jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jonas Rein | Bartosz Górski | Ayça M Keskin | Minh Hoang Le | Song Lin
Source: Journal of the American Chemical Society (article, 2026-02-26)
Field: Chemistry — Organic Chemistry

Desymmetrization of <i>meso</i> -Pyrrolidines via Oxoammonium-Catalyzed Enantioselective Hydride Transfer We report the oxidative desymmetrization of urea-protected pyrrolidines via site-selective hydride transfer from enantiotopic C-H bonds. The optimal oxoammonium-peptide conjugate catalyst provided over 90% <i>ee</i> across all tested pyrrolidines, providing products that can readily undergo subsequent N-deprotection and other derivatization reactions to form medicinally relevant compounds. We isolated key on-cycle catalytic intermediates, which allowed us to elucidate both the mechanism of catalytic activation and the origin of stereochemical induction in detail. In particular, a stereochemical model for asymmetric induction emerged from analyzing a covalent catalyst-substrate adduct, which served as an isolable analog of the enantiodetermining transition state. In this model, a tight hydrogen bond between the urea protecting group and the peptide directs the asymmetric hydride transfer. Desymmetrization ; Hydride ; Enantioselective synthesis ; Catalysis ; Asymmetric induction ; Conjugate ; Derivatization ; Covalent bond Asymmetric Synthesis and Catalysis ; Coordination Chemistry and Organometallics ; Asymmetric Hydrogenation and Catalysis
Authors: Mohammed Razeeth Shait Mohammed | Zubaida Khaddar Elias | Shakir M. Saied | Mohanad Saleh
Source: Applied Chemical Engineering (article, 2026-03-10)
Field: Chemistry — Organic Chemistry

A review of the past decade of Azlactones: Synthesis, Reactions, Biomedical and Pharmaceutical applications Azlactones (2,4-disubstituted oxazol-5-ones) are pivotal building blocks in pharmaceutical design due to their dual electrophilic-nucleophilic reactivity and structural plasticity. Their strained ring system enables diverse ring-opening pathways, facilitating rapid construction of peptidomimetics and bioactive conjugates. Recent studies demonstrate potent antibacterial and antifungal properties such as pyridinyl-functionalized azlactones exhibit efficacy against MRSA (MIC = 0.78 µg/mL), while azlactone-triazole hybrids inhibit Candida auris (MIC = 1.56 µg/mL) and Aspergillus fumigatus via ergosterol synthesis disruption. Additionally, spiro-azlactones show anticancer potential against MCF-7 and A549 cell lines through DNA interaction, tubulin polymerization inhibition, and cell cycle disruption. The spirocyclic structure enhances selectivity, target affinity, and metabolic stability. Antifungal ; Peptidomimetic ; Aspergillus fumigatus ; Ergosterol ; Tubulin ; Econazole Synthesis and Reactions of Organic Compounds ; Synthesis and Biological Evaluation ; Bioactive Compounds and Antitumor Agents
Authors: Lechen Dong | Fang Liu
Source: ChemRxiv (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

Accelerating Quantum Chemistry Automation: Early Detection and Recovery of SCF Convergence Failure via Time Series Feature Extraction Self-consistent field (SCF) convergence failures bottleneck high-throughput quantum chemistry, particularly in open-shell systems. To address this, we introduce an autonomous pipeline treating the electronic structure descriptors from initial spin-unrestricted SCF iterations as a time series. Using statistical descriptors extracted from just the first ten iterations, a lightweight gradient-boosted classifier accurately predicts convergence failures. The model achieves 94.8% accuracy on 55,000 test molecules after training on only 10,000 examples. Coupling this early-warning predictor with a 𝛽-level-shift restart (𝛽-RST) heuristic, derived from Bayesian Optimization of the shift parameters, enables automated recovery. Across six diverse calculation settings, our pipeline successfully rescued 57.5% of inherently difficult-to-converge molecules while maintaining a 94.1% convergence rate for easy cases, resulting in a 460,000-iteration reduction across the 1200 calculations. Ultimately, this framework provides a highly transferable, data-efficient, and autonomous solution for largescale electronic structure calculations. Bottleneck ; Convergence (economics) ; Pipeline (software) ; Rate of convergence ; Series (stratigraphy) ; Classifier (UML) ; Bayesian probability ; Field (mathematics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Pinar Degirmencioglu | Hüseyin Arbağ | Nuray Oktar
Source: Research on Chemical Intermediates (article, 2026-01-12)
Field: Chemistry — Inorganic Chemistry

