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.
Accurate prediction of grape phenology is essential for timely vineyard management decisions, such as scheduling irrigation and fertilization, to maximize crop yield and quality. While traditional biophysical models calibrated on historical field data can be used for season-long predictions, they lack the precision required for fine-grained vineyard management. Deep learning methods are a compelling alternative but their performance is hindered by sparse phenology datasets, particularly at the cultivar level. We propose a hybrid modeling approach that combines multi-task learning with a recurrent neural network to parameterize a differentiable biophysical model. By using multi-task learning to predict the parameters of the biophysical model, our approach enables shared learning across cultivars while preserving biological structure, thereby improving the robustness and accuracy of predictions. Empirical evaluation using real-world and synthetic datasets demonstrates that our method significantly outperforms both conventional biophysical models and baseline deep learning approaches in predicting phenological stages, as well as other crop state variables such as cold-hardiness and wh
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
There is a growing body of work seeking to replicate the success of machine learning (ML) on domains like computer vision (CV) and natural language processing (NLP) to applications involving biophysical data. One of the key ingredients of prior successes in CV and NLP was the broad acceptance of difficult benchmarks that distilled key subproblems into approachable tasks that any junior researcher could investigate, but good benchmarks for biophysical domains are rare. This scarcity is partially due to a narrow focus on benchmarks which simulate biophysical data; we propose instead to carefully abstract biophysical problems into simpler ones with key geometric similarities. In particular we propose a new class of closed-form test functions for biophysical sequence optimization, which we call Ehrlich functions. We provide empirical results demonstrating these functions are interesting objects of study and can be non-trivial to solve with a standard genetic optimization baseline.
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
Cyanopolyynes, a family of nitrogen containing carbon chains, are common in the interstellar medium and possibly form the backbone of species relevant to prebiotic chemistry. Following their gas phase formation, they are expected to freeze out on ice grains in cold interstellar regions. In this work we present the hydrogenation reaction network of isolated HC_{3}N, the smallest cyanopolyyne, that consists over-a-barrier radical-neutral reactions and barrierless radical-radical reactions. We employ density functional theory, coupled cluster and multiconfigurational methods to obtain activation and reaction energies for the hydrogenation network of HC_{3}N. This work explores the reaction network of the isolated molecule and constitutes a preview on the reactions occurring on the ice grain surface. We find that the reactions where the hydrogen atom adds to the carbon chain at carbon atom opposite of the cyano-group give the lowest and most narrow barriers. Subsequent hydrogenation leads to the astrochemically relevant vinyl cyanide and ethyl cyanide. Alternatively, the cyano-group can hydrogenate via radical-radical reactions, leading to the fully saturated propylamine. These results
Stratospheric aerosol injection (SAI) has been proposed as a geoengineering strategy to mitigate global warming by increasing Earth's albedo. Silica-based materials, such as diamond-doped silica aerogels, have shown promising optical properties, but their impact on stratospheric chemistry, ozone one in particular, remains largely unknown. Here, we present first-principles molecular dynamics (MD) simulations of the heterogeneous reaction between hydrogen chloride ($\mathrm{HCl}$) and chlorine nitrate ($\mathrm{ClONO_2}$), two main reservoirs of stratospheric chlorine and nitrogen species, on a dry, hydroxylated $α$-quartz silica interface. Our results reveal a barrierless reaction pathway toward the formation of chlorine gas ($\mathrm{Cl}_2$), a major contributor to stratospheric ozone loss. We design a heterogeneous kinetic model informed by our MD simulation and available experimental data: despite the barrierless formation of $\mathrm{Cl_2}$, the higher surface affinities and partial pressures of $\mathrm{HNO_3}$ and $\mathrm{HCl}$ compared to those of $\mathrm{ClONO_2}$ result in a negligible reaction probability, $γ_\mathrm{ClONO_2}$, upon chlorine nitrate collision with the si
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.
A challenge in global change biology is to predict how species will respond to future environmental change and to manage these responses. To make such predictions and management actions robust to novel futures, we need to accurately characterize how organisms experience their environments and the biological mechanisms by which they respond. All organisms are thermodynamically connected to their environments through the exchange of heat and water at fine spatial and temporal scales and this exchange can be captured with biophysical models. Although mechanistic models based on biophysical ecology have a long history of development and application, their use in global change biology remains limited despite their enormous promise and increasingly accessible software. We contend that greater understanding and training in the theory and methods of biophysical models is vital to expand their application. Our review shows how biophysical models can be implemented to understand and predict climate change impacts on species' behavior, phenology, survival, distribution, and abundance. We illustrate the types of outputs that can be generated, and the data inputs required for different implemen
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.
