jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yilin Wang | Jing-Wei Zhao | Shu Gao | S. J. Zhao | Xiaoqi Li | Xue Chen | Hang Zhang | Tianyi Tian | Zhen Zhao
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Spectroscopy

Web System-Assisted Nitrogen-Doped Carbon Dots Fluorescent Probe for Intelligent Detection of Flavonoids Fluorescence ; Nitrogen ; Carbon fibers ; Doping ; Nanotechnology ; Chemistry ; Materials science ; Optoelectronics ; Organic chemistry ; Physics ; Optics Molecular Sensors and Ion Detection ; Carbon and Quantum Dots Applications ; Electrochemical sensors and biosensors
Authors: Yuichi Yokoyama | Kohei Yamagami | Yuta Sumiya | Hayaru Shouno | Masaichiro Mizumaki
Source: ArXiv.org (preprint, 2025-10-10)
Field: Materials Science — Materials Chemistry

Deep prior-based denoising for state-of-the-art scientific imaging and metrology Deep learning has revolutionized computer vision, yet a major gap persists between complex, data-hungry deep learning models and the practical demands of state-of-the-art scientific measurements. To fundamentally bridge this gap, we propose deep prior-based denoising, a robust deep learning model that requires no training data. We demonstrate its effectiveness by removing grid artifacts in angle-resolved photoemission spectroscopy (ARPES), a long-standing and critical data analysis challenge in materials science. Our results demonstrate that deep prior-based denoising yields clearer ARPES images in a fraction of the time required by conventional, experiment-based denoising methods. This ultra-efficient approach to ARPES will enable high-speed, high-resolution three-dimensional band structure mapping in momentum space, thereby dramatically accelerating our understanding of microscopic electronic structures of materials. Beyond ARPES, deep prior-based denoising represents a versatile tool that could become a new standard in any advanced scientific measurement fields where data acquisition is limited. Deep learning ; Noise reduction ; Metrology ; Data acquisition ; Grid ; Noise (video) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Yu-Han Lv | Guangying Li | Lei Zhang | Xiaoli Tan | Tao Bo
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Force Field Development and Microstructure-Property Investigation of Naf-Bef2 Molten Salt Fuel Microstructure ; Molten salt ; Property (philosophy) ; Materials science ; Development (topology) ; Field (mathematics) ; Metallurgy ; Mathematics ; Philosophy Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; High Temperature Alloys and Creep
Authors: Ewa M. Nowara | Joshua A. Rackers | Patricia Suriana | Pan Kessel | M. SHEN | Andrew M. Watkins | Michael Maser
Source: ArXiv.org (preprint, 2025-07-13)
Field: Chemistry — Physical and Theoretical Chemistry

Do we need equivariant models for molecule generation? Deep generative models are increasingly used for molecular discovery, with most recent approaches relying on equivariant graph neural networks (GNNs) under the assumption that explicit equivariance is essential for generating high-quality 3D molecules. However, these models are complex, difficult to train, and scale poorly. We investigate whether non-equivariant convolutional neural networks (CNNs) trained with rotation augmentations can learn equivariance and match the performance of equivariant models. We derive a loss decomposition that separates prediction error from equivariance error, and evaluate how model size, dataset size, and training duration affect performance across denoising, molecule generation, and property prediction. To our knowledge, this is the first study to analyze learned equivariance in generative tasks. Equivariant map ; Property (philosophy) ; Generative grammar ; Generative model ; Graph ; Convolutional neural network ; Artificial neural network ; Dependency (UML) Various Chemistry Research Topics ; History and advancements in chemistry ; Inorganic and Organometallic Chemistry
Authors: Tano Kim Kender | Marco Corrias | Cesare Franchini
Source: Advanced Intelligent Discovery (article, 2025-07-09)
Field: Materials Science — Materials Chemistry

