A Universal Synthesis of Single-Atom Catalysts via Operando Bond Formation Driven by Electricity.

Single-atom catalysts (SACs), featuring highly uniform active sites, tunable coordination environments, and synergistic effects with support, have emerged as one of the most efficient catalysts for various reactions, particularly for electrochemical CO2 reduction (ECR). However, the scalability of SACs is restricted due to the limited choice of available support and problems that emerge when preparing SACs by thermal deposition. Here, an in situ reconstruction method for preparing SACs is developed with a variety of atomic sites, including nickel, cadmium, cobalt, and magnesium. Driven by electricity, different oxygen-containing metal precursors, such as MOF-74 and metal oxides, are directly atomized onto nitrogen-doped carbon (NC) supports, yielding SACs with variable metal active sites and coordination structures. The electrochemical force facilitates the in situ generation of bonds between the metal and the supports without the need for additional complex steps. A series of MNxOy (M denotes metal) SACs on NC have been synthesized and utilized for ECR. Among these, NiNxOy SACs using Ni-MOF-74 as a metal precursor exhibit excellent ECR performance. This universal and general SAC synthesis strategy at room temperature is simpler than most reported synthesis methods to date, providing practical guidance for the design of the next generation of high-performance SACs.

A photoelectrocatalytic system as a reaction platform for selective radical-radical coupling.

The selection of electrode material is a critical factor that determines the selectivity of electrochemical organic reactions. However, the fundamental principles governing this relationship are still largely unexplored. Herein, we demonstrate a photoelectrocatalytic (PEC) system as a promising reaction platform for the selective radical-radical coupling reaction owing to the inherent charge-transfer properties of photoelectrocatalysis. As a model reaction, the radical trifluoromethylation of arenes is shown on hematite photoanodes without employing molecular catalysts. The PEC platform exhibited superior mono- to bis-trifluoromethylated product selectivity compared to conventional electrochemical methods utilizing conducting anodes. Electrochemical and density functional theory (DFT) computational studies revealed that controlling the kinetics of anodic oxidation of aromatic substrates is essential for increasing reaction selectivity. Only the PEC configuration could generate sufficiently high-energy charge carriers with controlled kinetics due to the generation of photovoltage and charge-carrier recombination, which are characteristic features of semiconductor photoelectrodes. This study opens a novel approach towards selective electrochemical organic reactions through understanding the intrinsic physicochemical properties of semiconducting materials.

A large-scale reaction dataset of mechanistic pathways of organic reactions.

Understanding organic reaction mechanisms is crucial for interpreting the formation of products at the atomic and electronic level, but still remains as a domain of knowledgeable experts. The lack of a large-scale dataset with chemically reasonable mechanistic sequences also hinders the development of reliable machine learning models to predict organic reactions based on mechanisms as human chemists do. Here, we present a high-quality and the first large-scale reaction dataset, denoted as mech-USPTO-31K, with chemically reasonable arrow-pushing diagrams validated by synthetic chemists, encompassing a wide spectrum of polar organic reaction mechanisms. We envision this dataset curated by applying a simple and flexible method that automatically generates reaction mechanisms using autonomously extracted reaction templates and expert-coded mechanistic templates to become an invaluable tool to develop future reaction outcome prediction models and discover new reactions.

Estimating the synthetic accessibility of molecules with building block and reaction-aware SAScore.

Synthetic accessibility prediction is a task to estimate how easily a given molecule might be synthesizable in the laboratory, playing a crucial role in computer-aided molecular design. Although synthesis planning programs can determine synthesis routes, their slow processing times make them impractical for large-scale molecule screening. On the other hand, existing rapid synthesis accessibility estimation methods offer speed but typically lack integration with actual synthesis routes and building block information. In this work, we introduce BR-SAScore, an enhanced version of SAScore that integrates the available building block information (B) and reaction knowledge (R) from synthesis planning programs into the scoring process. In particular, we differentiate fragments inherent in building blocks and fragments to be derived from synthesis (reactions) when scoring synthetic accessibility. Compared to existing methods, our experimental findings demonstrate that BR-SAScore offers more accurate and precise identification of a molecule's synthetic accessibility by the synthesis planning program with a fast calculation time. Moreover, we illustrate how BR-SAScore provides chemically interpretable results, aligning with the capability of the synthesis planning program embedded with the same reaction knowledge and available building blocks.Scientific contributionWe introduce BR-SAScore, an extension of SAScore, to estimate the synthetic accessibility of molecules by leveraging known building-block and reactivity information. In our experiments, BR-SAScore shows superior prediction performance on predicting molecule synthetic accessibility compared to previous methods, including SAScore and deep-learning models, while requiring significantly less computation time. In addition, we show that BR-SAScore is able to precisely identify the chemical fragment contributing to the synthetic infeasibility, holding great potential for future molecule synthesizability optimization.

