jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Niclas S. Mueller | Alexander P. Fellows | Ben John | Andrew E. Naclerio | Christian Carbogno | K. Gharagozloo-Hubmann | Damián Baláž | Ryan A. Kowalski | Hendrik H. Heenen | Christoph Scheurer | Karsten Reuter | Joshua D. Caldwell | Martin Wolf | Piran R. Kidambi | Martin Thämer | Alexander Paarmann
Source: ArXiv.org (preprint, 2025-04-22)
Field: Materials Science — Materials Chemistry

Full Crystallographic Imaging of Hexagonal Boron Nitride Monolayers with Phonon-Enhanced Sum-Frequency Microscopy Hexagonal boron nitride (hBN) is an important 2D material for van der Waals heterostructures, single photon emitters, and infrared nanophotonics. The optical characterization of mono- and few-layer samples of hBN however remains a challenge as the material is almost invisible optically. Here we introduce phase-resolved sum-frequency microscopy as a technique for imaging monolayers of hBN grown by chemical vapor deposition (CVD) and visualize their crystal orientation. A combination of femtosecond mid-infrared (IR) and visible laser pulses is used for sum-frequency generation (SFG), which is imaged in a wide-field optical microscope. The IR laser resonantly excites a phonon of hBN that leads to an ~800-fold enhancement of the SFG intensity, making it possible to image large 100x100 μm2 sample areas in less than 1 s. Implementing heterodyne detection in combination with azimuthal rotation of the sample further provides full crystallographic information. Through combined knowledge of topography and crystal orientation, we find that triangular domains of CVD-grown monolayer hBN have nitrogen-terminated zigzag edges. Overall, SFG microscopy can be used as an ultra-sensitive tool to image crystal structure, strain, stacking sequences, and twist angles, and is applicable to the wide range of van der Waals structures, where location and identification of monolayer regions and interfaces with broken inversion symmetry is of paramount importance. Monolayer ; van der Waals force ; Femtosecond ; Microscopy ; Chemical vapor deposition ; Stacking ; Crystal (programming language) ; Infrared microscopy ; Optical microscope Boron and Carbon Nanomaterials Research ; Force Microscopy Techniques and Applications ; 2D Materials and Applications
Authors: Jie Yao | Cristiana L. Ciobanu | Nigel J. Cook | Kathy Ehrig | Ashley D. Slattery | Eugene S. Ilton | Nοbuyοshi Miyajima | Gerd Steinle‐Neumann
Source: American Mineralogist (article, 2025-08-21)
Field: Chemistry — Inorganic Chemistry

Long-range modulation in uranium-bearing hematite superstructure constrained from STEM analysis and ab initio calculations Abstract Natural hematite from the Mesoproterozoic Olympic Dam Cu-U-Au-Ag deposit, South Australia, contains pentavalent uranium, with an average composition of Fe1.974U0.0138Pb0.0045O3. Scanning transmission electron microscopy (STEM) and ab initio calculations provide evidence for the superstructuring of hematite to accommodate uranium. High-angle annular dark-field STEM imaging reveals a 12-fold, long-range modulation along &amp;lt;1¯100&amp;gt;* directions as well as blocks of bright and dark contrast. This modulation was reproduced using an empirical model of a 4 × 4 × 1 hematite superstructure (Fe1.467O3) with variable Fe occupancy over 12 layers of Fe. An excellent match is observed between the model, electron diffraction patterns, and images, yet analytical data obtained on the [22¯01] zone axis require expansion of the superstructure to a 4 × 4 × 4 unit. Given that the large size of the 4 × 4 × 4 hematite superstructure prohibits ab initio calculations to constrain a model with variable Fe and O occupancies to achieve charge balance, a simplified Fe and O vacancy scheme is employed for the incorporation of U5+. The model involves a double-corner Fe vacancy environment constrained for pentavalent U from prior ab initio simulations and localized O vacancies for charge balance. Our assessment of this relaxed superstructure (Fe1.9693+U0.0105+O2.9792−) with 12 Fe vacancies and 8 O vacancies shows a good match with analytical data. Calculated Bader charge and magnetic moment validate the inference of U5+ incorporation into the hematite superstructure. Moreover, formation energy calculations show how the superstructure is stabilized by the presence of U relative to the vacancy defect structure. The natural U-rich hematite coexists with nanoscale magnetite needles, and the formation of magnetite and local collapse of the hematite superstructure to disordered U-bearing hematite are interpreted as a response to radioactive decay of U over 1.6 Ga. Models for U incorporation in Fe-oxides that combine ab initio calculations and STEM analysis are further valuable for the long-term storage of radioactive waste as much as for optimized, low environmental impact exploitation of uranium-bearing ores. Superstructure ; Hematite ; Ab initio ; Uranium ; Bearing (navigation) ; Range (aeronautics) ; Materials science ; Crystallography ; Chemistry ; Mineralogy ; Thermodynamics ; Physics ; Metallurgy ; Organic chemistry ; Composite material Radioactive element chemistry and processing ; Nuclear Materials and Properties ; Nuclear materials and radiation effects
Authors: Sébastian Volz | Jie Chen | Zhongwei Zhang | Yangyu Guo | Marc Bescond
Source: Research Square (preprint, 2025-12-08)
Field: Materials Science — Materials Chemistry

