protoplanetary disks around young stars, and from comets in our own solar system and exoplanetary atmospheres to galaxies at high redshifts. This review summarizes the spectroscopy and excitation of water in interstellar space as well as the basic chemical processes that form and destroy water under interstellar conditions. Three major routes to water formation are identified: low temperature ion-molecule chemistry, high-temperature neutral-neutral chemistry and gas-ice chemistry. The rate coefficients of several important processes entering the networks are discussed in detail; several of them have been determined only in the last decade through laboratory experiments and theoretical calculations. Astronomical examples of each of the different chemical routes are presented using data from powerful new telescopes, in particular the Herschel Space Observatory. Basic chemical physics studies remain critically important to analyze astronomical data.
We briefly review the various contexts within which one might address the issue of ``why'' the dimensionless constants of Nature have the particular values that they are observed to have. Both the general historical trend, in physics, of replacing a-priori-given, absolute structures by dynamical entities, and anthropic considerations, suggest that coupling ``constants'' have a dynamical nature. This hints at the existence of observable violations of the Equivalence Principle at some level, and motivates the need for improved tests of the Equivalence Principle.
Upon mechanical loading, granular materials yield and undergo plastic deformation. The nature of plastic deformation is essential for the development of the macroscopic constitutive models and the understanding of shear band formation. However, we still do not fully understand the microscopic nature of plastic deformation in disordered granular materials. Here we used synchrotron X-ray tomography technique to track the structural evolutions of three-dimensional granular materials under shear. We establish that highly distorted coplanar tetrahedra are the structural defects responsible for microscopic plasticity in disordered granular packings. The elementary plastic events occur through flip events which correspond to a neighbor switching process among these coplanar tetrahedra (or equivalently as the rotation motion of 4-ring disclinations). These events are discrete in space and possess specific orientations with the principal stress direction.