Large Language Models for Inorganic Synthesis Predictions.

We evaluate the effectiveness of pretrained and fine-tuned large language models (LLMs) for predicting the synthesizability of inorganic compounds and the selection of precursors needed to perform inorganic synthesis. The predictions of fine-tuned LLMs are comparable to─and sometimes better than─recent bespoke machine learning models for these tasks but require only minimal user expertise, cost, and time to develop. Therefore, this strategy can serve both as an effective and strong baseline for future machine learning studies of various chemical applications and as a practical tool for experimental chemists.

Reaction Templates: Bridging Synthesis Knowledge and Artificial Intelligence.

ConspectusThe field of chemical research boasts a long history of developing software to automate synthesis planning and reaction prediction. Early software relied heavily on expert systems, requiring significant effort to encode vast amounts of synthesis knowledge into a computer-readable format. However, recent advancements in deep learning have shifted the focus toward AI models, offering improved prediction capabilities. Despite these advancements, current AI models often lack the integration of known synthesis rules and intuitions, creating a gap that hinders interpretability and future development of the models. To bridge them, our research group has been actively working on incorporating reaction templates into deep learning models, achieving promising results across various applications.In this Account, we present our latest works to incorporate the known synthesis knowledge into the deep learning models through the utilization of reaction templates. We begin by highlighting the limitations of early computer programs heavily reliant on hand-coded rules. These programs, while providing a foundation for the field, presented limitations in scalability and adaptability. We then introduce SMARTS (SMILES arbitrary target specification), a popular Python-readable format for representing chemical reactions. This format of reaction encoding facilitates the quick integration of synthesis knowledge into AI models built using the Python language. With the SMARTS-based reaction templates, we introduce our recent efforts of developing an AI model for reaction-based molecule optimization. Subsequently, we discuss the recent efforts to automate the extraction of reaction templates from vast chemical reaction databases. This approach eliminates the previously required manual effort of encoding knowledge, a process that could be time-consuming and prone to error when dealing with large data sets. By customizing the automated extraction algorithm, we have developed powerful AI models for specific tasks such as retrosynthesis (LocalRetro), reaction outcome prediction (LocalTransform), and atom-to-atom mapping (LocalMapper). These models, aligned with the intuition of chemists, demonstrate the effectiveness of incorporating reaction templates into deep learning frameworks.Looking toward the future, we believe that utilizing reaction templates to connect known chemical knowledge and AI models holds immense potential for various applications. Not only can this approach significantly benefit future AI models focused on challenging tasks like reaction mechanism labeling and prediction, but we anticipate it can also extend its reach to the realm of inorganic synthesis. By integrating synthesis knowledge, we can not only achieve improved performance but also enhance the interpretability of AI models, paving the way for further advancements in AI-powered chemical synthesis.

Effect of cinnamon powder on quality attributes and off-flavor in fried chicken drumsticks made from long-term thawed Korean native chicken.

