jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nicole Mancini | Fabian Berger | Marcin Rybicki | Kaido Sillar | Joachim Sauer
Source: Journal of the American Chemical Society (article, 2026-03-16)
Field: Chemistry — Inorganic Chemistry

Atomistic Model for Water Adsorption in Mg-MOF-74: Quantum Chemical Prediction of Structures and Isotherms The design of improved metal-organic frameworks (MOFs) for water harvesting requires the reliable prediction of adsorption isotherms, i.e., Gibbs free energies of adsorption, with no other input than the atomic positions. We employ density functional theory (DFT) and show that, in Mg-MOF-74, well-defined adsorption structures exist for water loadings of <i>n</i> = 1, 2, 3, 4, and 5 molecules per Mg<sup>2+</sup> ion. The first water molecule attaches to the open metal site, while on adsorption of subsequent molecules, structures with an increasing number of hydrogen bonds per molecule form: dimers (<i>n</i> = 2), chains in pore direction (<i>n</i> = 3), and a monolayer on the pore wall (<i>n</i> = 4). For <i>n</i> = 5, a tube-like stack of water trimers connected to the monolayer fills the pore completely, and all water molecules are 4-fold coordinated. For isotherm predictions, we use a <i>Multisite Langmuir</i> model with Gibbs free energies of -33, -19, -13, -10, and -21 kJ/mol for the steps leading to adsorption states with <i>n</i> = 1, 2, 3, 4, and 5 molecules, respectively. The close agreement of the predicted total isotherm with experiment corresponds to an accuracy of ±2 kJ/mol for Gibbs free energies. This is achieved only after adding "high-level" Coupled Cluster corrections (0, 3, 9, 8, and 11 kJ/mol for <i>n</i> = 1, 2, 3, 4, and 5, respectively) to the DFT energies. We show that the large variations observed between different experimental isotherms can be explained by sample imperfections or incomplete evacuation of samples before isotherm measurements. Monolayer ; Adsorption ; Molecule ; Langmuir adsorption model ; Density functional theory ; Gibbs free energy Metal-Organic Frameworks: Synthesis and Applications ; Mesoporous Materials and Catalysis ; Covalent Organic Framework Applications
Authors: Alessandro Brusa | Simone Zanella | Armando Carlone
Source: ChemRxiv (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

Synthonpedia -A free reaction database as a tool for retrosynthetic analysis Authors Retrosynthetic analysis represents a fundamental component of synthetic design, providing a logically consistent framework for the deconstruction of molecules of arbitrary complexity. Nevertheless, in practical applications, chemists may be influenced by cognitive biases toward well-established or familiar transformations, which can affect disconnection choices. In recent years, the rapid development of AI-driven retrosynthetic tools has sought to address these limitations by automating synthetic planning through the prediction of reaction outcomes, intermediates, and starting materials. Despite substantial advances, such approaches may still reflect biases originating from incomplete, uneven, or historically skewed literature data. Here, we introduce a new retrosynthetic tool designed to collaborate with chemists in synthesis planning. The concept is to build a freely accessible, public database of reported reactions and methodologies encoded as generalized synthon structures. Upon disconnection of a target molecule, the resulting fragments are matched against the database, and the corresponding reactions associated with the specific disconnection are retrieved. This strategy enables a systematic exploration of the reaction space covered by the database, including transformations that are infrequently reported yet may offer unconventional or insightful solutions in synthetic design. Retrosynthetic analysis ; Disconnection ; Code refactoring ; Component (thermodynamics) ; Deconstruction (building) Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Catalytic C–H Functionalization Methods
Authors: Noah D'Amico | Sandeep Puri | Ian Jones (214954) | Andrew Gillespie | Cuikun Lin | Bo Zhao | R. V. Duncan
Source: ArXiv.org (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Production of Low-Density Aerogel Nuclear Fuels for Use in Fission Fragment Rockets and Novel Reactor Design Graphene hydrogels were created and loaded with uranyl nitrate or thorium nitrate and freeze-dried to produce graphene aerogel nuclear fuels. These aerogels had densities between 0.018-0.035 g/cm3 and consisted of ~7.3 +- 0.5% uranium/thorium by mass. The ultra-low density of the aerogels allows for high energy ions to escape the fuel particles without depositing all their energy as heat, as is typical in nuclear fuels. Their measured alpha activity was ~16 pCi/mg, which could be enhanced up to ~49 pCi/mg by decreasing the thickness of aerogel samples to allow all alpha particles to escape. Additionally, high energy neutrons were used to induce fission to provide a source of fission fragments from the aerogel fuels. This novel form of nuclear fuel has potential applications in space propulsion such as fission fragment rocket engines, as well as in terrestrial applications for modular reactors, direct conversion methods, and in medical radiotherapeutics. Aerogel ; Fission ; Fission products ; Nuclear fuel ; Neutron ; Nuclear fission ; Uranium ; Alpha particle ; Fissile material Graphite, nuclear technology, radiation studies ; Covalent Organic Framework Applications ; Nuclear Materials and Properties
Authors: Jesper Byggmästar | Tiago Lopes | Zheyong Fan | Tapio Ala-Nissila
Source: ArXiv.org (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

