jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhimeng Shao | Bowen Gao | Zhifang Hu | Honglin Jiang | Qidong Zhang | Zhihe Dou | Yanxi Yin
Source: Materials Today Physics (article, 2025-10-17)
Field: Materials Science — Materials Chemistry

Modeling strategies for hydrogen reduction of high-purity metals: From DFT to ReaxFF and machine learning ReaxFF ; Density functional theory ; Bridging (networking) ; Hydrogen ; Molecular dynamics ; Multiscale modeling ; Reduction (mathematics) Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Nuclear Materials and Properties
Authors: Mridul Kumar | Yevgeny Rakita
Source: ArXiv.org (preprint, 2025-08-31)
Field: Materials Science — Materials Chemistry

Protocol for Clustering 4DSTEM Data for Phase Differentiation in Glasses Phase-change materials (PCMs) such as Ge-Sb-Te alloys are widely used in non-volatile memory applications due to their rapid and reversible switching between amorphous and crystalline states. However, their functional properties are strongly governed by nanoscale variations in composition and structure, which are challenging to resolve using conventional techniques. Here, we apply unsupervised machine learning to 4-dimensional scanning transmission electron microscopy (4D-STEM) data to identify compositional and structural heterogeneity in Ge-Sb-Te. After preprocessing and dimensionality reduction with principal component analysis (PCA), cluster validation was performed with t-SNE and UMAP, followed by k-means clustering optimized through silhouette scoring. Four distinct clusters were identified which were mapped back to the diffraction data. Elemental intensity histograms revealed chemical signatures change across clusters, oxygen and germanium enrichment in Cluster 1, tellurium in Cluster 2, antimony in Cluster 3, and germanium again in Cluster 4. Furthermore, averaged diffraction patterns from these clusters confirmed structural variations. Together, these findings demonstrate that clustering analysis can provide a powerful framework for correlating local chemical and structural features in PCMs, offering deeper insights into their intrinsic heterogeneity. Cluster analysis ; Principal component analysis ; Silhouette ; Cluster (spacecraft) ; Pattern recognition (psychology) ; Germanium ; Dimensionality reduction ; Amorphous solid ; Phase (matter) Phase-change materials and chalcogenides ; Chemical and Physical Properties of Materials ; X-ray Diffraction in Crystallography
Authors: Tao Xia (408280) | Tingzhen Chen (17275390) | Wei Wu (27708) | Chen Zhou (466781)
Source: Figshare (article, 2025-10-20)
Field: Materials Science — Materials Chemistry

A Deep Learning-BasedFramework for Valence Bond StructureSelection and Weight Prediction The valence bond (VB) theory offers a chemically intuitive, multiconfigurational framework for analyzing bonding, resonance, and reaction mechanisms. However, its broader application has been limited by high computational costs. In this paper, we present DLVB, a deep learning-based framework that integrates the VB theory with graph transformers through a chemically meaningful representation of VB structures. DLVB accurately predicts VB structural weights without the need for <i>ab initio</i> calculations and provides an efficient selected configuration interaction (SCI) scheme for identifying key configurations that enable the construction of compact VB wave functions. The DLVB-based SCI scheme can identify important VB structures from arbitrary structure sets within a given active space, outperforming traditional ionic-order-based selection methods in both accuracy and scalability. This approach offers a new pathway for extending the applicability of the VB theory to the bonding analysis of systems with larger active spaces and increased molecular complexity. Valence bond theory ; Valence (chemistry) ; Modern valence bond theory ; Graph theory ; Representation (politics) ; Generalized valence bond ; Bond graph ; Scheme (mathematics) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Iam‐Choon Khoo
Source:  (article, 2025-09-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Monocrystalline blue-phase liquid crystals for optical wave mixing and prospective applications in passive optical limiting and image processing Recent advances in the synthesis of large areal size monocrystalline 3-D chiral photonic crystals based on blue-phase liquid crystals (BPLC) are briefly reviewed. Unlike their usual polycrystal counterparts formed by self-assembly, monocrystalline BPLC’s ensure crystal/optical-phase uniformity, leading to significant improvement in the overall image/signal quality and resolution as well as process efficiency in optical wave-mixing based real time holography and image processing. In general, optical nonlinearity (characterized by the intensity dependent index coefficient n<sub>2</sub> in cm<sup>2</sup>/Watt) of a transparent material is in inverse relationship to the response speed &tau;, i.e., n<sub>2</sub>/&tau; is roughly a constant. Noteworthily, pristine and transparent BPLC’s having the same organic constituents as their 1-D cholesteric counterpart, possess effective optical nonlinearities near the band-edges that can be as high as 10<sup>-11</sup> to 10<sup>-10</sup>cm<sup>2</sup>/Watt with a sub-picosecond [10<sup>-13</sup>s or less] response time, i.e. n<sub>2</sub>/&tau;~10<sup>2</sup> to 10<sup>3</sup>. This is two to three orders of magnitude larger than all other known transparent nonlinear optical materials. Special fabrication techniques of large areal size BPLC will be presented, along with their prospective use in ultrafast laser pulse modulation and polarization switching, wave mixing, passive optical limiting, free-space optical wave front conjugation and neural network-like image processing operation. Monocrystalline silicon ; Ultrashort pulse ; Laser ; Holography ; Liquid crystal ; Birefringence ; Optical engineering ; Light intensity ; Nonlinear optics ; Fabrication Liquid Crystal Research Advancements ; Orbital Angular Momentum in Optics ; Molecular spectroscopy and chirality
Authors: Christian Lenser | Denise Ramler | Alexander Schwiers | Norbert H. Menzler | Olivier Guillon
Source: ECS Meeting Abstracts (article, 2025-07-11)
Field: Materials Science — Materials Chemistry

