jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chasheng He | Guihua Luo | Weike Su | Hongliang Duan | Yuanyuan Xie | Guijun Zhang | An Su
Source: Industrial & Engineering Chemistry Research (article, 2025-05-28)
Field: Chemistry — Inorganic Chemistry

Ruthenium Complexes for Asymmetric Hydrogenation and Selective Dehalogenation Revealed via Bayesian Optimization In this study, we report the discovery of ruthenium complexes that enable efficient asymmetric hydrogenation (AH) of ketones and selective dehalogenation. Using Bayesian optimization-assisted multiparameter screening, we identified ruthenium catalysts exhibiting distinct temperature-dependent behaviors. Below 129 °C, these catalysts achieve AH of aryl ketone halides with up to 99% conversion and >99% enantiomeric excess (ee), without hydrodehalogenation. Above this temperature, catalyst decomposition generates a ruthenium species that selectively promotes base-free dehalogenation using ethanol as a hydrogen donor, achieving yields up to 95%. Unlike conventional hydrodehalogenation catalyzed by metals such as Pd, Ni, or Rh, this method avoids the need for strong bases and hydrogen. These diverse catalytic activities were applied to synthesize the chiral fluoroiodobenzyl alcohol 2C, a key intermediate of Lorlatinib, in 60% yield with 94% ee, and to selectively dechlorinate an Ipragliflozin intermediate with a 64% yield. This work highlights the potential of ruthenium complexes for sustainable and selective catalysis. Ruthenium ; Halogenation ; Bayesian optimization ; Bayesian probability ; Asymmetric hydrogenation ; Chemistry ; Combinatorial chemistry ; Enantioselective synthesis ; Computer science ; Catalysis ; Organic chemistry ; Artificial intelligence Asymmetric Hydrogenation and Catalysis ; Carbon dioxide utilization in catalysis ; CO2 Reduction Techniques and Catalysts
Authors: Sandeep Kumar
Source: Shodh Sari-An International Multidisciplinary Journal (article, 2025-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Developing Effective Instructional Strategies for Teaching Organic Chemistry Reaction Mechanisms Organic chemistry is often perceived as one of the most challenging subjects in undergraduate science curricula, largely due to the complexity of reaction mechanisms. This paper explores instructional strategies designed to enhance students’ comprehension and retention of reaction mechanisms. By integrating active learning techniques, technology-enhanced tools, and cognitive science principles, the paper outlines evidence-based methods that promote deeper understanding and long-term retention of organic chemistry concepts. Recommendations for curriculum design and teaching practices are provided, supported by empirical studies and classroom implementations. Chemistry ; Computer science ; Biochemical engineering ; Mathematics education ; Psychology ; Engineering Various Chemistry Research Topics
Authors: Ned Thaddeus Taylor | Joe Pitfield | Francis Huw Davies | Steven Paul Hepplestone
Source: npj Computational Materials (article, 2025-08-08)
Field: Materials Science — Materials Chemistry

RAFFLE: active learning accelerated interface structure prediction Abstract Interfaces between materials are critical to the performance of many devices, yet predicting their structure is computationally demanding due to the vast configuration space. We introduce RAFFLE, a software package for efficiently exploring low-energy interface configurations between arbitrary crystal pairs, enabling the generation of ensembles of interface structures. RAFFLE leverages physical insights and genetic algorithms to intelligently sample configurations, using dynamically evolving 2-, 3-, and 4-body distribution functions as generalised structural descriptors. These descriptors are refined through active learning to guide atom placement strategies. RAFFLE performs well across diverse systems, including bulk materials, intercalation compounds, and interfaces. It correctly recovers known bulk phases of aluminum and MoS 2 , and predicts stable phases in intercalation and grain-boundary systems. For Si∣Ge interfaces, it finds intermixed and abrupt structures to be similarly stable. By accelerating interface structure prediction, RAFFLE offers a powerful tool for materials discovery, enabling efficient exploration of complex configuration spaces. Interface (matter) ; Computer science ; Software ; Boundary (topology) ; Materials science ; Computational science ; Mathematics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: V Priyadarshini | Pengfei Zou | Debashish Dash | Shrabani Mahata | Manvendra Singh Chauhan | Kumar Akash
Source: Results in Engineering (article, 2025-08-06)
Field: Materials Science — Materials Chemistry