Automatic Determination of Quasicrystalline Patterns from Microscopy Images Quasicrystals are aperiodically ordered solids that exhibit long‐range order without translational periodicity, bridging the gap between crystalline and amorphous materials. Due to their lack of translational periodicity, information on atomic arrangements in quasicrystals cannot be extracted by current crystalline lattice recognition softwares. This work introduces a method to automatically detect quasicrystalline atomic arrangements and tiling using image feature recognition coupled with machine learning, tailored toward quasiperiodic tilings with 8‐, 10‐, and 12‐fold rotational symmetry. Atom positions are identified using clustering of feature descriptors. Subsequent nearest‐neighbor analysis and border following on the interatomic connections deliver the tiling. Support vector machines further increase the quality of the results, reaching an accuracy consistent with those reported in the literature. A statistical analysis of the results is performed. The code is now part of the open‐source package AiSurf. Microscopy ; Materials science ; Optics ; Physics Quasicrystal Structures and Properties ; Nanocluster Synthesis and Applications ; Quantum Dots Synthesis And Properties
Authors: Akram Ibrahim | Ahmed M. Hafez | Mahmooda Sultana | Can Ataca
Source: ArXiv.org (preprint, 2025-04-07)
Field: Materials Science — Materials Chemistry

Data-Driven Molecular Dynamics and TEM Analysis of Crystal Growth and Hydrogen Sensing in Pt-Functionalized Graphene Chemiresistive Sensors Graphene functionalized with catalytic transition metals offers high-performance gas sensing by coupling graphene's exceptional electronic transport properties with the metal's catalytic activity, yet the atomistic relationships connecting synthesis parameters, morphological outcomes, and sensor performance remain elusive. We developed an equivariant machine-learning interatomic potential (MLIP) with near-DFT accuracy to perform molecular dynamics (MD) simulations of Pt crystal growth on graphene and subsequent H$_2$-sensing. MD simulations validated by TEM show that Pt deposition begins with dispersed nuclei coalescing into polycrystalline nanoclusters with predominantly FCC interiors, while both MD and Raman spectroscopy reveal a predominantly non-covalent Pt-graphene interaction that induces moderate local strain and charge transfer yet preserves graphene's structural integrity. Reactive MD simulations confirm H$_2$ dissociative chemisorption exclusively on Pt, with negligible spillover onto pristine graphene. However, H adsorption on Pt attenuates the Pt-graphene interfacial binding, providing an indirect electronic pathway for H$_2$ sensing. Transient and steady-state kinetic analyses demonstrate that an intermediate Pt loading minimizes the limit of detection; lower loadings facilitate faster response and recovery kinetics and enhance signal transduction, whereas higher loadings increase the doping level of graphene. DFT charge analysis indicates that under-coordinated Pt clusters induce $n$-type doping in graphene, whereas continuous Pt films induce $p$-type doping, with both effects attenuated upon H adsorption. The developed machine-learned MD framework enables quantum-mechanically accurate modeling of metal crystal growth on graphene, elucidates the underlying H$_2$-sensing mechanism, and correlates several key sensing figures of merit with metal loading and morphology. Nanoclusters ; Graphene ; Molecular dynamics ; Crystal (programming language) ; Raman spectroscopy ; Chemisorption ; Transition metal ; Crystallite ; Interatomic potential Graphene research and applications ; Gas Sensing Nanomaterials and Sensors ; 2D Materials and Applications
Authors: Deepa Sreedev (18776776) | Nandakumar Kalarikkal (4962604) | Subila Kurukkal Balakrishnan (18776779)
Source: Figshare (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