A Unfied Thermal Transport Picture Across Phases via Spatiotemporal Coherence Coherence (philosophical gambling strategy) ; Thermal conduction ; Thermal conductivity ; Thermal ; Dissipation ; Energy transport ; Phase transition ; Amorphous solid Thermal properties of materials ; Thermography and Photoacoustic Techniques ; Thermoelastic and Magnetoelastic Phenomena
Authors: Florian Bogdain | Sebastian Mai | Leticia González | Oliver Kuehn
Source: ChemRxiv (preprint, 2025-06-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

BSE@GW-Based Protocol for Spin-Vibronic Quantum Dynamics Using the Linear Vibronic Coupling Model. Formulation and Application to an Fe(II) Compound A protocol for generating potential energy surfaces and performing photoinduced nonadiabatic multidimensional wave packet propagation is presented. The workflow starts with the parameterization of a linear vibronic coupling (LVC) Hamiltonian using the BSE@GW approach. In a second step, the LVC model is used as input for multi-layer multi-configurational time-dependent Hartree (ML-MCTDH) wave packet propagation. To facilitate automated ML tree generation, a spectral clustering algorithm is applied based on a correlation matrix obtained from nuclear coordinate expectation values of a full-dimensional Time-dependent Hartree (TDH) simulation. The performance of the protocol is tested on the photoinduced spin-vibronic dynamics of a transition metal complex, [Fe(cpmp)]$^{+2}$. For this example, it is shown that BSE@GW provides a more robust description of the character of the transitions contributing to the absorption spectrum compared to TD-DFT. Furthermore, the LVC parameterization is tested against explicit calculations of potential energy curves to find the validity of the linear approximation over a wide range of normal mode elongation. Finally, the flexibility of spectral clustering is used to generate different ML trees, resulting in very different numerical efficiencies for ML-MCTDH propagation. In terms of electronic structure and dimensionality, [Fe(cpmp)]$^{+2}$ is a challenging example, suggesting that the new protocol should be applicable to a wide range of systems. Vibronic coupling ; Vibronic spectroscopy ; Coupling (piping) ; Spin (aerodynamics) ; Physics ; Molecular physics ; Quantum mechanics ; Chemistry ; Materials science ; Excited state ; Thermodynamics Magnetism in coordination complexes ; Advanced NMR Techniques and Applications ; Electron Spin Resonance Studies
Authors: Hiroki Kurita | Takeru Nakaki | Zhenjin Wang | Fumio Narita
Source: Smart Materials and Structures (article, 2025-10-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Fabrication and characterization of Ti–6Al–4V/Fe–48Co magnetostrictive composites via spark plasma sintering for structural health monitoring applications Abstract In this study, Ti–6Al–4V/Fe–48Co composites were fabricated via spark plasma sintering using Ti–6Al–4V and Fe–48Co alloy powders as starting materials, to apply them to structural health monitoring (SHM). The resulting composites exhibited distinct magnetostrictive behavior, with magnetostriction values increasing as the Fe–48Co content increased. Under compressive loading, the composites showed proportional changes in magnetic flux density. Furthermore, voltage generation was confirmed under impact loading. Notably, detectable signals were observed even in composites with low Fe–48Co content, indicating the potential to achieve both high specific strength and functional sensing capability. These results suggest that Ti–6Al–4V/Fe–48Co composites are promising candidates for next-generation SHM systems. Spark plasma sintering ; Magnetostriction ; Fabrication ; Sintering ; Alloy ; Characterization (materials science) ; Structural health monitoring Magnetic Properties and Applications ; Shape Memory Alloy Transformations ; Metallic Glasses and Amorphous Alloys
Authors: Shahnizan Imran Mohd Nazri | Nur Farhayu Ariffin | Mohamad Firdaus Mohamad Borhan
Source: Challenge Journal of Concrete Research Letters (article, 2025-12-22)
Field: Materials Science — Polymers and Plastics