Nine-element machine-learned interatomic potentials for multiphase refractory alloys New refractory alloys are being continuously designed and characterised for applications requiring good high-temperature mechanical properties and stability. Computational design from atomistic simulations is limited by interatomic potentials missing key elements, being too inaccurate, or computationally too slow for large-scale simulations. Here we present development of a refractory alloy database and two computationally efficient and general-purpose machine-learned potentials (tabGAP and NEP). We also design a cross-sampling strategy for effective sampling of training data using predictions from two potentials with completely different underlying architecture. The potentials support arbitrary alloy compositions of elements in groups four to six in the periodic table (Ti, Zr, Hf, V, Nb, Ta, Cr, Mo, W). The database is diverse yet multitargeted to enable simulations of refractory metals and alloys across different pure-metal, solid-solution, intermetallic, and glassy phases. We demonstrate the usefulness of the potentials by reproducing known pressure-, temperature-, and solute-induced phase transitions, grain boundary segregation, and simulations of radiation damage in the WTaCrVHf metallic glass. Interatomic potential ; Grain boundary ; Refractory metals ; Refractory (planetary science) ; Alloy ; Phase (matter) ; Boundary (topology) ; Periodic boundary conditions Machine Learning in Materials Science ; Metallic Glasses and Amorphous Alloys ; Rare-earth and actinide compounds
Authors: Hui Liu | Teresa Olszewska | Hao Liu
Source: Polymers (article, 2026-02-03)
Field: Materials Science — Polymers and Plastics

Curcumin-Based Tri-Allyl Derivative for Bismaleimide Resins: Synergistic Modulation of Thermal Stability and Fire Safety Bio-based bismaleimide (BMI) resins can reduce environmental impact and impart intrinsic flame retardancy, but achieving a high glass transition temperature (<i>T</i>g) remains challenging. Here, we replace the conventional petrochemical co-monomer O,O'-diallyl bisphenol A (DABPA) with a synthesized tri-allyl derivative of curcumin (AEC) in 4,4'-bismaleimidodiphenylmethane (BDM)-based resins. The AEC monomer, synthesized via exhaustive O- and C-alkylation of curcumin, acts as a trifunctional crosslinker. By systematically varying the imide:allyl molar ratio, we optimized the network properties. We optimize the network's thermal and fire-safety properties. The optimized formulation (BDM: AEC = 1:0.87, denoted BA-0.87) yields 43.06% char at 800 °C and reduces the peak heat release rate (PHRR) by 13.2% compared to the conventional BDM/DABPA control (BD-0.87). Meanwhile, BA-0.87 passes UL-94 V-0 with no dripping and attains a <i>T</i>g above 400 °C-nearly 100 °C higher than BD-0.87. These enhancements arise from curcumin's rigid conjugated structure, which increases crosslink density and promotes char formation during decomposition. Our work demonstrates a viable, bio-derived pathway to engineer BMI resins that simultaneously improve thermal stability and intrinsic flame retardancy. Such resins are promising for demanding aerospace and high-temperature electronic applications that require both fire safety and stability. Thermal stability ; Char ; Bisphenol A ; Derivative (finance) ; Work (physics) ; Thermal ; Polymer ; Glass transition ; Fire safety Flame retardant materials and properties ; Polymer composites and self-healing ; Synthesis and properties of polymers
Authors: Jianshuo Cheng | Mouwei Liu | Liangliang Zhu
Source: Chemical Society Reviews (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Photo-responsive chemical systems enabling multiscale structural ordering Beyond elemental composition and molecular structure, ordered multiscale architectures (self-assembled structure, crystal structure, phase structure, <i>etc.</i>) play a comparably important role in determining material functions, especially the optoelectronic properties. Light, a unique external stimulus with superior spatiotemporal resolution and non-invasive nature, provides unparalleled capabilities for dynamically constructing and manipulating such ordered structures. Since previous reviews on light-responsive materials have generally overlooked how light can govern multiscale structural ordering, this review comprehensively surveys the rapidly advancing field of photo-responsive chemical systems designed to achieve and control multiscale structural ordering. We begin by elucidating the fundamental photochemical and photophysical mechanisms at the molecular level, which serve as the primary triggers for molecular structural, configurational, or conformational transformations. Based on these events that initiate molecular perturbation, the core of the review critically examines several key light-mediated strategies for constructing and manipulating ordered hierarchical architectures, notably photo-triggered polymerization-induced self-assembly, liquid crystal-assisted phase structural amplification, photo-induced self-assembly mediated by host-guest interaction, and the emerging paradigm of photoexcitation-induced assembly (PEIA). We discuss how these strategies successfully translate molecular or nanoscale events into well-defined, macroscopic ordered states in real time and <i>in situ</i>. Finally, we provide a forward-looking perspective on the existing challenges and promising future research directions. Multiscale modeling ; Key (lock) ; Phase (matter) ; Perspective (graphical) ; Field (mathematics) ; Nanoscopic scale Block Copolymer Self-Assembly ; Luminescence and Fluorescent Materials ; Advanced Polymer Synthesis and Characterization
Authors: M. P. Goda | Mohamed Raafat Ibrahiem | Mohamed Saad Aly | Ramy Sadek | Hesham Tantawy
Source: Discover Applied Sciences (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