The effect of cinnamon powder on the quality and mitigation of off-flavor in fried chicken drumsticks made from long-term thawed Korean native chicken (Woorimatdag No. 1, WRMD1) was investigated. The WRMD1 drumsticks were categorized into 5 groups: conventional thawing (16 h, CT), long-term thawing (48 h, LT), cinnamon powder added into 'LT' as marinade (0.03%, CM) or incorporated into the batter (1.35%, CB), and long-term thawing with cinnamon powder incorporated both in the marinade and batter (0.03% + 1.35%, CMB). The crude fat content was significantly higher in the CT and CMB than that of the CB. The CM, CB, and CMB showed significantly lower levels of 2-thiobarbituric acid reactive substance compared with the CT and LT. The predominant fatty acids in all treatments were C18:1n9, C18:2n6, and C16:0. The LT displayed lower total unsaturated fatty acid content than the CT (P < 0.05). The CM effectively decreased lipid oxidative volatiles, such as 1-octanol, 1-octen-3-ol, and 2-octen-1-ol, (E), in the LT (P < 0.05). Both the CM and CB showed an inclination to increase specific pyrazines associated with pleasant notes compared with the LT, and showed higher levels of pyrazines, such as pyrazine, 2-ethyl-6-methyl-, and pyrazine, 3-ethyl-2,5-dimethyl-, than those of the CMB (P < 0.05). The CM contained higher levels of 2,3-butanedione when compared with the other groups (P < 0.05). Multivariate analysis demonstrated that cinnamon had an effect in discriminating the treatment groups with cinnamon addition from both the CT and LT, whereas the CM, CB, and CMB formed distinct clusters. The CM and CMB received significantly higher aroma scores from panelists in comparison to the other groups. These findings suggest that the CM (0.03% cinnamon powder) can be used to enhance the aroma in fried WRMD1 drumsticks by reducing or masking the off-flavor volatiles associated with long-term thawing.

Precise atom-to-atom mapping for organic reactions via human-in-the-loop machine learning.

Atom-to-atom mapping (AAM) is a task of identifying the position of each atom in the molecules before and after a chemical reaction, which is important for understanding the reaction mechanism. As more machine learning (ML) models were developed for retrosynthesis and reaction outcome prediction recently, the quality of these models is highly dependent on the quality of the AAM in reaction datasets. Although there are algorithms using graph theory or unsupervised learning to label the AAM for reaction datasets, existing methods map the atoms based on substructure alignments instead of chemistry knowledge. Here, we present LocalMapper, an ML model that learns correct AAM from chemist-labeled reactions via human-in-the-loop machine learning. We show that LocalMapper can predict the AAM for 50 K reactions with 98.5% calibrated accuracy by learning from only 2% of the human-labeled reactions from the entire dataset. More importantly, the confident predictions given by LocalMapper, which cover 97% of 50 K reactions, show 100% accuracy for 3,000 randomly sampled reactions. In an out-of-distribution experiment, LocalMapper shows favorable performance over other existing methods. We expect LocalMapper can be used to generate more precise reaction AAM and improve the quality of future ML-based reaction prediction models.

Variational quantum eigensolver for closed-shell molecules with non-bosonic corrections.

The realization of quantum advantage with noisy-intermediate-scale quantum (NISQ) machines has become one of the major challenges in computational sciences. Maintaining coherence of a physical system with more than ten qubits is a critical challenge that motivates research on compact system representations to reduce algorithm complexity. Toward this end, the variational quantum eigensolver (VQE) used to perform quantum simulations is considered to be one of the most promising algorithms for quantum chemistry in the NISQ era. We investigate reduced mapping of one spatial orbital to a single qubit to analyze the ground state energy in a way that the Pauli operators of qubits are mapped to the creation/annihilation of singlet pairs of electrons. To include the effect of non-bosonic (or non-paired) excitations, we introduce a simple correction scheme in the electron correlation model approximated by the geometrical mean of the bosonic (or paired) terms. Employing it in a VQE algorithm, we assess ground state energies of H2O, N2, and Li2O in good agreement with full configuration interaction (FCI) models respectively, using only 6, 8, and 12 qubits with quantum gate depths proportional to the squares of the qubit counts. With the adopted seniority-zero approximation that uses only one half of the qubit counts of a conventional VQE algorithm, we find that our non-bosonic correction method reaches reliable quantum chemistry simulations at least for the tested systems.

Identifying the active sites and intermediates on copper surfaces for electrochemical nitrate reduction to ammonia.