Advanced active learning techniques for precision-driven machine-learned potentials in Sb2S3 • A machine-learned interatomic potential for amorphous and crystalline Sb₂S₃ was developed using active learning and DFT reference data. • The trained moment tensor potential accurately reproduces DFT-level energies, forces, and stresses with high R² values, demonstrating excellent predictive capability. • Structural features such as pair distribution functions and bond environments from MTP-generated configurations closely match experimental and ab initio references. • Ab initio molecular dynamics simulations were employed to benchmark thermal and dynamic behavior, guiding accurate MTP fitting. The model effectively captures the medium-range order and coordination in disordered Sb₂S₃, making it suitable for simulating amorphous phases at low computational cost. • This work enables large-scale simulations of Sb₂S₃ for electronic and thermomechanical applications, bridging accuracy and efficiency through ML potentials. We present a powerful and efficient strategy for modeling amorphous antimony trisulfide (Sb₂S₃) using a Machine-Learned Interatomic Potential (MLIP) developed via an active learning approach based on the Moment Tensor Potential (MTP) framework. Beginning with bulk geometries of the orthorhombic, triclinic, and monoclinic phases of Sb₂S₃, we extracted essential elemental and structural features to construct a robust training dataset. The active learning loop systematically identified high-uncertainty configurations, which were refined through Density Functional Theory (DFT) calculations to interact with improving model accuracy. This iterative, data-efficient learning process not only significantly reduced computational expense but also yielded remarkable accuracy in predicting energies, atomic forces, and stress tensors. Our findings reveal a striking concordance between MTP predictions and DFT results, affirming the potential's reliability. Furthermore, the resulting MTP model is benchmarked against experimental and DFT/PBE (density functional theory/Perdew−Burke−Ernzerhof) level using ab initio molecular dynamics (AIMD) in terms of structural metrics such as radial and angular distribution functions. Our results show that the MTP model reliably captures the short- and medium-range ordering in amorphous Sb₂S₃ and can serve as an efficient surrogate for DFT in large-scale atomistic simulations of disordered materials. By advancing the ML training of interatomic potentials tailored to complex, disordered systems like amorphous Sb₂S₃, this study lays the groundwork for a scalable and generalizable approach to simulating next-generation amorphous materials with high precision and efficiency. Computer science ; Artificial intelligence ; Machine learning ; Active learning (machine learning) Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Electronic and Structural Properties of Oxides
Authors: Javier Emilio Alfonso Ramos | Carlo Adamo | Éric Brémond | Thijs Stuyver
Source: Journal of Chemical Theory and Computation (article, 2025-09-11)
Field: Chemistry — Organic Chemistry

CYCLO70: A New Challenging Pericyclic Benchmarking Set for Kinetics and Thermochemistry Evaluation Here, a new challenging benchmarking data set for cycloaddition reactions, CYCLO70, is presented and analyzed. CYCLO70 has been generated with the specific aim of being representative of the most challenging regions of the chemical reaction space surrounding Diels-Alder, dipolar cycloadditions, and (sigmatropic) rearrangement reactions with the help of an active learning approach. Testing 93 different functionals, spanning from spin-local density approximation to the most recent double-hybrid functionals, we observe that the errors on CYCLO70 are significantly bigger than those on the cycloaddition subset of BH9, the most popular benchmarking data set for this reaction class. Furthermore, we observe that the range-separated hybrid ωB97M-V is the best performing functional to model barrier heights and reaction energies, with a deviation closest to the desirable "chemical accuracy"; among the double hybrids, PBE-QIDH performs best, and among the fixed-range hybrids, M06-2X and r<sup>2</sup>SCAN0 emerge as the most balanced in terms of simultaneously reproducing both properties. Next, we perform a principal component analysis on the errors across the data set and demonstrate not only that the errors across different functional approximations correlate to a significant extent (the first two components explain 98% of the variance), but we also observe that functionals belonging to the same rung of Jacob's ladder cluster together in the constructed two-dimensional plot. These results were further validated on a set of Diels-Alder reactions relevant to self-healing polymer design, reinforcing the practical relevance of CYCLO70. Benchmarking ; Set (abstract data type) ; Thermochemistry ; Pericyclic reaction ; Relevance (law) ; Data set ; Principal component analysis ; Cycloaddition Chemical Thermodynamics and Molecular Structure ; Crystallography and molecular interactions ; Thermal and Kinetic Analysis
Authors: Theta Murty | Apu Sarkar | R.N. Singh
Source: Journal of Nuclear Materials (article, 2025-06-04)
Field: Materials Science — Materials Chemistry