Plasma Assisted Combustion (PAC) is a promising technology to enhance the combustion of lean mixtures prone to instabilities and flame blow-off. Although many PAC experiments demonstrated combustion enhancement, several studies report an increase in NOx emissions. The aim of this study is to determine the kinetic pathways leading to NOx formation in the second stage of a sequential combustor assisted by Nanosecond Repetitively Pulsed Discharges (NRPDs). For this purpose, Large Eddy Simulation (LES) associated with an accurate description of the combustion/NOx chemistry and a phenomenological model of the plasma kinetics is used. Detailed kinetics 0-Dimensional reactors complement the study. First, the LES setup is validated by comparison with experiments. Then, the NOx chemistry is analyzed. For the conditions of operation studied, it is shown that the production of atomic nitrogen in the plasma by direct electron impact on nitrogen molecules increases the formation of NO. Then, the NO molecules are transported through the turbulent flame without being strongly affected. This study illustrates the need to limit the diatomic nitrogen dissociation process in order to mitigate harmful
Cartilage is a connective tissue that covers the surfaces of bones in joints and provides a smooth gliding surface for movement. It is characterized by specific biophysical properties that allow it to withstand compressive loads, distribute mechanical forces, and maintain tissue integrity. The bi-ophysical properties of cartilage are primarily determined by its extracellular matrix, which is composed of collagen fibers, proteoglycans, and water. The collagen fibers provide tensile strength, the proteoglycans provide compressive resistance, and the water content provides lubrication and shock absorption. The potential for greater knowledge of cartilage function through refinement and engineering-level understanding could inform the design of interventions for cartilage dysfunction and pathology. The aim is to assist to present basic principles of cartilage modeling and discussing the underlying physics and assumptions with relatively simple settings, and also it presents the derivation of multiphase cartilage models that are consistent with the discussions. Furthermore, modern developments align the structure captured in the models with observed complexities. The interactions betwee
British biophysics has a tradition of scientific invention and innovation, resulting in new technologies transforming biological insight, such as rapid DNA sequencing, super-resolution and label-free microscopy, high-throughput and single-molecule bio-sensing, and bio-inspired synthetic materials. Some advances were established through democratised platforms and many have biomedical success, a key example involving the SARS-CoV-2 spike protein during the COVID-19 pandemic. Here, three UK labs made crucial contributions revealing how the spike protein targets human cells, and how therapies of vaccines and neutralizing nanobodies work, enabled largely through biophysical innovations of cryo-electron microscopy. Here, we discuss leading-edge innovations which resulted from discovery-led British 'Physics of Life' research (capturing blends of physical-life sciences research in the UK including biophysics and biological physics) and have matured into wide-reaching sustainable commercial ventures enabling translational impact. We describe the biophysical science which led to these academic spinouts, presenting the scientific questions that were addressed through innovating new techniques
Kohn-Sham density functional theory is in principle an exact formulation of quantum mechanical electronic structure theory, but in practice we have to rely on approximate exchange-correlation (xc) functionals. The objective of our work has been to design an xc functional with broad accuracy across as wide an expanse of chemistry and physics as possible, leading-as a long-range goal-to a functional with good accuracy for all problems, i.e., a universal functional. To guide our path toward that goal and to measure our progress, we have developed-building on earlier work in our group-a set of databases of reference data for a variety of energetic and structural properties in chemistry and physics. These databases include energies of molecular processes such as atomization, complexation, proton addition, and ionization; they also include molecular geometries and solid-state lattice constants, chemical reaction barrier heights, and cohesive energies and band gaps of solids. For the present paper we gather many of these databases into four comprehensive databases, two with 384 energetic data for chemistry and solid-state physics and another two with 68 structural data for chemistry and s
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
The Hodgkin-Huxley (HH) model is the currently accepted formalism of neuronal excitability. However, the HH model does not capture a number of biophysical behaviors associated with action potentials or propagating nerve impulses. Physical mechanisms underlying these processes, such as reversible heat transfer and axonal swelling have been separately investigated and compartmentally modeled to indicate the nervous system is not purely electrical or biochemical. Rather, mechanical forces and principles of thermodynamics also govern neuronal excitability and signaling. To advance our understanding of neural function and dysfunction, compartmentalized analyses of electrical, chemical, and mechanical processes need to revaluated and integrated into more comprehensive theories. The present quantitative perspective is intended to broaden the awareness of known biophysical phenomena, which are often overlooked in neuroscience. By starting to consider the collective influence of the biophysical forces influencing neural function, new paradigms can be applied to the characterization and manipulation of nervous systems.
A solution of the two-dimensional Schrödinger equation with Pauli-Fierz Hamiltonian and trigonometric double-well potential is obtained within the framework of the first-order of adiabatic approximation. The case of vibrational strong coupling is considered which is pertinent for polariton chemistry and (presumably) for enzymatic hydrogen transfer. We exemplify the application of the solution by calculating the proton transfer rate constant in the hydrogen bond of the Zundel ion ${\rm{H_5O_2^{+}}}$ (oxonium hydrate) within the framework of the Weiner's theory. An analytic formula is derived which provides the calculation of the proton transfer rate with the help of elements implemented in {\sl {Mathematica}}. The parameters of the model for the Zundel ion are extracted from the literature data on IR spectroscopy and quantum chemical calculations. The approach yields a vivid manifestation of the phenomenon of vibrationally enhanced tunneling, i.e., a sharp bell-shaped peak of the rate enhancement by the external vibration at its symmetric coupling to the proton coordinate. The results obtained testify that the effect of resonant activation in our model is robust and stable to variat