Copper (Cu) is a widely used catalyst for the nitrate reduction reaction (NO3RR), but its susceptibility to surface oxidation and complex electrochemical conditions hinders the identification of active sites. Here, we employed electropolished metallic Cu with a predominant (100) surface and compared it to native oxide-covered Cu. The electropolished Cu surface rapidly oxidized after exposure to either air or electrolyte solutions. However, this oxide was reduced below 0.1 V vs. RHE, thus returning to the metallic Cu before NO3RR. It was distinguished from the native oxide on Cu, which remained during NO3RR. Fast NO3- and NO reduction on the metallic Cu delivered 91.5 ± 3.7% faradaic efficiency for NH3 at -0.4 V vs. RHE. In contrast, the native oxide on Cu formed undesired products and low NH3 yield. Operando shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS) analysis revealed the adsorbed NO3-, NO2, and NO species on the electropolished Cu as the intermediates of NH3. Low overpotential NO3- and NO adsorptions and favorable NO reduction are key to increased NH3 productivity over Cu samples, which was consistent with the DFT calculation on Cu(100).

Physicochemical Features and Volatile Organic Compounds of Horse Loin Subjected to Sous-Vide Cooking.

The purpose of this study was to evaluate the effect of temperature and time of sous-vide cooking method on the characteristics of Thoroughbred horse loin. Sliced portions (200 ± 50 g) were cooked by boiling (control) and sous-vide (65 and 70 °C for 12, 18, and 24 h). The samples were analyzed for proximate composition, pH, color, texture, microstructure, sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), microbiology, volatile organic compounds (VOCs), nucleotide content, and fatty acids composition. The color analysis showed decreased redness at elevated temperatures. Improved tenderness, demonstrated by reduced shear force values (36.36 N at 65 °C for 24 h and 35.70 N at 70 °C for 24 h). The micrographs indicated dense fiber arrangements at 70 °C. The SDS-PAGE revealed muscle protein degradation with extended sous-vide cooking. The VOC analysis identified specific compounds, potentially distinctive markers for sous-vide cooking of horse meat including 1-octen-3-ol, decanal, n-caproic acid vinyl ester, cyclotetrasiloxane, octamethyl, and 3,3-dimethyl-1,2-epoxybutane. This study highlights the cooking time's primary role in sous vide-cooked horse meat tenderness and proposes specific VOCs as potential markers. Further research should explore the exclusivity of these VOCs to sous-vide cooking.

Identification and comparison of aroma and taste-related compounds from breast meat of three breeds of Korean native chickens.

This study was aimed to identify and compare the taste-related compounds (nucleotide-related compounds, free amino acid contents, and fatty acid composition) and aroma (volatile organic compounds [VOC]) compounds in the chicken breast meat from 3 kinds of Korean native chicken (KNC), namely Hanhyup 3 (HH3), Woorimatdag 1 (WRMD1) and Woorimatdag 2 (WRMD2). Among the 3 breeds, WRMD1 had significantly higher IMP and AMP contents than HH3. WRMD2 exhibited higher levels of umami and sweet-taste amino acids and oleic acid composition compared to HH3 (P < 0.05). HH3 showed a higher composition of unsaturated fatty acids than WRMD2 (P < 0.05). On their discrimination by flavor composition, some compounds including aspartic acid were analyzed as important compounds. Regarding aroma compounds, unique aroma compounds were detected for each breed and some compounds such as isopropyl myristate, p-cresol, (S)-(+)-3-Methyl-1-pentanol, and cyclic octa-atomic sulfur were expected to be utilized as key compounds in discrimination of the 3 breeds. From the result of this study, the differences on the flavor compounds of three breeds were elucidated and key compounds for their discrimination were presented.

Predicting synthesis recipes of inorganic crystal materials using elementwise template formulation.

While advances in computational techniques have accelerated virtual materials design, the actual synthesis of predicted candidate materials is still an expensive and slow process. While a few initial studies attempted to predict the synthesis routes for inorganic crystals, the existing models do not yield the priority of predictions and could produce thermodynamically unrealistic precursor chemicals. Here, we propose an element-wise graph neural network to predict inorganic synthesis recipes. The trained model outperforms the popularity-based statistical baseline model for the top-k exact match accuracy test, showing the validity of our approach for inorganic solid-state synthesis. We further validate our model by the publication-year-split test, where the model trained based on the materials data until the year 2016 is shown to successfully predict synthetic precursors for the materials synthesized after 2016. The high correlation between the probability score and prediction accuracy suggests that the probability score can be interpreted as a measure of confidence levels, which can offer the priority of the predictions.