Novel use of three-point bend test for determining the threshold stress for hydride reorientation in Zr-2.5%Nb pressure tube material Tube (container) ; Hydride ; Materials science ; Stress (linguistics) ; Composite material ; Point (geometry) ; Metallurgy ; Structural engineering ; Engineering ; Geometry ; Mathematics Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Fusion materials and technologies
Authors: Nikolaj A. Zhestkij | Svyatoslav A. Povarov | L. Yu. Volodin | R. I. Chelmodeev | Mikhail Melkomukov | Yuliya Kenzhebayeva | Sergey S. Rzhevskiy | Sergei A. Shipilovskikh | Anastasia Lubimova | Maria Timofeeva | Valentin A. Milichko
Source: Materials Chemistry Frontiers (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

One-step flashlight processing of MOF thin films for non-linear light absorption A one-step and large-scale patterning of different metal-organic framework thin films using a flash xenon lamp in real time with cm 2 s −1 speed is demonstrated. Materials science ; Absorption (acoustics) ; Thin film ; Optics ; Optoelectronics ; Nanotechnology ; Composite material ; Physics ZnO doping and properties ; Metal-Organic Frameworks: Synthesis and Applications ; Transition Metal Oxide Nanomaterials
Authors: Taylor Bader | Kyle Boone | Chris Johnson | Cindy L. Berrie | Candan Tamerler
Source: Biomimetics (article, 2025-02-12)
Field: Materials Science — Biomaterials

Probing Solid-Binding Peptide Self-Assembly Kinetics Using a Frequency Response Cooperativity Model Biomolecular adsorption has great significance in medical, environmental, and technological processes. Understanding adsorption equilibrium and binding kinetics is essential for advanced process implementation. This requires identifying intrinsic determinants that predict optimal adsorption properties at bio-hybrid interfaces. Solid-binding peptides (SBPs) have targetable intrinsic properties involving peptide-peptide and peptide-solid interactions, which result in high-affinity material-selective binding. Atomic force microscopy investigations confirmed this complex interplay of multi-step peptide assemblies in a cooperative modus. Yet, most studies report adsorption properties of SBPs using non-cooperative or single-step adsorption models. Using non-cooperative kinetic models for predicting cooperative self-assembly behavior creates an oversimplified view of peptide adsorption, restricting implementing SBPs beyond their current use. To address these limitations and provide insight into surface-level events during self-assembly, a novel method, the Frequency Response Cooperativity model, was developed. This model iteratively fits adsorption data through spectral analysis of several time-dependent kinetic parameters. The model, applied to a widely used gold-binding peptide data obtained using a quartz crystal microbalance with dissipation, verified multi-step assembly. Peak deconvolution of spectral plots revealed distinct differences in the size and distribution of the kinetic rates present during adsorption across the concentrations. This approach provides new fundamental insights into the intricate dynamics of self-assembly of biomolecules on surfaces. Cooperativity ; Kinetics ; Peptide ; Chemistry ; Thermodynamics ; Materials science ; Biophysics ; Crystallography ; Physics ; Biology ; Biochemistry ; Classical mechanics Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis ; Advanced biosensing and bioanalysis techniques
Authors: Jian Zhang | Francesco Aiello | Mauro Salazar | Diletta Giuntini
Source: Acta Materialia (article, 2025-06-05)
Field: Materials Science — Ceramics and Composites