Leveraging MachineLearning for Thermoelectric MaterialDesign: Addressing Composition–Property Relations and DataImbalance Challenges Thermoelectric (TE) technology has emerged as a promising and sustainable solution to address the growing global energy demand. While machine learning accelerates the discovery of high-performance thermoelectric materials, its effectiveness is frequently hampered by data imbalance and quality issues. This study addresses these challenges by utilizing a highly imbalanced data set comprising Germanium Telluride (GeTe) materials, including both pure GeTe and its doped or alloyed variants. A classification model was developed based on four key descriptors, such as temperature, Seebeck coefficient, electronegativity, and electron affinity, to categorize samples into low, medium, and high figure of merit (ZT) classes. To mitigate the effects of class imbalance, an ensemble learning approach was combined with the Adaptive Synthetic Sampling (ADASYN) oversampling technique. Among the models evaluated, the XGBoost classifier demonstrated superior performance, achieving a macro-average precision of 0.94, recall of 0.95, F1-score of 0.94, and an overall accuracy of 94%, making it the most effective model for identifying high-performance TE materials under imbalanced conditions. The XGBoost regression model performed well with an <i>R</i><sup>2</sup> of 0.97 and an RMSE of 0.07, allowing for effective screening of materials with high ZT values. To improve model interpretability, SHAP (SHapley Additive exPlanations) analysis was conducted, which revealed that temperature is the most significant factor for predicting the figure of merit. This work provides a solid and interpretable framework for accelerating the discovery of thermoelectric materials for next-generation energy conversion technologies. Oversampling ; Thermoelectric materials ; Thermoelectric effect ; Figure of merit ; Classifier (UML) ; Precision and recall ; Ensemble learning ; Thermoelectric generator Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Machine Learning and ELM
Authors: Mitchell Falgoust | Peter Kroll
Source: Advanced Theory and Simulations (article, 2025-10-07)
Field: Materials Science — Polymers and Plastics

Cross‐Linking Matters: Atomistic Simulations of the Thermal Degradation of Polymethylsiloxanes Abstract Utilizing an advanced machine‐learning interatomic potential (MLIP) for Si─C─O─H, the thermal breakdown of polysiloxanes covering polymers from linear polydimethylsiloxane (PDMS) to highly networked polymethylsilsesquioxane (PMSQ), is investigated. The reactive simulations reveal that increasing cross‐linking density enhances both thermal stability and ceramic yield after pyrolysis. Multiple pathways for forming siloxane oligomers are uncovered–pathways that depend on the number of unbranched units in the polymer. By simulating the release of gaseous species, the mass‐retention profiles observed in experiments are reproduced: PMSQ exhibits the highest mass retention, while PDMS is prone to substantial mass loss. Non‐isothermal simulations produce thermogravimetric studies and show that higher thermal stability and the peak of mass loss rate align with the degree of cross‐linking. Moreover, these simulations enable a kinetic analysis of TGA data, yielding an activation energy for PDMS degradation that is consistent with literature values. Finally, it is shown that PDMS domains embedded within PMSQ matrices act as sacrificial templates, carving out nanoscale voids that give rise to tailored porosity in the final SiCO ceramics. Overall, the atomistic simulations provide insights into reaction mechanisms and validate experimental trends and offer predictive guidance for tuning preceramic polymers toward tailored ceramic yields and microstructures. Polydimethylsiloxane ; Thermogravimetric analysis ; Polymer ; Ceramic ; Thermal stability ; Degradation (telecommunications) ; Thermal ; Siloxane Polymer crystallization and properties ; Silicone and Siloxane Chemistry ; Polymer composites and self-healing
Authors: Nupur Prakash | Paul Gasper | Francois L. E. Usseglio‐Viretta
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Machine Learning Segmentation for Microscopy with Domain-Informed Targets via Custom Loss Semantic segmentation is a critical step in microscopy analysis to enable quantification of sample properties or to run structurally-resolved physics-based simulations. Machine learning has emerged as a viable alternative to traditional segmentation approaches like thresholding or watershed segmentation due to its noise tolerance and ability to perform shape- and texture-based segmentation. However, traditional methods still maintain an advantage by allowing for the explicit incorporation of domain knowledge that may be known a priori or measured ex situ, for example, enforcing that the volume fractions of phases from the segmentation match known values. In comparison, machine learning methods for semantic segmentation in the materials domain, which are limited by sparsely available hand-labels for model training, cannot explicitly incorporate domain knowledge into the classification problem, limiting their trustability and explainability. Here, we develop new regularization loss terms that incorporate domain knowledge into the training of a tree-based machine learning classification model, and demonstrate that the predicted segmentation can be tuned without modifying the training labels. The loss terms presented here enable targeting of specific volume fractions for the predicted phases as well as maximizing or minimizing the connectivity of a target phase. This method provides materials researchers additional knobs to tune the output of a machine learning-based segmentation model, leveraging the capabilities of machine-learned segmentation models while enabling domain knowledge to be explicitly incorporated into the model training process. Figure 1 Segmentation ; Thresholding ; A priori and a posteriori ; Scale-space segmentation ; Image segmentation ; Domain (mathematical analysis) ; Pattern recognition (psychology) ; Segmentation-based object categorization Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Block Copolymer Self-Assembly
Authors: Felipe Hawthorne | Paulo R. E. Raulino | Ronaldo Rodrigues Pelá | Cristiano F. Woellner
Source: ArXiv.org (preprint, 2025-05-17)
Field: Materials Science — Materials Chemistry