Accelerated chemical science with AI.

In light of the pressing need for practical materials and molecular solutions to renewable energy and health problems, to name just two examples, one wonders how to accelerate research and development in the chemical sciences, so as to address the time it takes to bring materials from initial discovery to commercialization. Artificial intelligence (AI)-based techniques, in particular, are having a transformative and accelerating impact on many if not most, technological domains. To shed light on these questions, the authors and participants gathered in person for the ASLLA Symposium on the theme of 'Accelerated Chemical Science with AI' at Gangneung, Republic of Korea. We present the findings, ideas, comments, and often contentious opinions expressed during four panel discussions related to the respective general topics: 'Data', 'New applications', 'Machine learning algorithms', and 'Education'. All discussions were recorded, transcribed into text using Open AI's Whisper, and summarized using LG AI Research's EXAONE LLM, followed by revision by all authors. For the broader benefit of current researchers, educators in higher education, and academic bodies such as associations, publishers, librarians, and companies, we provide chemistry-specific recommendations and summarize the resulting conclusions.

Physicochemical Properties and Volatile Organic Compounds of Dairy Beef Round Subjected to Various Cooking Methods.

To evaluate the effect of different cooking methods on the physicochemical quality and volatile organic compounds (VOC) of dairy beef round, twelve beef round pieces were divided into four groups: raw, boiling, microwave, and sous-vide. The sous-vide group had a higher pH than the boiling or microwave groups. The boiling group exhibited the highest shear force and CIE L*, followed by the microwave and sous-vide groups (p<0.05). The sous-vide group received higher taste and tenderness scores from panelists (p<0.05) and showed significantly higher levels of aspartic and glutamic acids than the other groups. The sous-vide and microwave groups had the highest oleic acid and polyunsaturated fatty acid levels, respectively. The sous-vide group had significantly higher hypoxanthine and inosine levels than the other groups. However, the microwave group had higher inosine monophosphate levels than the other groups. The sous-vide group had a higher alcohol content, including 1-octen-3-ol, than the other groups. Octanal and nonanal were the most abundant aldehydes in all groups. (R)-(-)-14-methyl-8-hexadecyn-1-ol, p-cresol, and 1-tridecyne were used to distinguish the VOC for each group in the multivariate analysis. Sous-vide could be effective in increasing meat tenderness as well as taste-related free amino acid (aspartic acid and glutamic acid) and fatty acid (oleic acid) levels. Furthermore, specific VOC, including 1-octen-3-ol, 2-ethylhexanal ethylene glycol acetal, and 2-octen-1-ol, (E)-, could be potential markers for distinguishing sous-vide from other cooking methods. Further studies are required to understand the mechanisms underlying the predominant association of these VOC with the sous-vide cooking method.

Exploring the Diverse Landscape of Biaryl-Containing Peptides Generated by Cytochrome P450 Macrocyclases.

Cytochrome P450 enzymes (P450s) catalyze diverse oxidative cross-coupling reactions between aromatic substrates in the natural product biosynthesis. Specifically, P450s install distinct biaryl macrocyclic linkages in three families of ribosomally synthesized and post-translationally modified peptides (RiPPs). However, the chemical diversity of biaryl-containing macrocyclic RiPPs remains largely unexplored. Here, we demonstrate that P450s have the capability to generate diverse biaryl linkages on RiPPs, collectively named "cyptides". Homology-based genome mining for P450 macrocyclases revealed 19 novel groups of homologous biosynthetic gene clusters (BGCs) with distinct aromatic residue patterns in the precursor peptides. Using the P450-modified precursor peptides heterologously coexpressed with corresponding P450s in Escherichia coli, we determined the NMR structures of three novel biaryl-containing peptides─the enzymatic products, roseovertin (1), rubrin (2), and lapparbin (3)─and confirmed the formation of three unprecedented or rare biaryl linkages: Trp C-7'-to-His N-τ in 1, Trp C-7'-to-Tyr C-6 in 2, and Tyr C-6-to-Trp N-1' in 3. Biochemical characterization indicated that certain P450s in these pathways have a relaxed substrate specificity. Overall, our studies suggest that P450 macrocyclases have evolved to create diverse biaryl linkages in RiPPs, promoting the exploration of a broader chemical space for biaryl-containing peptides encoded in bacterial genomes.