Evolutionary process and mechanical properties of polymers: A comprehensive review The growing demand for innovative construction materials has driven the exploration of polymer-based composites due to their enhanced mechanical performance and potential environmental benefits. However, a clear understanding of the historical development, classification, and structural behavior of polymers in construction remains limited. This review aims to bridge this gap by synthesizing current knowledge on the historical evolution, mechanical benefits, chemical benefits, drawbacks and real world applications of polymers in construction. The study focuses on key thermosetting resins such as polyester, epoxy, vinyl ester, and PET and assesses their mechanical properties relevant to structural applications. To address the lack of integrated insight in the literature, the review compiles and compares data from various primary sources. Mechanical properties such as tensile, compressive, and flexural strength are evaluated to determine the suitability of each polymer for construction use. The outcomes indicate that epoxy resins demonstrate superior tensile and flexural strength, while polyester and polyethylene terephthalate PET offer notable advantages in compressive resistance and sustainability. Overall, the findings provide a consolidated understanding of polymer development and performance, offering valuable guidance for researchers and engineers aiming to enhance material efficiency, durability, and environmental responsibility in the construction industry. Thermosetting polymer ; Epoxy ; Flexural strength ; Bridge (graph theory) ; Process (computing) ; Polyethylene terephthalate ; Polymer ; Polyester Natural Fiber Reinforced Composites ; Polymer composites and self-healing ; Epoxy Resin Curing Processes
Authors: Charles R. Kelly | Jacqueline M. Cole
Source: Scientific Data (article, 2025-12-12)
Field: Materials Science — Materials Chemistry

A dataset of Curie and Néel temperatures auto-generated with ChemDataExtractor and the Snowball algorithm An auto-generated dataset of Curie and Néel temperatures is presented, containing 56,037 records extracted from 108,181 published scientific articles. Each record contains the extracted chemical entity and associated extracted temperature. The dataset was auto-generated by mining text from the papers using the 'chemistry-aware' natural-language-processing toolkit, ChemDataExtractor 2.2.2, in conjunction with the Snowball v2 parser, which has been adapted to extract Curie and Néel temperatures from scientific text. This dataset is the first of its kind to be generated using the Snowball v2 parser, with its evaluation yielding a precision of 72% and a recall of 61%. The public availability of this dataset will aid in the design, prediction and analysis of magnetic materials. Curie temperature ; Curie ; Marie curie ; Precision and recall ; Snowball sampling ; Recall Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Biomedical Text Mining and Ontologies
Authors: Thanana Nuchkrua | Sudchai Boonto | Xiaoqi Liu
Source: Preprints.org (preprint, 2025-11-12)
Field: Materials Science — Materials Chemistry