(<i>Invited</i>) Interdiffusion in Multi-Layer Electrolytes for Solid Oxide Cells: Co-Firing and Rapid Densification Techniques Doped ceria (DC) is a ubiquitous material for high-temperature solid oxide fuel and electrolysis cells (SOFC/SOEC). When used as a fuel-electrode material, DC exhibits high electrochemical activity and good stability in Ni-DC cermets. As an electrolyte material, DC offers high ionic conductivity and low-resistance interfaces to Ni-DC cermet electrodes, but the mixed conductivity necessitates the inclusion of an electron blocking layer. This presentation will cover the processing and co-firing of multi-layer electrolytes of Gadolinia-doped ceria (GDC) and Yttria-stabilized zirconia (YSZ), primarily by suspension-based techniques. A statistical analysis of the parameter space of the paste constituents on the paste rheology reveals that the damping factor tan δ and the yield point of the paste are the best predictors for the fidelity of the print. It will be shown that the common particle size descriptor of the d 50 is not well correlated to tan δ and the yield point, and that a bimodality coefficient calculated from skewness and kurtosis of the particle size distribution shows strong correlation with the damping factor, while the yield point of the paste is heavily influenced by the binder content. The importance of these factors for producing desirable printed layers will be shown. Moreover, understanding the correlation of the powder properties to the relevant rheological parameters is essential for the application of machine-learning to design printable pastes. While precise control of the screen-printing parameters enables the fabrication of dense and gas-tight electrolytes, the amount of interdiffusion between the GDC and YSZ phases during co-firing remains an issue. Among the investigated mitigation strategies are several sintering techniques that enable rapid densification in short timeframes, namely ultra-high temperature sintering (UHS), field-assisted sintering (FAST) and black-light sintering (BLS). For these novel techniques, initial investigations show that the rapid densification of GDC-YSZ powder mixtures is accompanied by severe interdiffusion and the formation of mixed phases irrespective of the very short interaction times. In addition, the sample environments present during FAST and UHS lead to reduction of the specimen, negatively impacting density and mechanical stability. While these techniques show promising results for chemically homogeneous materials, the problem of interdiffusion in the YSZ-GDC system is not mitigated. Electrolyte ; Layer (electronics) ; Materials science ; Oxide ; Chemical engineering ; Nanotechnology ; Metallurgy ; Chemistry ; Electrode ; Physical chemistry ; Engineering Advancements in Solid Oxide Fuel Cells
Authors: Lei Liao | Yawei Lv | Shihong Yu | Tang Weimin | Haipeng Lan | Hui‐Xiong Deng | Kenli Li | Changzhong Jiang
Source: Research Square (preprint, 2025-11-07)
Field: Materials Science — Materials Chemistry