HZSM-5-doped silica microsphere catalysts for methanol-to-olefins reaction: effect of HZSM5/SiO2 ratio Abstract The methanol-to-olefins (MTO) process provides a promising non-oil-based route for light olefin production, but conventional HZSM-5 catalysts suffer from coke deposition and rapid deactivation. In this study, HZSM-5 was incorporated into silica microspheres (MS) via a novel microencapsulation strategy specifically designed to enable simultaneous tuning of acidity and porosity, addressing limitations of traditional catalyst modification methods. Catalysts with varying HZSM-5/SiO 2 weight ratios (0, 0.1, 0.25, 0.5) were synthesized and characterized using XRD, BET, DRIFTS, SEM, TEM, TGA, FTIR, and ICP-OES. Among them, 0.25HZSM-MS exhibited a balanced micro-mesoporous structure together with moderate Lewis/Brønsted acidity, achieving 85.6% methanol conversion with 48.7% light olefin selectivity. Remarkably, coke formation was reduced by 77% compared with parent HZSM-5, and activity was maintained over eight regeneration cycles. These findings highlight, for the first time, that this microencapsulation-based control of HZSM-5 dispersion within silica microspheres represents a distinct methodological advance, enabling synergistic tuning of acidity and porosity and offering a durable and selective catalyst for MTO applications. Catalysis ; Olefin fiber ; Coke ; Porosity ; Dispersion (optics) ; Methanol Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Catalysis for Biomass Conversion
Authors: Yunlong Wang | Zhixin Liang | Chi Ding | Junjie Wang | Zheyong Fan | Hui-Tian Wang | Dingyu Xing | Jian Sun
Source: ArXiv.org (article, 2026-01-20)
Field: Materials Science — Materials Chemistry

GPUTB-2:An efficient E(3) network method for learning high-precision orthogonal Hamiltonian Although equivariant neural networks have become a cornerstone for learning electronic Hamiltonians, the intrinsic non-orthogonality of linear combinations of atomic orbitals (LCAO) basis sets poses a fundamental challenge. The computational cost of Hamiltonian orthogonalization scales as O(N^3), which severely hinders electronic structure calculations for large-scale systems containing hundreds of thousands to millions of atoms. To address this issue, we develop GPUTB-2, a framework that learns implicitly orthogonality-preserving Hamiltonians by training directly on electronic band structures. Benefiting from an E(3)-equivariant network accelerated by Gaunt tensor product and SO(2) tensor product layers, GPUTB-2 achieves significantly higher accuracy than GPUTB across multiple benchmark systems. Moreover, GPUTB-2 accurately predicts large-scale electronic structures, including transport properties of temperature-perturbed SnSe and the band structures of magic-angle twisted bilayer graphene. By further integrating this framework with the linear-scaling quantum transport (LSQT) method, we investigate the electronic properties of million-atom amorphous graphene and uncover pressure-induced electronic structure transitions in more complex amorphous silicon. Together, these results establish GPUTB-2 as a high-accuracy and scalable approach for predicting orthogonal Hamiltonians. Orthogonalization ; Electronic structure ; Tensor product ; Hamiltonian (control theory) ; Artificial neural network ; Atomic orbital ; Scalability ; Deep learning Machine Learning in Materials Science ; Quantum many-body systems ; 2D Materials and Applications
Authors: Nadia Ziani | Chaima Salmi | Mohammed Laid Tedjani | Salah Eddine Laouini | Abderrhmane Bouafia | Fatima Zohra Zeggai | K. Bachari
Source: Journal of Sol-Gel Science and Technology (article, 2026-02-05)
Field: Materials Science — Biomaterials

