ABSTRACT Heterogeneous catalysis has a wide range of applications in chemical manufacturing and sustainable technologies. It uses solid catalysis to enable efficient chemical transformations. Traditional research on active sites and reaction mechanisms relies heavily on experiments and computational methods, such as density functional theory calculations. However, the volume of scientific literature and data is growing fast. This rapid growth has made it increasingly difficult to capture, process, and act on emerging insights systematically. Recently, large language models (LLMs) have emerged as powerful tools to support various stages in catalysis research. Their ability to understand and generate natural language helps them extract useful information from vast amounts of text, assist in catalyst design, aid in planning experiments, and clarify complex descriptors. In this advanced review, we first analyze recent progress in applying LLMs to heterogeneous catalysis, focusing on four key areas: literature mining and knowledge extraction, catalyst design and screening, experiment automation and workflow optimization, and the interpretation of high‐dimensional descriptors. We then highlight the challenges in this field despite these advances, most notably the need for domain‐specific fine‐tuning and the improvement of molecular representation. We conclude by discussing future opportunities for integrating LLMs with complementary machine learning approaches and expert‐in‐the‐loop systems, toward accelerating the rational discovery of next‐generation catalysts. This article is categorized under: Data Science > Artificial Intelligence/Machine Learning Data Science > Chemoinformatics Theoretical and Physical Chemistry > Reaction Dynamics and Kinetics

ABSTRACT Aptamers—short single‐stranded DNA or RNA—are the latest biomolecules to fall within reach of powerful structure‐prediction pipelines that blend bioinformatics, computational chemistry, and artificial intelligence. These tools now enable high‐throughput exploration of aptamer conformational landscapes, a prerequisite for rational design and optimization of their exceptional target affinity and specificity. Next‐generation sequencing has democratized library analysis, allowing any laboratory to handle millions of variants. Hybrid workflows currently offer the most reliable secondary and tertiary structure models, and explicit treatment of conformational flexibility is proving indispensable for mapping binding‐competent states. Yet every predictive tier—from classic free‐energy minimization to deep learning—still underrepresents chemically modified nucleotides, the very substitutions that grant therapeutic aptamers nuclease resistance and pharmacokinetic longevity. Capturing the structural and dynamical consequences of these modifications remains the key unsolved problem. Progress, therefore, hinges on two fronts: richer parameterization and training data that encompass modified bases, and tighter coupling of in silico screens with biophysical and structural validation. Bridging these gaps will convert the current wave of computational advances into clinically relevant aptamer‐based drugs ready to be delivered to the patients. This article is categorized under: Structure and Mechanism > Molecular Structures Data Science > Computer Algorithms and Programming Data Science > Artificial Intelligence/Machine Learning

ABSTRACT By bridging molecular‐level insights with macroscopic performance metrics, computational strategies are poised to transform how we design next‐generation 3D‐printable materials with enhanced precision, functionality, and sustainability. We present a critical overview examining the role of computational methods in advancing the design and application of 3D‐printable polymers. We cover key considerations—including solvation behavior, viscosity, gel point, mechanical properties, and polymer structure—as well as the design of new polymer functionalities. We highlight how a spectrum of physics‐based methods, ranging from quantum chemical to coarse‐grained simulations, can be leveraged to interrogate relevant polymer properties at multiple scales. In particular, we illustrate the growing impact of machine learning in accelerating polymer discovery and optimization. Such methods, whether applied independently or integrated into multi‐scale modeling frameworks, offer powerful tools for pre‐screening and selecting optimal formulations tailored to diverse 3D printing technologies and applications. Although challenges remain to integrate different approaches into workable prediction pipelines, the rate of advance and improvements in methods, data interoperability, and data quality, offer great promise of a ‘concept to print’ pipeline in the future. This article is categorized under: Structure and Mechanism > Computational Materials Science Data Science > Artificial Intelligence/Machine Learning Structure and Mechanism > Molecular Structures

ABSTRACTVibrational spectroscopy is an indispensable analytical tool that provides structural fingerprints for molecules, solids, and interfaces thereof. This study introduces THeSeuSS (THz Spectra Simulations Software)—an automated computational platform that efficiently simulates IR and Raman spectra for both periodic and non‐periodic systems. Using DFT, DFTB and machine‐learning force field, THeSeuSS offers robust capabilities for detailed vibrational spectra simulations. Our extensive evaluations and benchmarks demonstrate that THeSeuSS accurately reproduces both previously calculated and experimental spectra, enabling precise comparisons and interpretations of vibrational characteristics in various test cases, including H2O and glycine molecules in the gas phase, as well as solid ammonia and solid ibuprofen. Designed with a user‐friendly interface and seamless integration with existing computational chemistry tools, THeSeuSS enhances the accessibility and applicability of advanced spectroscopic simulations, supporting research and development in chemical, pharmaceutical, and material sciences.