Physics-aware graph neural networks for automated tight-binding model construction in quantum transport simulations Ab initio ; Quantum ; Scaling ; Nanoelectronics ; Graph ; Multiscale modeling ; Artificial neural network ; Density functional theory ; Band gap Machine Learning in Materials Science ; Thin-Film Transistor Technologies ; Advanced Memory and Neural Computing
Authors: Andrés Solé | Albert Mosella-Montoro | Joan Cardona | Daniel Aravena | Silvia Gómez‐Coca | Eliseo Ruíz | Javier Ruiz‐Hidalgo
Source: npj Computational Materials (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

PRISM: Periodic Representation with multIscale and Similarity graph Modelling for enhanced crystal structure property prediction <title>Abstract</title> Crystal structures are characterised by repeating atomic patterns within unit cells across three-dimensional space, posing unique challenges for graph-based representation learning. Current methods often overlook essential periodic boundary conditions and multiscale interactions inherent to crystalline structures. In this paper, we introduce PRISM, a graph neural network framework that explicitly integrates multiscale representations and periodic feature encoding by employing a set of expert modules, each specialised in encoding distinct structural and chemical aspects of periodic systems. Extensive experiments across crystal structure-based benchmarks demonstrate that PRISM improves state-of-the-art predictive accuracy, significantly enhancing crystal property prediction. The code will be released on GitHub upon acceptance. Property (philosophy) ; Representation (politics) ; Graph ; Encoding (memory) ; Set (abstract data type) ; Code (set theory) ; Feature (linguistics) ; Periodic boundary conditions ; Computation Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Crystallography and molecular interactions
Authors: Hossein Tahmasbi | Andreas Knüpfer | Thomas D. Kühne | Hossein Mirhosseini
Source: arXiv (Cornell University) (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

Benchmarking Universal Interatomic Potentials on Elemental Systems The rapid emergence of universal Machine Learning Interatomic Potentials (uMLIPs) has transformed materials modeling. However, a comprehensive understanding of their generalization behavior across configurational space remains an open challenge. In this work, we introduce a benchmarking framework to evaluate both the equilibrium and far-from-equilibrium performance of state-of-the-art uMLIPs, including three MACE-based models, MatterSim, and PET-MAD. Our assessment utilizes Equation-of-State (EOS) tests to evaluate near-equilibrium properties, such as bulk moduli and equilibrium volumes, alongside extensive Minima Hopping (MH) structural searches to probe the global Potential Energy Surface (PES). Here, we assess universality within the fundamental limit of unary (elemental) systems, which serve as a necessary baseline for broader chemical generalization and provide a framework that can be systematically extended to multicomponent materials. We find that while most models exhibit high accuracy in reproducing equilibrium volumes for transition metals, significant performance gaps emerge in alkali and alkaline earth metal groups. Crucially, our MH results reveal a decoupling between search efficiency and structural fidelity, highlighting that smoother learned PESs do not necessarily yield more accurate energetic landscapes. Benchmarking ; Universality (dynamical systems) ; Maxima and minima ; Interatomic potential ; Granularity ; Benchmark (surveying) ; Generalization Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Quantum many-body systems
Authors: Guobin Zhao | Logan M. Brabson | Saumil Chheda | Ju Huang | Haewon Kim | K. Liu | K. Mochida | Thang Duc Pham | Prerna Prerna | Gianmarco Terrones | Sunghyun Yoon | Lionel Zoubritzky | François‐Xavier Coudert | Maciej Harańczyk | Heather J. Kulik | Mohamad Moosavi | David S. Sholl | Ilja Siepmann | Randall Q. Snurr | Yongchul G. Chung
Source: ChemRxiv (preprint, 2025-03-21)
Field: Chemistry — Inorganic Chemistry