Quantum-Informed Metal-Hydride Hydrogen Storage: Physics-Consistent Multiscale Modeling, Predictive Control, and Digital Twin Framework Classical macroscopic models of metal–hydride (MH) hydrogen storage rely on empirical Arrhenius laws that neglect quantum phenomena such as tunneling, zero-point motion, and hydrogen–lattice interactions. As a result, their predictive and control performance degrade across wide temperature ranges, particularly in cryogenic regimes where quantum transport remains active. This paper presents a unified quantum-informed diffusion and control framework that bridges microscopic hydrogen–lattice physics with macroscopic predictive control. A temperature-dependent quantum correction operator is incorporated into the classical diffusion law, yielding an analytically tractable yet physically enriched model. Parameters are identified through weighted robust regression with bootstrap-based uncertainty quantification and integrated into a model predictive control (MPC) scheme that adapts to temperaturedependent dynamics. Simulation results show that tunneling-enhanced diffusion improves lowtemperature response and reduces steady-state error and control effort by up to 50% compared with classical Arrhenius-based control. While the present study focuses on numerical validation, the proposed architecture establishes a transferable foundation for digital-twin development—linking microscopic quantum transport and system-level predictive control for next-generation hydrogen storage technologies. Model predictive control ; Quantum ; Diffusion ; Operator (biology) ; Control theory (sociology) ; Hydrogen ; Control (management) Hydrogen Storage and Materials ; Electrocatalysts for Energy Conversion ; Ammonia Synthesis and Nitrogen Reduction
Authors: Jonathan Ortiz Vázquez | Gabriel Romero Paredes Rubio | Miguel Galván Arellano | Erick Ismael Castro Ontiveros | R. Peña Sierra
Source: MRS Advances (article, 2025-01-09)
Field: Materials Science — Materials Chemistry

Analysis of the persistent photoconductivity in composite nanocrystalline ZnO/Si films produced by co-sputtering at room temperature Abstract The persistent photoconductivity (PPC) behavior in composite nanocrystalline films ZnO(RF)/Si(DC) films grown by co-sputtering was studied. The studied films were produced by fixing the ZnO RF magnetron power at 120 Watts and varying the DC power for the Si target from 0 to 80 Watts. The produced films were structural, optical, and electrical characterized; n-type films were produced for all the composition range. The PPC behavior was studied in samples grown with the composition formed with the ZnO (RF = 120 W)/Si (DC = 20 W) power condition, and by exposing the sample surfaces under continuous or pulsed ultraviolet (UV) radiation (390–400 nm). After applying an extended cleansing period, the PPC characteristic time constants, τ. 63 , were obtained; the turn-off time constant varied from 16.8 to 22.8 s, while the turn-on time constant changed from 24 to 30 s. These periods are linked to the UV light lattice absorption and the corresponding energy relaxation mechanisms. Graphical abstract Materials science ; Nanocrystalline material ; Sputtering ; Photoconductivity ; Composite number ; Nanocrystal ; Optoelectronics ; Composite material ; Nanotechnology ; Thin film ZnO doping and properties ; Ga2O3 and related materials ; Quantum Dots Synthesis And Properties
Authors: Takao Fujita
Source: ChemRxiv (preprint, 2025-09-10)
Field: Chemistry — Organic Chemistry

Molecular Directed, Bidirected, and Multidirected graphs A directed graph (or digraph) consists of a finite vertex set 𝑉 and a set of ordered edges 𝐸 ⊆ 𝑉 × 𝑉, each edge (𝑢, 𝑣) indicating a one-way connection from 𝑢 (source) to 𝑣 (target). A bidirected graph is a generalization of an undirected graph where each edge is assigned a direction at each of its endpoints independently, allowing more expressive edge orientation. A multidirected graph is a structure with vertices and edges, where edges may repeat, sources and targets are assigned, and multiplicities recorded. A molecular graph models a molecule with atoms as vertices and bonds as edges, representing its structural connectivity. In this paper, we examine definitions such as molecular bidirected graphs and multidirected graphs. These are concepts that extend molecular graphs by incorporating directional information. Symmetric graph ; Undirected graph ; Vertex (graph theory) ; Mixed graph ; Comparability graph ; Neighbourhood (mathematics) ; Graph ; Line graph ; Complement graph ; Independent set Click Chemistry and Applications ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: C. Cao | Z. X. Zhang | J.; id_orcid 0000-0003-2221-0554 Bloem | K. Sima'an
Source: UvA-DARE (University of Amsterdam) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