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Machine learning interatomic potentials (MLIPs) offer an efficient and accurate framework for large-scale molecular dynamics (MD) simulations, effectively bridging the gap between classical force fields and \textit{ab initio} methods. In this work, we present a reactive MLIP for graphene, trained on an extensive dataset generated via \textit{ab initio} molecular dynamics (AIMD) simulations. The model accurately reproduces key mechanical and vibrational properties, including stress-strain behavior, elastic constants, phonon dispersion, and vibrational density of states. Notably, it captures temperature-dependent fracture mechanisms and the emergence of linear acetylenic carbon chains upon tearing. The phonon analysis also reveals the expected quadratic ZA mode and excellent agreement with experimental and DFT benchmarks. Our MLIP scales linearly with system size, enabling simulations of large graphene sheets with \textit{ab initio}-level precision. This work delivers a robust and transferable MLIP, alongside an accessible training workflow that can be extended to other materials. Bridging (networking) ; Phonon ; Molecular dynamics ; Quadratic equation ; Interatomic potential ; Work (physics) ; Workflow ; Molecular vibration Machine Learning in Materials Science ; 2D Materials and Applications ; Graphene research and applications
Authors: Luı́s M. Bernardo | Andre Luiz da S. Pereira | Daniel Dias | Hélida Vasques Peixoto Vieira | Renato Malbar | Aline Machado de Azevedo Novaes | Rafaella Magliano Balbi de Faria | Rogério M. Carvalho | Maria Luísa A. Gonçalves | Ana Mehl | Leandro A. F. Fernandes
Source:  (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

Uncertainty-Aware Chemical Analyses via Monte Carlo Deep Model Inference We present a computer vision-based solution for estimating Chloride concentration and pH in water samples. The proposed chemical methodology produces samples with visually distinguishable reactions. Images are captured in a controlled lighting environment, and preprocessing techniques are applied to segment the Region of Interest (ROI). Each ROI is processed by a Convolutional Neural Network (CNN), which extracts highlevel descriptors. From each input sample, a total of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n \times n$</tex> CNN-based descriptors (one per pixel in the spatially aligned feature map) yield a high-dimensional representation of local sample characteristics. They are input into a Neural Network model. After training, the model can infer a predictive distribution for each input image. This allows for the computation of descriptive statistics that characterize the associated uncertainty of the model's predictions. Our approach is inspired by the Monte Carlo method: descriptors are treated as independent observations drawn from a latent feature distribution. Although they are deterministically extracted from activation maps, they result from random visual outcomes of the same underlying analytical parameter. Conversely, they are not independent. The proposed approach can be applied to several tasks, including quality assurance, process monitoring, and compliance verification. Monte Carlo method ; Feature (linguistics) ; Pattern recognition (psychology) ; Convolutional neural network ; Preprocessor ; Computation ; Inference ; Artificial neural network ; Pixel Machine Learning in Materials Science ; Spectroscopy and Chemometric Analyses ; Water Quality Monitoring Technologies
Authors: Min Zhang | Jiayue Liu | Yaozong Yang | Xian-Yu Wang | Zhihong Du | Hailei Zhao
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine-learning-assisted prediction of highly active and stable PrBaCo<sub>2</sub>O<sub>5+<i>δ</i></sub>-based oxygen electrodes for solid oxide cells A new highly active and stable PBC-based material was screened by a machine learning technique. The ABE and η descriptors are identified as important factors for the activity and stability of the PBC-based electrode, respectively. Oxide ; Oxygen ; Electrode ; Clark electrode ; Materials science ; Computer science ; Chemistry ; Physical chemistry ; Metallurgy ; Organic chemistry Advancements in Solid Oxide Fuel Cells ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Jane E. Joseph | Do Hoon Kiem | Sinchul Yeom | Mina Yoon
Source: ArXiv.org (preprint, 2025-11-29)
Field: Materials Science — Materials Chemistry

