Activity cliff prediction - identifying positions where small structural changes cause large potency shifts - has been a persistent challenge in computational medicinal chemistry. This work focuses on a parsimonious definition: which small modifications, at which positions, confer the highest probability of an outcome change. Position-level sensitivity is calculated using 25 million matched molecular pairs from 50 ChEMBL targets across six protein families, revealing that two questions have fundamentally different answers. "Which positions vary most?" is answered by scaffold size alone (NDCG@3 = 0.966), requiring no machine learning. "Which are true activity cliffs?" - where small modifications cause disproportionately large effects, as captured by SALI normalization - requires an 11-feature model with 3D pharmacophore context (NDCG@3 = 0.910 vs. 0.839 random), generalizing across all six protein families, novel scaffolds (0.913), and temporal splits (0.878). The model identifies the cliff-prone position first 53% of the time (vs. 27% random - 2x lift), reducing positions a chemist must explore from 3.1 to 2.1 - a 31% reduction in first-round experiments. Predicting which modificat
Light-matter interaction not only plays an instrumental role in characterizing materials' properties via various spectroscopic techniques but also provides a general strategy to manipulate material properties via the design of novel nanostructures. This perspective summarizes recent theoretical advances in modeling light-matter interactions in chemistry, mainly focusing on plasmon and polariton chemistry. The former utilizes the highly localized photon, plasmonic hot electrons, and local heat to drive chemical reactions. In contrast, polariton chemistry modifies the potential energy curvatures of bare electronic systems, and hence their chemistry, via forming light-matter hybrid states, so-called polaritons. The perspective starts with the basic background of light-matter interactions, molecular quantum electrodynamics theory, and the challenges of modeling light-matter interactions in chemistry. Then, the recent advances in modeling plasmon and polariton chemistry are described, and future directions toward multiscale simulations of light-matter interaction-mediated chemistry are discussed.
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.
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
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
In anticipation of the completion of the High-Luminosity Large Hadron Collider (HL-LHC) programme by the end of 2041, CERN is preparing to launch a new major facility in the mid-2040s. According to the 2020 update of the European Strategy for Particle Physics (ESPP), the highest-priority next collider is an electron-positron Higgs factory, followed in the longer term by a hadron-hadron collider at the highest achievable energy. The CERN directorate established a Future Colliders Comparative Evaluation working group in June 2023. This group brings together project leaders and domain experts to conduct a consistent evaluation of the Future Circular Collider (FCC) and alternative scenarios based on shared assumptions and standardized criteria. This report presents a comparative evaluation of proposed future collider projects submitted as input for the Update of the European Strategy for Particle Physics. These proposals are compared considering main performance parameters, environmental impact and sustainability, technical maturity, cost of construction and operation, required human resources, and realistic implementation timelines. An overview of the international collider projects w
In the much-celebrated book Deep Medicine, Eric Topol argues that the development of artificial intelligence for health care will lead to a dramatic shift in the culture and practice of medicine. In the next several decades, he suggests, AI will become sophisticated enough that many of the everyday tasks of physicians could be delegated to it. Topol is perhaps the most articulate advocate of the benefits of AI in medicine, but he is hardly alone in spruiking its potential to allow physicians to dedicate more of their time and attention to providing empathetic care for their patients in the future. Unfortunately, several factors suggest a radically different picture for the future of health care. Far from facilitating a return to a time of closer doctor-patient relationships, the use of medical AI seems likely to further erode therapeutic relationships and threaten professional and patient satisfaction.
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
Reliable predictions of the behaviour of chemical systems are essential across many industries, from nanoscale engineering over validation of advanced materials to nanotoxicity assessment in health and medicine. For the future we therefore envision a paradigm shift for the design of chemical simulations across all length scales from a prescriptive to a predictive and quantitative science. This paper presents an integrative perspective about the state-of-the-art of modelling in computational and theoretical chemistry with examples from data- and equation-based models. Extension to include reliable risk assessments and quality control are discussed. To specify and broaden the concept of chemical accuracy in the design cycle of reliable and robust molecular simulations the fields of computational chemistry, physics, mathematics, visualisation science, and engineering are bridged. Methods from electronic structure calculations serve as examples to explain how uncertainties arise: through assumed mechanisms in form of equations, model parameters, algorithms, and numerical implementations. We provide a full classification of uncertainties throughout the chemical modelling cycle and discu
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
We present spectral analysis of the transiting Saturn-mass planet WASP-117b, observed with the G141 grism of Wide Field Camera 3 (WFC3) on the Hubble Space Telescope. We reduce and fit the extracted spectrum from the raw transmission data using the open-source software Iraclis before performing a fully Bayesian retrieval using the publicly available analysis suite TauREx 3.0. We detect water vapour alongside a layer of fully opaque cloud, retrieving a terminator temperature of 833 K. In order to quantify the statistical significance of this detection, we employ the Atmospheric Detectability Index (ADI), deriving a value of 2.30, which provides positive but not strong evidence against the flatline model. Due to the eccentric orbit of WASP-117b, it is likely that chemical and mixing timescales oscillate throughout orbit due to the changing temperature, possibly allowing warmer chemistry to remain visible as the planet begins transit, despite the proximity of its point of ingress to apastron. We present simulated spectra of the planet as would be observed by the future space missions Ariel and JWST and show that, despite not being able to probe such chemistry with current HST data, th
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.