How Aligned Are Unimodal Language and Graph Encodings of Chemical Molecules? Chemical molecules can be represented as graphs or as language descriptions. Training unimodal models on graphs results in different encodings than training them on language. Therefore, the existing literature force-aligns the unimodal models during training to use them in downstream applications such as drug discovery. But to what extent are graph and language unimodal model representations inherently aligned, i.e., aligned prior to any force-alignment training? Knowing this is useful for a more expedient and effective forced-alignment. For the first time, we explore methods to gauge the alignment of graph and language unimodal models. We find compelling differences between models and their ability to represent slight structural differences without force-alignment. We also present an unified unimodal alignment (<b>U2A</b>) benchmark for gauging the inherent alignment between graph and language encoders which we make available with this paper. Graph ; Language model ; Encoder ; Benchmark (surveying) ; Natural language Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Sajal Kumar Maiti | David Buttar | Fernanda Duarte
Source: ChemRxiv (preprint, 2025-07-07)
Field: Materials Science — Materials Chemistry

Benchmark of double-ended transition state search methods for metal-catalysed reactions Efficient and reliable identification of transition states (TS) is critical for reaction modelling. Among the approaches available, the combination of double-ended TS search with eigenvector-following, referred to as “hierarchical TS search”, is an effective tool to locate TSs starting from reactant and product geometries. However, there has been limited systematic benchmarking of these methods on transition metal-catalysed reactions. Here, we benchmark eight hierarchical double-ended methods, including two which we have implemented within our autodE code. They are tested using out-of-the-box settings on a diverse set of 30 metal-catalysed reactions, evaluating their success rate and computational efficiency. Our results find three key trends: (i) the final eigenvector-following step requires an accurate Hessian matrix or explicitly calculated min-mode for a high success rate, (ii) 'bracket' methods such as Dewar-Healy-Stewart (DHS), which use only two images, outperform popular double-ended methods like climbing image nudged elastic band (CI-NEB), (iii) starting from the same two minima, different hierarchical methods may find different TSs. Furthermore, we observe a correlation between the similarity of the reactant and product geometries with both the success rate and the computational cost. Our findings highlight the effectiveness of double-ended methods for TS search in metal-catalysed systems and the potential of hitherto underutilised methods for TS search workflows. This is further illustrated through two case studies in industrially relevant organometallic reactions. Benchmark (surveying) ; Transition (genetics) ; Transition metal ; State (computer science) ; Computer science ; Catalysis ; Chemistry ; Algorithm ; Organic chemistry ; Geography Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Yaolong Zhang | Hua Guo
Source: Chemical Science (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

Node-equivariant message passing for efficient and accurate machine learning interatomic potentials Machine learned interatomic potentials, particularly equivariant message-passing (MP) models, have demonstrated high fidelity in representing first-principles data, revolutionizing computational studies in materials science, biophysics, and catalysis. However, these equivariant MP models still incur substantial computational and memory needs due to their expensive tensor product operations over edge space, significantly limiting their applicability in large-scale or long-time simulations. In this work, we propose a novel node-equivariant MP (NEMP) framework that performs equivariant operations between the central node and a virtual summed node encoding structure information of its neighbors. Crucially, NEMP maintains comparable or even superior accuracy across diverse test systems-including molecules, extended systems, and universal potential benchmarks-while achieving 1-2 orders of magnitude reduction in memory and computational costs compared to edge equivariant MP models. In fact, NEMP reaches computational efficiency comparable to that of local descriptor-based models, and enabling previously inaccessible large-scale simulations. Node (physics) ; Equivariant map ; Reduction (mathematics) ; Enhanced Data Rates for GSM Evolution ; Computational complexity theory ; Encoding (memory) ; Computational model ; Tensor (intrinsic definition) ; Limiting Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Quantum many-body systems
Authors: Irene Casademont‐Reig
Source: VUBIR (Vrije Universiteit Brussel) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Pursuit of singlet fission fulvene candidates using inverse design Inverse ; Singlet state ; Fission ; Singlet fission ; Resonance (particle physics) Solid-state spectroscopy and crystallography ; Nuclear physics research studies ; Boron Compounds in Chemistry