Innovative image processing evaluation of infrared obscuration efficiency in red phosphorus-based compositions under varying loading pressures Airborne obscurants based on red phosphorus (RP), used as smoke-producing agent, are effective infrared obscuration countermeasures for military operations. This study investigates the significant influence of various loading pressures (P = 4, 4.5, and 5 tons) and target (black-body radiator) temperatures (T = 50, 100, 150, and 200 °C) on the infrared obscuration efficiency of selected RP-based smoke composition using a novel evaluation approach based on image processing techniques. The selected composition consists of 61% red phosphorus, 4% lead oxide, 25% magnesium, and 10% epoxy resin by weight. A thermal camera operating in the far-infrared region (8–12 µm) and advanced image processing algorithms were used to measure the infrared obscuration efficiency of the tested compositions. The algorithms implemented using Python and the CV2, OS, and Matplotlib libraries, enabled pixel-based intensity analysis to detect variations in infrared images over time and precisely assess the smoke screen’s effectiveness in obscuring the target’s image and temperature. Among the tested samples, P3 (5 tons) achieved the best thermal performance of 110 °C after 49 s. It provided the highest obscuration efficiency of 95%, with a minimum formation time of 4 s and a successful duration of 39 s, compared to P1 (4 tons) and P2 (4.5 tons). Furthermore, T1 (50 °C) demonstrated superior obscuration efficiency over T2 (100 °C), T3 (150 °C), and T4 (200 °C), validating the effectiveness of image processing techniques as an innovative evaluation tool. Image processing ; Infrared ; Signal processing ; Data processing ; Digital image processing Luminescence Properties of Advanced Materials ; Glass properties and applications ; 2D Materials and Applications
Authors: Rama Juha | Ibrahim Alghoraibi
Source: Scientific Reports (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

Stable nitrogen-doped carbon quantum dots with pH-controlled fluorescence response for Fe3+ detection Nitrogen-doped carbon quantum dots (NCQDs) were synthesized via a facile and optimized hydrothermal method. The as-prepared CQDs were comprehensively characterized using AFM, HRTEM, DLS, ζ-potential, EDX, XRD, Raman, FTIR, UV-Vis, and fluorescence spectroscopy, confirming their nanoscale dimensions, graphitic structure, abundant nitrogen- and oxygen-containing surface functionalities, and excellent aqueous dispersibility. The CQDs exhibited strong blue fluorescence with excitation-independent emission and a relatively high quantum yield of 37.8%. Their fluorescence stability was systematically evaluated over a wide range of pH values, ionic strengths, and solvent environments, demonstrating robust optical performance. Importantly, the fluorescence sensing behavior toward Fe<sup>3+</sup> ions was critically examined under different pH conditions, revealing that Fe<sup>3+</sup> detection in alkaline media is severely hindered by Fe(OH)<sub>3</sub> precipitation, which leads to misleading quenching effects. By conducting sensing experiments under strongly acidic conditions (pH = 2), where Fe<sup>3+</sup> remains fully soluble, a clear and reliable fluorescence quenching response was achieved over a wide linear range (20-1000 µM), with a detection limit of 10.25 µM. In addition, the practical applicability of the CQDs was demonstrated through fluorescent ink and CQDs/PVA composite films. Fluorescence ; Quantum yield ; Quenching (fluorescence) ; Detection limit ; Carbon quantum dots ; Quantum dot ; Aqueous solution ; Ion Carbon and Quantum Dots Applications ; Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications
Authors: Xin Jin | Yutao Li | Kelian Dietmar Marc Gaedecke | Xiaoqi Zhang | Berend Smit
Source: Chemical Science (article, 2026-01-01)
Field: Chemistry — Inorganic Chemistry