Interpretable Graph Neural Networks for Classifying Structure and Magnetism in Delafossite Compounds Delafossites (ABC2, where A and B are metals and C is a chalcogen) are a versatile family of quantum materials and layered oxides/chalcogenides whose properties are highly sensitive to atomic composition and stacking geometry. Their broad chemical tunability makes them an ideal platform for large-scale combinatorial exploration and high-throughput computational screening with desirable quantum properties. In this work, we employ a Concept Whitening Graph Neural Network, a gray-box AI model, to classify delafossite structures by stacking sequence and magnetic states. By aligning learned representations with human-interpretable physical concepts, this gray-box approach enables both accurate prediction and insight into the structural and chemical features driving magnetic behavior. The magnetic-ordering models achieved validation accuracies exceeding 80 percent, with a further slight uptick observed in the model incorporating the largest number of concepts. Concept alignment analysis revealed measurable learning across nine physically meaningful descriptors, with coefficients of determination ranging from approximately 0.6 for the d-shell valence-electron concepts to 0.4-0.5 for the magnetic coupling parameters. Furthermore, we mapped the concept importances onto the material graph representation, elucidating interpretable physical trends and the progression of stable concept-aligned regions across training. These results demonstrate the potential of interpretable graph-based learning to capture the underlying physics of complex materials systems and provide an interpretable framework for accelerating the discovery and understanding of delafossites and related crystalline materials. Delafossite ; Stacking ; Magnetism ; Graph ; Artificial neural network ; Quantum chemical ; Pattern recognition (psychology) ; Coupling (piping) Machine Learning in Materials Science ; Copper-based nanomaterials and applications ; Iron oxide chemistry and applications
Authors: Yagyank Srivastava | Amey G. Gokhale | Ankit Jain
Source: ArXiv.org (preprint, 2025-09-18)
Field: Materials Science — Materials Chemistry

Computational uncertainties in lattice thermal conductivity prediction of crystalline solids We report computational uncertainties in Boltzmann Transport Equation (BTE)-based lattice thermal conductivity prediction of 50 diverse semiconductors from the use of different BTE solvers (ShengBTE, Phono3Py, and in-house code) and interatomic forces. The interatomic forces are obtained either using the density functional theory (DFT) as implemented in packages Quantum Espresso and VASP employing commonly used exchange correlation functionals (PBE, LDA, PBEsol, and rSCAN) or using the pre-trained foundational machine learning forcefields trained on two different material datasets. We find that the considered BTE solvers introduce minimal uncertainties and, using the same interatomic force constants, all solvers result in an excellent agreement with each other, with a mean absolute percentage error (MAPE) of only 1%. While this error increases to around 10% with the use of different DFT packages, the error is still small and can be reduced further with the use of stringent planewave energy cutoffs. On the other hand, the differences in thermal conductivity due to the use of different exchange correlation functionals are large, with a MAPE of more than 20%. The currently available pre-trained foundational ML models predict the right trend for thermal conductivity, but the associated errors are high, limiting their applications for coarse screening of materials. Thermal conductivity ; Density functional theory ; Interatomic potential ; Limiting ; Computation ; Thermal ; Boltzmann equation ; Lattice (music) Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science