Double-tough ceramics: Optimization-supported multiscale computational design To overcome the brittleness limitation of ceramics, various toughening mechanisms have been proposed. Some of the most remarkable, especially for oxides, include the tetragonal-to-monoclinic phase transformation leading to crack shielding in zirconia, and bioinspired brick-and-mortar microstructures fostering crack deflection. It has, however, proven challenging to incorporate both these mechanisms into a single all-ceramic material. In this work, we propose a computational methodology for the design of a material that combines these two toughening strategies, using a multiscale modeling approach that captures both their individual contributions and the overall fracture performance. This is achieved by developing an all-ceramic composite with a brick-and-mortar microstructure, in which the nanocrystalline mortar is transformation-toughened. Key factors influencing phase transformation, such as grain boundary properties, grain orientations, and kinetic coefficients, are analyzed, and the resulting transformation stress–strain behavior is incorporated into the microscale mortar constitutive model. We demonstrate that the synergistic effect of the two toughening mechanisms is achievable, and that it is an extremely effective strategy to boost fracture performance. The influence of brick size, mortar thickness, and properties of the constituent materials is then systematically investigated. Finally, a gradient-free optimization algorithm is employed to identify optimal geometric and material parameters, revealing that longer, thinner bricks with minimal mortar thickness provide the best fracture resistance. Optimal combinations of material properties are identified for given brick sizes and mortar thicknesses. • The design of a ‘double-tough’ all-ceramic material is proposed, consisting of a brick-and-mortar microstructure with a nanocrystalline, transformation-toughened mortar. • A computational multiscale modeling approach is developed to capture the overall structural performance and the effects of both toughening mechanisms. • Optimization algorithms are employed to fine-tune brick sizes, mortar thicknesses, and material properties, maximizing fracture resistance. Materials science ; Ceramic ; Multiscale modeling ; Composite material ; Mechanical engineering ; Nanotechnology ; Engineering Advanced ceramic materials synthesis ; Advanced materials and composites ; Ferroelectric and Piezoelectric Materials
Authors: Albert F. B. Bittencourt | Pedro Ivo R. Moraes | Juarez L. F. Da Silva
Source: ACS Omega (article, 2025-01-22)
Field: Materials Science — Materials Chemistry

Mechanistic Insights into the Direct Partial Oxidation of Methane to Methanol Catalyzed by Single-Atom Transition Metals on Hydroxyapatite The direct conversion of methane to methanol offers a promising approach to utilize abundant natural gas resources; however, the finding of suitable low-cost catalysts remains challenging due to the chemical inertness of methane. In this study, we performed a theoretical investigation of the role of transition-metal single-atom catalysts (TM-SACs) anchored on the hydroxyapatite support, where TM = Fe, Co, Ni, and Cu. We examined adsorption properties, formation of oxidized active sites, methane activation, methanol formation, and its stability using density functional theory calculations with van der Waals corrections, combined with the climbing image nudged elastic band method for the localization of transition states. Our findings reveal that Cu/HAP exhibits the most favorable energy profile for the conversion of methane to methanol, offering lower activation barriers and a more exothermic reaction pathway compared to other systems. In contrast, Fe/HAP shows superior oxygen dissociation capabilities but faces challenges in methanol production due to higher reaction barriers. These findings provide valuable information for the future design of TM/HAP catalysts for sustainable methane utilization. Catalysis ; Methane ; Transition metal ; Methanol ; Partial oxidation ; Atom (system on chip) ; Anaerobic oxidation of methane ; Chemistry ; Materials science ; Chemical engineering ; Inorganic chemistry ; Organic chemistry ; Computer science Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysts for Methane Reforming
Authors: Ling Meng | Samad Razzaq | Diwakar Singh | Francesc Viñes | Francesc Illas | Kai S. Exner
Source: npj 2D Materials and Applications (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