CoRE MOF DB: a curated experimental metal-organic framework database with machine-learned properties for integrated material-process screening We present an updated version of the CoRE MOF database, which includes a curated set of computation-ready MOF crystal structures designed for high-throughput computational materials discovery. Data collection and curation procedures were improved from the previous version to enable more frequent updates in the future. Machine learning-predicted properties, such as stability metrics and heat capacities, are included in the dataset to streamline screening activities. An updated version of MOFid was developed to provide detailed information on metal nodes, organic linkers, and topologies of a MOF structure. DDEC06 partial atomic charges of MOFs were assigned based on a machine learning model. Gibbs-Ensemble Monte Carlo simulations were used to classify the hydrophobicity of MOFs. The finalized dataset was subsequently used to perform integrated material-process screening for various carbon capture conditions using high-fidelity temperature-swing adsorption (TSA) simulations. Our workflow identified multiple MOF candidates that are predicted to outperform CALF-20 for these applications. Process (computing) ; Core (optical fiber) ; Computer science ; Database ; Metal-organic framework ; Key (lock) ; Chemistry ; Programming language Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Zhiguo Hao | Zhipeng Li | Xing Li | Yanfang Gao | Lijun Li | Ridong He
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Multi-physics Coupling Model for Molten Hydroxide Direct Carbon Fuel Cells: Performance Prediction and Validation under Dual Gradient Distribution of Concentration and Temperature Electrolyte ; Isothermal process ; Power density ; Coupling (piping) ; Multiphysics ; Electrochemistry ; Range (aeronautics) ; Power (physics) ; Heat transfer Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Ndenga, Barack
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-25)
Field: Chemistry — Physical and Theoretical Chemistry

Quantum π-Unification II: Definition, Mathematical Structure, and Foundational Properties of the Quantum π for Molecular Systems This work presents the second article of the Quantum-π Series, introducing a complete mathematical and numerical demonstration of the Quantum-π invariant (π_q) for molecular π-systems. I formally define the Quantum-π value for discrete and continuous electronic amplitudes, propose the general computation rule π_q = π (1 + W_eff), and derive the effective winding W_eff from amplitude-weighted phase evolution along a molecular contour. Using model systems such as benzene, cyclobutadiene, and pyridine, I compute π_q numerically through phase unwrapping and discrete sampling of Ψ_eff, showing clear correlations with aromaticity, heteroatom perturbation, and electronic instability. The results confirm that π_q behaves as a robust phase-information invariant capable of distinguishing aromatic, antiaromatic, and perturbed π-structures. The manuscript includes a fully reproducible methodology, a computation workflow, proposed figure prompts, and a detailed theoretical framework supporting future extensions toward biomolecules, polymers, quantum devices, and machine-learning–augmented chemical prediction. This article continues the scientific program dedicated to unifying geometry, electronic structure, and phase topology through the Quantum-π concept. Computation ; Invariant (physics) ; Quantum computer ; Quantum ; Topology (electrical circuits) ; Quantum phases Crystallography and molecular interactions ; Synthesis and Properties of Aromatic Compounds ; Advanced Physical and Chemical Molecular Interactions
Authors: Jie Zhao | Changqing Dong | Junjie Xue | Xiaoying Hu | Junjiao Zhang
Source: The Journal of Physical Chemistry C (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

Prediction of Oxygen Vacancy Formation Energy in Oxides Based on the Graph Neural Network Approach Oxygen vacancy formation energy (Eovf) prediction plays an important role in addressing the issue of performance evaluation in various applications of materials. In this study, a convolutional and attentional graph neural network (CAGNN) was proposed for the prediction of charge-neutral Eovf at arbitrary oxygen sites of oxide crystals. The crystal structure and atomic properties were the only needed input. For the elements in the training set distribution, the mean absolute error (MAE) of predicting Eovf with CAGNN was 0.50 eV. The computational cost of CAGNN was significantly reduced compared to density functional theory (DFT). The prediction of Eovf (oxides containing n cationic elements, n = 2, 3, 4) with the CAGNN model trained by the data of oxides containing m (m < n) cationic elements were carried out, which validated the model’s generalization and extrapolation performance. Finally, through the materials rediscovery tests, CAGNN shows promising application prospects in material discovery for fields with a strong correlation to the Eovf. Extrapolation ; Artificial neural network ; Generalization ; Convolutional neural network ; Vacancy defect ; Graph ; Density functional theory ; Oxygen ; Training set Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Mary T. Ajide (15940347) | Parisa Naeiji (7871210) | Joaquín Klug (1953574) | Niall J. English (1332720)
Source: Figshare (article, 2025-06-10)
Field: Materials Science — Materials Chemistry