Correlated Local Fluctuations in the Hydrogen Bond Network of Liquid Water.

The hypothesis that liquid water can separate into two phases in the supercooled state has been supported by recent experimental and theoretical studies. However, whether such structural inhomogeneity extends to ambient conditions is under intense debate. Due to the dynamic nature of the hydrogen bond network of liquid water, exploring its structure requires detailed insight into the collective motion of neighboring water molecules, a missing link that has not been examined so far. Here, highly sensitive quantum mechanical calculations detect that the time evolution of nearby hydrogen bonds is strongly correlated, revealing a direct mechanism for the appearance of short-range structural fluctuations in the hydrogen bond network of liquid water for the first time. This correlated dynamics is found to be closely connected to the static structural picture. The distortions from the tetrahedral structure do not occur independently but are correlated due to the preference of nearby donors and acceptors to be in similar environments. The existence of such cooperative fluctuations is further supported by the temperature dependence of the local structural evolution and explained by conventional analysis of localized orbitals. It was found that such correlated structural fluctuations are only observed on a short length scale in simulations at ambient conditions. The correlations of the nearby hydrogen bond pairs of liquid water unveiled here are expected to offer a new insight into connecting the dynamics of individual water molecules and the local structure of the hydrogen bond network.

Single-Atom Molybdenum-N3 Sites for Selective Hydrogenation of CO2 to CO.

The design of efficient non-noble metal catalysts for CO2 hydrogenation to fuels and chemicals is desired yet remains a challenge. Herein, we report that single Mo atoms with a MoN3 (pyrrolic) moiety enable remarkable CO2 adsorption and hydrogenation to CO, as predicted by density functional theory studies and evidenced by a high and stable conversion of CO2 reaching about 30.4 % with a CO selectivity of almost 100 % at 500 °C and very low H2 partial pressure. Atomically dispersed MoN3 is calculated to facilitate CO2 activation and reduces CO2 to CO* via the direct dissociation path. Furthermore, the highest transition state energy in CO formation is 0.82 eV, which is substantially lower than that of CH4 formation (2.16 eV) and accounts for the dominant yield of CO. The enhanced catalytic performances of Mo/NC originate from facile CO desorption with the help of dispersed Mo on nitrogen-doped carbon (Mo/NC), and in the absence of Mo nanoparticles. The resulting catalyst preserves good stability without degradation of CO2 conversion rate even after 68 hours of continuous reaction. This finding provides a promising route for the construction of highly active, selective, and robust single-atom non-precious metal catalysts for reverse water-gas shift reaction.

Atomic-Scale Homogeneous RuCu Alloy Nanoparticles for Highly Efficient Electrocatalytic Nitrogen Reduction.

Ruthenium (Ru) is the most widely used metal as an electrocatalyst for nitrogen (N2 ) reduction reaction (NRR) because of the relatively high N2 adsorption strength for successive reaction. Recently, it has been well reported that the homogeneous Ru-based metal alloys such as RuRh, RuPt, and RuCo significantly enhance the selectivity and formation rate of ammonia (NH3 ). However, the metal combinations for NRR have been limited to several miscible combinations of metals with Ru, although various immiscible combinations have immense potential to show high NRR performance. In this study, an immiscible combination of Ru and copper (Cu) is first utilized, and homogeneous alloy nanoparticles (RuCu NPs) are fabricated by the carbothermal shock method. The RuCu homogeneous NP alloys on cellulose/carbon nanotube sponge exhibit the highest selectivity and NH3 formation rate of ≈31% and -73 µmol h-1 cm-2 , respectively. These are the highest values of the selectivity and NH3 formation rates among existing Ru-based alloy metal combinations.