Nitrogen embedding enhances stability and activity of single-atom motifs of MXenes under anodic polarization Abstract MXenes undergo water-mediated surface reconstruction under anodic polarization and spontaneously form single-atom centers, reminiscent of single-atom catalysts (SAC), which exhibit great potential for the oxygen evolution reaction (OER). Yet, the as-formed SAC-type MXene motifs still suffer from the conventional activity-stability trade-off under OER conditions. Here, we propose a nitrogen embedding strategy to modulate the local coordination environment of the single-atom centers, thereby enhancing both their stability under anodic polarization and OER activity. Based on density functional theory calculations, we systematically evaluate 53 N-doped SAC-type MXene motifs, covering M 2 X 1 O, M 3 X 2 O, and M 4 X 3 O MXenes, using a three-step screening approach, which identifies promising candidates that (i) allow nitrogen doping under anodic polarization, (ii) are thermodynamically stable under OER conditions, and (iii) exhibit high OER activity. We demonstrate that N-doped Ta 2 C 1 O and Ta 2 N 1 O SAC-like MXenes break the traditional activity-stability trade-off in OER and provide theoretical guidance for designing efficient and durable SAC-like MXene electrocatalysts. MXenes ; Oxygen evolution ; Anode ; Polarization (electrochemistry) ; Nitrogen ; Doping MXene and MAX Phase Materials ; Electrocatalysts for Energy Conversion ; Ammonia Synthesis and Nitrogen Reduction
Authors: Vikas Tiwari | Tarak Karmakar
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Modeling catalytic reaction on ligand-protected metal nanoclusters Monolayer protected metal nanoclusters (MPC) show great potential for catalysis, highlighting the need for unraveling their mechanistic intricacies to enhance catalyst performance. However, due to their inherent complexity, an in-depth understanding of how nanoclusters facilitate chemical transformation has remained elusive. In this work, we employed a combination of classical molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) MD simulations, augmented with the on-the-fly probability-based enhanced sampling (OPES) method, to elucidate substrate binding to the MPC and a chemical reaction on the MPC surface. We investigated the oxidation of amino alcohol to oxazolidine, catalyzed by a peptide ligand functionalized Au<sub>25</sub> gold nanocluster, as a prototypical example. Classical MD simulations unveiled the crucial role of solvent and peptide-based ligands in substrate binding, while QM/MM simulations elucidated the mechanistic pathway and provided insight into the free energy landscape of the chemical reaction. This work demonstrates a computational workflow that can be applied to study similar MPC-catalyzed chemical reactions. Nanoclusters ; Ligand (biochemistry) ; Catalysis ; Metal ; Combinatorial chemistry ; Chemistry ; Materials science ; Nanotechnology ; Organic chemistry ; Receptor Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Catalytic Processes in Materials Science
Authors: Hossein Taouba | Jean-Luc Hayen | Jean‐François Liégeois
Source: Journal of Chemical Education (article, 2025-01-09)
Field: Materials Science — Materials Chemistry

Solubility and Extractability in the Pharmaceutical Sciences: A Practical Exercise with Pure Compounds The extractability of pure organic compounds is a ubiquitous concept in the pharmaceutical field. During this laboratory session, each student receives one vial containing an unknown compound. For this sample, they will determine its solubility in an organic solvent, in water, and then its extractability profile by three liquid–liquid extractions at different pH values followed by the evaporation of the organic phase and the quantification of the residue by gravimetric analysis. The data obtained will allow students to draw a curve representing the acid/base and the hydro- and lipophilic character of their given compound. Once the first part of the session is finished, the students who have tested the same compound share the results obtained. These pooled results will show that the sigmoidal-like curve of the lipophilic acid and its conjugate base or the lipophilic base and its conjugate acid have the same shape, although the solubility in organic solvent and water is different. Lipophilic amphoteric and neutral compounds are also tested in the same laboratory session. This exercise with pure compounds will allow students to identify the physicochemical characteristics of organic compounds and anticipate the separation of such compounds in a mixture. Solubility ; Pharmaceutical sciences ; Chemistry ; Chemical engineering ; Organic chemistry ; Medicine ; Pharmacology ; Engineering Crystallization and Solubility Studies ; Innovative Microfluidic and Catalytic Techniques Innovation ; Various Chemistry Research Topics