Machine learning potential for modelling dynamic hydrogen bond networks in MOF MIL-120 Metal–organic frameworks (MOFs) are porous materials with the potential for gas adsorption. This work presents a generalizable framework for simulating adsorption in flexible MOFs using machine learning potentials. Adsorption ; Hydrogen bond ; Hydrogen ; Porous medium ; Porosity Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Machine Learning in Materials Science
Authors: Bohan Wang | Dengke Zhao | Shuo Zhao | Binbin Fan | Fei Li | Yiyao Ge | Jie Zhang | Yi Yang | Kexin Chen | Guanghua Liu
Source: Journal of the American Ceramic Society (article, 2026-02-01)
Field: Materials Science — Ceramics and Composites

Effects of Processing Parameters and Reaction Mechanism in Combustion Synthesis of HfC <i> <sub>x</sub> </i> N <i> <sub>y</sub> </i> Powders ABSTRACT HfC x N y solid solution powders were synthesized by combustion synthesis using Hf and carbon black as reactants, with HfC or HfN as diluents under a nitrogen atmosphere. The effects of processing parameters on phase compositions and microstructures were systematically examined, and the reaction mechanism in the combustion synthesis of HfC x N y was elucidated. The results demonstrated that both phase compositions and microstructures were strongly dependent on the processing parameters. For undiluted samples with high Hf/C molar ratio and diluted samples with low HfC content, the microstructure featured HfC x N y crystals embedded in the unreacted molten Hf. At moderate HfC contents, HfO 2 formed and replaced molten Hf as the bonding phase. At high HfC contents, the microstructure evolved into agglomerates composed of HfC x N y crystals. In contrast, the introduction of HfN as a diluent led to more complex phase compositions. Based on thermodynamic calculations and experimental characterization, four reaction pathways for the formation of the HfC x N y solid solution have been proposed. In path 1, HfC x N y crystals are directly precipitated from Hf‐C‐N melt. In path 2, HfC crystals precipitate from Hf‐C melt, followed by the formation of HfC x N y phase through the reaction between HfC and N 2 . In path 3, HfC x N y phase forms through a solid‐state reaction between HfC and HfN. In path 4, HfC x N y phase forms via a solid‐state reaction between HfN and C. This study provides mechanistic insight into HfC x N y formation in combustion synthesis and offers guidance for tailoring high‐quality powder. Microstructure ; Diluent ; Combustion ; Agglomerate ; Phase (matter) ; Molar ratio ; Reaction mechanism Advanced ceramic materials synthesis ; Semiconductor materials and devices ; MXene and MAX Phase Materials
Authors: Osheen Sharma | Jitendra Pal Singh | Jaiparkash
Source: ChemistrySelect (article, 2026-01-30)
Field: Materials Science — Materials Chemistry