Machine LearningForce Field Predictions of Structuraland Dynamical Properties in HOPG Defects and the HOPG-Water Interfacewith Electronic Structure Analysis Understanding the electronic, structural, and dynamical properties of highly oriented pyrolytic graphite (HOPG) and its interface with water is crucial, as its layered structure, vacancy defects, and elemental doping offer untapped potential across various fields. This study employed <i>ab initio</i> (DFT) methods and state-of-the-art “on-the-fly” machine learning force fields (MLFFs) to investigate pristine HOPG, vacancy-engineered and doped systems (N, O, S), graphene nanoribbon (GNR) interfaces, and the HOPG-water interface. Projected, total, and local density of states (PDOS, TDOS, LDOS) analyses revealed that defects and dopants significantly modified the electronic band structure by introducing midgap states, altering orbital overlaps, shifting the Fermi level, and inducing local magnetic moments. GNRs interfaced with HOPG exhibited hybridized electronic states, forming distinct bands via π-orbital interactions, while dopants such as C–N improved conductivity, C–O introduced midgap states, and C–S generated localized defect states affecting conductivity and reactivity. Defect-induced crystallographic distortions were captured in 3D band structures, and mean squared displacement (MSD) analysis confirmed atomic stability in solid HOPG and structured water layering at the HOPG-water interface due to disrupted hydrogen bonding. The integration of MLFFs enabled efficient and accurate simulations of large atomic systems, significantly reducing the computational cost of <i>ab initio</i> molecular dynamics. Overall, this work offers critical insights into how defects and interfacial environments influence HOPG’s electronic behavior, providing a robust computational framework for the rational design of defect-engineered carbon-based materials. These findings have direct implications for advancing HOPG in catalysis, energy storage systems such as batteries and supercapacitors, nanoelectronics including field-effect transistors, as well as in sensors, functional coatings, and other next-generation electronic applications. Electronic structure ; Graphene ; Dopant ; Density functional theory ; Highly oriented pyrolytic graphite ; Nanoelectronics ; Vacancy defect ; Band gap ; Doping Machine Learning in Materials Science ; Graphene research and applications ; Electrocatalysts for Energy Conversion
Authors: Qing Lu | Zhongwei Zhang | Yijie Zhu | C. Y. Ding | Xiaomeng Wang | Junjie Wang | Yi Han | Jian Sun
Source: The Journal of Chemical Physics (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

Diverse carbon units in high-pressure C–K system predicted from first-principles and machine-learning methods Metal-carbon compounds are significant for their diverse carbon units, which exhibit distinctive electronic and bonding properties, and broad application potential. In this article, we present a detailed C-K phase diagram, constructed using the crystal structure prediction method, MAGUS, based on machine-learning potentials fitted from first-principles calculations, revealing diverse carbon units, from allylenide ions to graphene-like two-dimensional layers. We found that the Pnma C12K16 phase contains allylenide ions, which contribute to its insulating behavior. Meanwhile, the ambient-pressure stable Cmcm C12K4 phase contains potassium-intercalated carbon layers with unique pentagonal-hexagonal-heptagonal (5-6-7) carbon rings, which we term "σ-graphene." This σ-graphene monolayer can be synthesized either by exfoliating bulk C12K4 using an electrochemical method or by removing potassium atoms via evaporating. Furthermore, Boltzmann transport calculations show that pristine σ-graphene exhibits a high electrical conductivity (∼5.5 × 107 S/m at 300 K), comparable with silver and copper, making it a promising material for electrical transport applications. In addition, σ-graphene demonstrates excellent adsorption capabilities for O2 and NO2, with adsorption energies of -0.503 and -0.528 eV, respectively, suggesting potential applications in catalysis and environmental monitoring. Our work highlights the C-K system as a versatile platform for synthesizing and applying novel carbon-based materials. Carbon fibers ; Monolayer ; Adsorption ; Phase (matter) ; Electrochemistry ; Work (physics) ; Crystal (programming language) Boron and Carbon Nanomaterials Research ; Graphene research and applications ; Electrocatalysts for Energy Conversion