CaCO₃@SiO₂ core–shell nanocomposites: advanced synthesis and multifunctional applications in CO₂ methanation, photocatalytic degradation, and molecular docking This study presents the synthesis, characterization, and multifunctional applications of Calcium carbonate–silica (CaCO₃@SiO₂) core–shell nanocomposite (NCs) aimed at addressing environmental challenges. The NCs were synthesized using a co-precipitation and sol-gel method, resulting in core-shell structures that were thoroughly characterized through various techniques, including X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and UV-Visible spectroscopy. The catalytic performance of the nanocomposites was evaluated for CO₂ methanation, achieving an impressive conversion rate of 87.84% at 360°C, demonstrating their potential for sustainable carbon utilization. Additionally, the photocatalytic activity was assessed through the degradation of Evans Blue dye and Fenitrothion pesticide under solar irradiation, with removal efficiencies of 99.4% and 99.92%, respectively, within 140 minutes. In-silico molecular docking studies indicated strong binding affinities with key bacterial and viral protein targets, suggesting potential antibacterial and antiviral applications. The results highlight the multifunctional capabilities of CaCO₃@SiO₂ NCs as promising materials for carbon capture, water purification, and biomedical applications, paving the way for innovative solutions in environmental and energy sectors. Photocatalysis ; Nanocomposite ; Catalysis ; Human health ; Solar energy conversion ; Carbon fibers Calcium Carbonate Crystallization and Inhibition ; Carbon dioxide utilization in catalysis ; Microbial Applications in Construction Materials
Authors: Ilia A. Plugin | Н. П. Симоненко | Е. П. Симоненко | Tatiana L. Simonenko | Alexey S. Varezhnikov | Maksim A. Solomatin | Victor V. Sysoev | Nikolay T. Kuznetsov
Source: Sensors (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Chemiresistive Effect in Ti0.2V1.8C MXene/Metal Oxide Hetero-Structured Composites Two-dimensional carbide crystals (MXenes) are emerging as a promising platform for the development of novel gas sensors, offering advantages in energy efficiency and tunable analyte selectivity. One of the most effective strategies to enhance and tailor their functional performance involves forming hetero-structured composites with metal oxides. In this work, we explore a chemiresistive effect in double-metal MXene of Ti<sub>0.2</sub>V<sub>1.8</sub>C and its composites with 2 mol. % SnO<sub>2</sub> and Co<sub>3</sub>O<sub>4</sub> nanocrystalline oxides toward feasibility tests with alcohol and ammonia vapor probes. The materials were characterized by simultaneous thermal analysis, X-ray diffraction analysis, Raman spectroscopy, and scanning/transmission electron microscopy. Gas-sensing experiments were carried out on composite layers deposited on multi-electrode substrates to be exposed to the test gases, 200-2000 ppm concentrations, at an operating temperature of 370 °C. The developed sensor array demonstrated clear analyte discrimination. The distinct sensor responses enabled a selective identification of vapors through linear discriminant analysis, demonstrating the further potential of MXene-based materials for integrated electronic nose applications. Electronic nose ; Nanocrystalline material ; Raman spectroscopy ; Oxide ; Composite number ; Carbide ; Analyte ; Chemical vapor deposition MXene and MAX Phase Materials ; Gas Sensing Nanomaterials and Sensors ; 2D Materials and Applications
Authors: Shubhajit Das | Ruben Laplaza | Thanapat Worakul | Clémence Corminboeuf
Source: Chemical Science (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Inverse design of frustrated Lewis pairs for direct catalytic CO <sub>2</sub> hydrogenation: refining and expanding design rules Frustrated Lewis pairs (FLPs), composed of reactive combinations of Lewis acids (LAs) and bases (LBs) offer a metal-free platform for catalyzing a wide range of chemical transformations. Designing the optimal FLP active site for a particular chemical reaction is a challenging task due to the lack of rigorous principles and countless chemical possibilities. We recently designed principles, which outline the relative disposition (<i>i.e.</i>, distance and angle) and chemical composition of the LA and LB centers that maximize activity in B- and N-based FLPs. These criteria were already used to screen 25 000 FLP active sites built on N-containing linkers extracted from the CoRE MOF dataset, but in such an enormous multifunctional catalyst space, inverse design approaches provide a more efficient mean to explore all possible combinations. Here, we use the NaviCatGA genetic algorithm to simultaneously optimize the chemical and geometrical characteristics of intramolecular FLPs while considering synthetic complexity and catalyst quenching constraints. By integrating activity maps and non-linear regression models, our workflow explores a vast chemical space of 1.7 billion FLP candidates built from organic fragments curated from the literature-released as the open-source FragFLP25 dataset-to identify optimal compositions suitable for catalytic CO<sub>2</sub> hydrogenation. Analyzing the top candidates extracted from various Pareto fronts in the catalyst space, we not only uncover active FLP motifs for hydrogenation that have not been previously reported but also refine and extend the design principles previously established from our high-throughput screening study. Frustrated Lewis pair ; Inverse ; Catalysis ; Chemical space ; Workflow ; Intramolecular force ; Refining (metallurgy) ; Lewis acids and bases Organoboron and organosilicon chemistry ; Carbon dioxide utilization in catalysis ; N-Heterocyclic Carbenes in Organic and Inorganic Chemistry