Influence of Sintering Duration on Structural, Optical and Magnetic Behavior of Cobalt Ferrite ABSTRACT This work investigates the impact of sintering duration on the structural, optical and magnetic behavior of cobalt ferrite materials. These materials were synthesized using the solid‐state reaction method by sintering the mixture of cobalt oxide and ferric oxide at 1000°C for 12, 20, 30, and 50 h. X‐ray diffraction analys is confirmed the formation of a single‐phase spinel structure with the space group . Rietveld refinement showed that behavior of cation distribution among the tetrahedral and the octahedral sites in all materials is similar to that of almost inverse spinel phase. Crystallite size decreased from 53 to 40 nm as sintering time increases from 12 to 50 h. Sintering duration leads to the change in porosity of these materials. Fourier transform infrared spectroscopy confirmed the presence of metal‐oxygen bonding in all the materials. Energy dispersive spectroscopic analysis confirms the chemical homogeneity in all the synthesized materials. The energy band gap decreased slightly on increasing sintering duration. The hysteresis curves show the ferrimagnetic nature of all synthesized materials. The magnetization values of all sintered samples show a decreasing trend with sintering duration. The calculated magnetic moment was found in well correlation with the complete inverse spinel structure of these materials. Sintering ; Spinel ; Crystallite ; Ferrimagnetism ; Ferrite (magnet) ; Rietveld refinement ; Magnetization ; Cobalt ; Analytical Chemistry (journal) Magnetic Properties and Synthesis of Ferrites ; Pigment Synthesis and Properties ; Electromagnetic wave absorption materials
Authors: Suleiman Ibrahim Mohammad | Hamza Abu Owida | Asokan Vasudevan | Suhas Ballal | Shaker Al-Hasnaawei | Subhashree Ray | Naveen Chandra Talniya | Aashna Sinha | Vatsal Jain | Ahmad Abumalek
Source: Scientific Reports (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

Accurate prediction of density, viscosity, and speed of sound in aqueous aliphatic biogenic polyamine solutions using data-driven modeling This study presents a data-driven framework for predicting key thermophysical properties including dynamic viscosity, speed of sound, and density of aqueous solutions containing aliphatic biogenic polyamines (ABPs), which are of growing importance in biochemical and industrial applications. A total of 198 experimentally measured data points were used to train and validate five advanced machine learning models: K-Nearest Neighbors (KNN), Ensemble Learning (EL), Convolutional Neural Networks (CNN), Adaptive Boosting (AdaBoost), and Multi-Layer Perceptron Artificial Neural Networks (MLP-ANN). Hyperparameter optimization was conducted using the Coupled Simulated Annealing (CSA) algorithm. The study focused on three ABPs including Putrescine dihydrochloride (PUT), Cadaverine dihydrochloride (CAD), and Spermine tetrahydrochloride (SPER) across a range of molar masses, concentrations, and temperatures. Sensitivity analysis using the Monte Carlo method revealed ABP type and molar mass as dominant factors influencing the target properties. Among the models, MLP-ANN demonstrated the highest predictive accuracy for density and viscosity, while EL performed best for speed of sound. The results confirm the potential of machine learning as a reliable, efficient, and cost-effective alternative to conventional experimental approaches for modeling complex solution behavior. Cadaverine ; Hyperparameter ; Convolutional neural network ; Aqueous solution ; Artificial neural network ; Simulated annealing ; Multilayer perceptron ; Speed of sound Machine Learning in Materials Science ; Polyamine Metabolism and Applications ; Polymer composites and self-healing
Authors: Lion Frangoulis | Lorenzo A. Mariano. Vu Ha Anh Nguyen | Zahra Khatibi | Alessandro Lunghi
Source: arXiv (Cornell University) (article, 2026-04-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Maximizing the magnetic anisotropy of Dy complexes by fine tuning organic ligands: A systematic multireference high-throughput exploration of over 30k molecules The design of the coordination environment of magnetic ions is key to achieving properties such as large magnetic anisotropy and slow magnetic relaxation, but a systematic exploration of the relevant chemical space for these compounds is missing. Here, we automatically extract all entries of mononuclear Dy coordination complexes from crystallographic databases and use multireference ab initio methods to compute their magnetic anisotropy. In addition, we generate and simulate magnetic anisotropy for 25k new molecules with the general formula [Dy(H$_2$O)$_5$L$_2$]$^{n-}$ and pentagonal bipyramidal coordination geometry, a motif selected as very promising. While no molecule with record magnetic anisotropy is serendipitously identified in crystallography databases, molecules with crystal field splittings over 1600 cm$^{-1}$ are identified by systematically exploring new organic ligands. This corresponds to a ~100% increase of magnetic anisotropy over the reference compound, ~30% over any known pentagonal bipyramidal Dy complex, and approaching record values of pseudo bi-coordinated Dy ions. This study demonstrates that the fine-tuning of Dy's second coordination sphere by organic ligands design can significantly improve magnetic anisotropy and that automated computational screening is key to accelerating this chemically non-intuitive process. Magnetic anisotropy ; Anisotropy ; Molecule ; Pentagonal bipyramidal molecular geometry ; Ab initio ; Coordination sphere ; Ab initio quantum chemistry methods ; Ion Magnetism in coordination complexes ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced NMR Techniques and Applications