ABSTRACTRecent advances in single‐atom alloy (SAA) catalysts provide a unique platform for understanding spillover, due to the well‐defined nature of the active site for dissociative chemisorption. In particular, the use of spilled adsorbates following molecular dissociation on the host metal surface facilitates the generation of high‐value chemicals in subsequent catalytic reactions. Nevertheless, the factors that control the spillover process remain to be fully elucidated. This perspective discusses recent theoretical advances in the spillover dynamics on SAAs, with a particular focus on the dissociation and spillover processes of H2 and CH4. It provides valuable insights into how various factors, such as energy transfer, nuclear quantum effects, gas‐adsorbate interactions, and adsorbate size, impact the diffusion behavior of hydrogen and methyl species on SAA surfaces. The article concludes with a discussion of future prospects. This perspective underscores the significance of spillover dynamics in heterogeneous catalysis, with important implications for improving catalytic performance.

ABSTRACTProtein–protein interactions play a crucial role in human biological processes, and deciphering their structural information and interaction patterns is essential for drug development. The high costs of experimental structure determination have brought computational protein–protein docking methods into the spotlight. Traditional docking algorithms, which hinge on a sampling‐scoring framework, heavily rely on extensive sampling of candidate poses and customized scoring functions based on the geometric and chemical compatibility between proteins. However, these methods face challenges related to sampling efficiency and stability. The advent of deep learning (DL) has ushered in data‐driven docking methods that demonstrate significant advantages, particularly boosting the efficiency of protein–protein docking. We systematically review the historical development of protein–protein docking from traditional approaches to DL techniques and provide insights into emerging technologies in this field. Moreover, we summarize the commonly used datasets and evaluation metrics in protein–protein docking. We expect that this review can offer valuable guidance for the development of more efficient protein–protein docking algorithms.

ABSTRACTThe catalytic CO2 hydrogenation to produce valuable fuels and chemicals holds immense importance in addressing energy scarcity and environmental degradation. Given that the real catalytic reaction system is complex and dynamic, the structure of catalysts might experience dynamic evolution under real reaction conditions. It implies that the real active sites might only generated during the reaction process. The induction factor of dynamic evolution of active sites could be reactants, intermediates, products, and other local chemical environments. Utilizing in‐situ/operando characterization techniques allows for the real‐time observation of the dynamic evolution process, further combining multiscale theoretical simulations can effectively reveal the refined structure of real active sites and catalytic mechanisms. Herein, we summarized the latest advancements in understanding the dynamic active sites and catalytic mechanisms during the real reaction process for the CO2 hydrogenation to C1 products (CH3OH, CO, and CH4). The dynamic evolutions of the catalyst in morphology, size, valence state, and interface between active component and support were discussed, respectively. Future research could benefit from more in‐situ characterization and theoretical simulation to explore the microstructure and reaction mechanism, aiming to produce high conversion and selectivity catalysts for CO2 hydrogenation reactions.

ABSTRACTMany‐body Green's function theory in the GW approximation with the Bethe–Salpeter equation (BSE) provides a powerful framework for the first‐principles calculations of single‐particle and electron–hole excitations in perfect crystals and molecules alike. Application to complex molecular systems, for example, solvated dyes, molecular aggregates, thin films, interfaces, or macromolecules, is particularly challenging as they contain a prohibitively large number of atoms. Exploiting the often localized nature of excitation in such disordered systems, several methods have recently been developed in which GW‐BSE is applied to a smaller, tractable region of interest that is embedded into an environment described with a lower‐level method. Here, we review the various strategies proposed for such embedded many‐body Green's functions approaches, including quantum–quantum and quantum–classical embeddings, and focus in particular on how they include environment screening effects either intrinsically in the screened Coulomb interaction in the GW and BSE steps or via extrinsic electrostatic couplings.