Authors: Yanglili ZHOU | Weihua WANG | Ziwei Zhao
Source: Zhongguo Kexueyuan yuankan (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Artificial intelligence-driven materials science: Evolution, framework, dilemmas, and breakthroughs Artificial intelligence-driven materials science (AIMS) represents a revolutionary and disruptive paradigm in materials research, promising to fundamentally break through the traditional bottlenecks of research cycles and efficiency. Historically, the evolution of materials science research paradigms from empirical trial and error, theoretical modeling, and computational simulation to the new data-driven stage has been driven by innovations in cognitive tools and methods. Currently, artificial intelligence, as a disruptive cognitive tool, is fundamentally reconstructing the core elements and interaction logic of materials science: the research process achieves intelligent iteration and full-process closed-loop; the capabilities of researchers are reshaped and teams are organized; and the depth and breadth of research objects are expanded and precise demands are locked in, thus forming a new model and paradigm for materials research. This is precisely the manifestation of the emergence and development of new quality productivity in the field of materials science. Nevertheless, China still faces bottlenecks such as multi-scale modeling, multi-modal data fusion, and model generalization in the AIMS field. These need to be overcome through solidifying the data foundation, tackling core technologies, and improving ecological support, in order to achieve a fundamental transformation of the materials science research paradigm. This study reviews the evolution and current status of research paradigms in materials science, constructs a theoretical framework for the transformation of elements in AIMS, analyzes the difficulties and bottlenecks faced by AIMS, and proposes strategic paths and strategies for promoting the development of AIMS in China, with the aim of providing theoretical and practical references for accelerating the formation of the AIMS paradigm system and fostering new material productivity. Process (computing) ; Field (mathematics) ; Quality (philosophy) ; Paradigm shift ; Core (optical fiber) ; Generalization ; Order (exchange) Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Advanced Materials Characterization Techniques
Authors: JINGLAI DUAN | Wentao Wang | Xiu Liang | Yongliang Zhang | Jie Liu | Yu Bai | Dangyuan Lei
Source: Advanced Functional Materials (article, 2026-01-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Three‐Dimensional Hierarchical Nanowire‐Networks with Deep‐Focus Tolerance and Adhesion Robustness for Harsh‐Environment SERS Sensing ABSTRACT With growing demand for field‐deployable and reliable Surface‐Enhanced Raman Scattering (SERS) applications, a critical yet often overlooked challenge is the stringent requirement for precise laser focusing, which severely limits operation outside controlled laboratory environments. To overcome this focal precision barrier, we present a three‐dimensional (3D) hierarchical Au nanowire‐network (NWNW) engineered as a robust SERS substrate. Its architecture is constructed to provide exceptional deep focus tolerance, exhibiting pronounced 3D optical activity along the z‐direction and angular anisotropy under polarized illumination. This enables the substrate to achieve an ultralow detection limit of 1×10 −12 M for non‐resonant 4‐mercaptopyridine molecules, while maintaining outstanding signal uniformity and repeatability (relative standard deviation, RSD &lt;10%). Moreover, an effective medium model reveals that the synergistic interplay between increased light scattering rates, elevated local density of optical states, and broadened eigenmodes jointly contributes to efficient light harvesting and near‐field enhancement throughout the 3D architecture. Equally importantly, the NWNW's mechanical reinforcement ensures adhesion robustness and consistent SERS performance even when subjected to harsh environments. By solving the focal precision challenge while maintaining robustness, this work demonstrates that the 3D Au NWNW is not only a highly sensitive SERS substrate but also a viable platform for transitioning SERS from controlled laboratory environments toward demanding real‐world applications. Robustness (evolution) ; Raman scattering ; Laser ; Repeatability ; Anisotropy ; Substrate (aquarium) ; Light scattering Gold and Silver Nanoparticles Synthesis and Applications ; Nanowire Synthesis and Applications ; Plasmonic and Surface Plasmon Research
Authors: Naranchimeg Zorigt | Leila Khazdooz | Florian Auras | J Inizan | Saeed Mirzaei | Ahmadreza Khosropour | Alireza Abbaspourrad
Source: ChemRxiv (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