Two industry-grade datasets are presented in this paper that were collected at the Future Factories Lab at the University of South Carolina on December 11th and 12th of 2023. These datasets are generated by a manufacturing assembly line that utilizes industrial standards with respect to actuators, control mechanisms, and transducers. The two datasets were both generated simultaneously by operating the assembly line for 30 consecutive hours (with minor filtering) and collecting data from sensors equipped throughout the system. During operation, defects were also introduced into the assembly operation by manually removing parts needed for the final assembly. The datasets generated include a time series analog dataset and the other is a time series multi-modal dataset which includes images of the system alongside the analog data. These datasets were generated with the objective of providing tools to further the research towards enhancing intelligence in manufacturing. Real manufacturing datasets can be scarce let alone datasets with anomalies or defects. As such these datasets hope to address this gap and provide researchers with a foundation to build and train Artificial Intelligence
Future colliders are an essential component of a strategic vision for particle physics. Conceptual studies and technical developments for several exciting future collider options are underway internationally. In order to realize a future collider, a concerted accelerator R\&D program is required. The U.S. HEP accelerator R\&D program currently has no direct effort in collider-specific R\&D area. This shortcoming greatly compromises the U.S. leadership role in accelerator and particle physics. In this white paper, we propose a new national accelerator R\&D program on future colliders and outline the important characteristics of such a program.
Nanorobotics offers an emerging frontier in biomedicine, holding the potential to revolutionize diagnostic and therapeutic applications through its unique capabilities in manipulating biological systems at the nanoscale. Following PRISMA guidelines, a comprehensive literature search was conducted using IEEE Xplore and PubMed databases, resulting in the identification and analysis of a total of 414 papers. The studies were filtered to include only those that addressed both nanorobotics and direct medical applications. Our analysis traces the technology's evolution, highlighting its growing prominence in medicine as evidenced by the increasing number of publications over time. Applications ranged from targeted drug delivery and single-cell manipulation to minimally invasive surgery and biosensing. Despite the promise, limitations such as biocompatibility, precise control, and ethical concerns were also identified. This review aims to offer a thorough overview of the state of nanorobotics in medicine, drawing attention to current challenges and opportunities, and providing directions for future research in this rapidly advancing field.
Medicinal plants are increasingly recognized worldwide as an alternative source of efficacious and inexpensive medications to synthetic chemo-therapeutic compound. Rapid declining wild stocks of medicinal plants accompanied by adulteration and species substitutions reduce their efficacy, quality and safety. Consequently, the low accessibility to and non-affordability of orthodox medicine costs by rural dwellers to be healthy and economically productive further threaten their life expectancy. Finding comprehensive information on medicinal plants of conservation concern at a global level has been difficult. This has created a gap between computing technologies' promises and expectations in the healing process under complementary and alternative medicine. This paper presents the design and implementation of a Multimedia-based Medicinal Plants Sustainability Management System addressing these concerns. Medicinal plants' details for designing the system were collected through semi-structured interviews and databases. Unified Modelling Language, Microsoft-Visual-Studio.Net, C#3.0, Microsoft-Jet-Engine4.0, MySQL, Loquendo Multilingual Text-to-Speech Software, YouTube, and VLC Media Player
Earth's future detectability depends upon the trajectory of our civilization over the coming centuries. Human civilization is also the only known example of an energy-intensive civilization, so our history and future trajectories provide the basis for thinking about how to find life elsewhere. This special issue of Futures features contributions that consider the future evolution of the Earth system from an astrobiological perspective, with the goal of exploring the extent to which anthropogenic influence could be detectable across interstellar distances. This collection emphasizes the connection between the unfolding future of the Anthropocene with the search for extraterrestrial civilizations. Our rate of energy consumption will characterize the extent to which our energy-intensive society exerts direct influence on climate, which in turn may limit the ultimate lifetime of our civilization. If the answer to Fermi's question is that we are alone, so that our civilization represents the only form of intelligent life in the galaxy (or even the universe), then our responsibility to survive is even greater. If we do find evidence of another civilization on a distant exoplanet, then at
During this era of new drug designing, medicinal plants had become a very interesting object of further research. Pharmacology screening of active compound of medicinal plants would be time consuming and costly. Molecular docking is one of the in silico method which is more efficient compare to in vitro or in vivo method for its capability of finding the active compound in medicinal plants. In this method, three-dimensional structure becomes very important in the molecular docking methods, so we need a database that provides information on three-dimensional structures of chemical compounds from medicinal plants in Indonesia. Therefore, this study will prepare a database which provides information of the three dimensional structures of chemical compounds of medicinal plants. The database will be prepared by using MySQL format and is designed to be placed in http://herbaldb.farmasi.ui.ac.id website so that eventually this database can be accessed quickly and easily by users via the Internet.