WITHDRAWN We report 1,4-dihydropyridine covalent organic framework (DHP-COF) constructed through 1,4-dihydropyridine linkage via the Hantzsch pyridine synthesis. The synthesis is carried out under mild conditions and produces high yield of porous material. The 1,4-dihydropyridinelinked COF-3 (DHP-COF-3) has a high crystalline hexagonal structure with an ABC stacking pattern in three layers. The 1,4-dihydropyridine linkage introduces both hydrogen-bond acceptor sites and hydrophobic domains within the framework, which provide areas for electrostatic and hydrophobic interactions with per-and poly-fluoroalkyl substances (PFAS). DHP-COF-3 removed PFOS and PFOA from drinking water near quantitatively with a starting concentration of 2.4 μg L-¹, and it can be reused over several absorption cycles efficiently. Stacking ; Pyridine ; Yield (engineering) ; Linkage (software) ; Covalent bond ; Acceptor Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Nghia Le | Pere Miró
Source: ChemRxiv (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

Redox-State Driven Site Selectivity and Kinetic Gating in Polyoxovanadate Methylation Polyoxovanadate clusters offer atomically defined platforms for understanding how electronic structure controls surface reactivity in molecular metal oxides.We investigated the coupling between reduction state and alkylation reactivity in the hexavanadate cluster [V6O13(TRIOLX)2]2-(X = -CH3, -NO2) through a systematic analysis of successive methylation steps and their associated redox processes.We show that electron count governs the redistribution of 2-oxo nucleophilicity across the cluster surface, leading to pronounced redox-dependent regioselectivity in sequential functionalization.Microkinetic modeling reveals that thermodynamic accessibility alone does not dictate synthetic outcomes.Several partially methylated intermediates lie in kinetic shadow regions, where rapid downstream reactions prevent their accumulation under realistic conditions.These results explain why certain dimethylated species require stepwise strategies for isolation and define practical activation barrier thresholds for feasibility.Finally, we demonstrate that substituent effects on the TRIOL ligands can be quantitatively approximated using Hammett parameters, enabling rapid prediction of electronic trends without the need for additional calculations.Together, this work establishes redox-state control and kinetic gating as design principles for selective surface functionalization in polyoxovanadate clusters. Gating ; Selectivity ; Methylation ; Kinetic energy Polyoxometalates: Synthesis and Applications ; Vanadium and Halogenation Chemistry ; Chemical Synthesis and Reactions
Authors: Iqra Sarfraz | Sergei F. Vyboishchikov | Miquel Solà | Albert Artigas
Source: Journal of Materials Chemistry C (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

A simplified machine learning workflow for identifying potential singlet fission candidates: benzannulated biphenylenes as a case study Singlet fission (SF) can improve solar-cell efficiency. We present a machine-learning workflow combining AQME, ROBERT, and DFT calculations to screen 3835 benzannulated biphenylenes, identifying 505 promising candidates for SF. Workflow ; Singlet fission ; Limit (mathematics) ; Fission ; Singlet state ; Power (physics) Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Chemical and Physical Properties of Materials
Authors: Denis Andzheevich Sapegin | Joseph C. Bear | Jeremy K. Cockcroft | Edward J. Penny
Source: Polymer Chemistry (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Molecular insight into a disulfonimide-bearing diol: synthesis, characterisation and access to poly(disulfonimide)s A designed synthetic platform enables access to poly(1,2-disulfonimide)s. Practical observations, together with theoretical insights, pave the way for disulfonimide chemistry as a foundation for next-generation high-performance polymers. Foundation (evidence) ; Molecular conformation ; Key (lock) Synthesis and Catalytic Reactions ; Carbon dioxide utilization in catalysis ; Sulfur-Based Synthesis Techniques