jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Emily H. Mang | Annie K. Barnett | Sicong He | James E. Nathaniel | Ryan Jacobs | Dane Morgan | Michael L. Falk | Jaime Marian | Mitra L. Taheri
Source: Journal of Nuclear Materials (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

Metastable grain boundary sink behavior revealed through deep-learning image analysis Grain boundary ; Crystallographic defect ; Dislocation ; Molecular dynamics ; Metastability ; Relaxation (psychology) ; Microstructure ; Work (physics) ; Observable Fusion materials and technologies ; Microstructure and mechanical properties ; Nuclear Materials and Properties
Authors: Mario Wolter
Source: LeoPARD - TU Braunschweig Publications And Research Data (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

An Ontology for Theoretical Chemistry Ontologies, as formal and structured representations of knowledge within a domain, have been instrumental in enabling data integration, knowledge sharing, and interoperability. For instance in chemistry experimental techniques, instrumentation, and chemical entities are codified in ontologies such as the Chemical Methods Ontology[1] and the Chemical Information Ontology[2]. Surprisingly, no comparably comprehensive ontology exists for theoretical chemistry. An ontology is the foundation of the necessary standardization for building reusable software libraries, workflow engines, and data repositories. Classes in software code could be directly derived from the ontology and therefore facilitate the modular construction of software components. Thereby greatly enhancing the efficiency and reproducibility of computational workflows. Creating this ontology, however, is intrinsically a community endeavour. Several partly overlapping projects already address fragments of theoretical chemistry or focus on specific software packages (e.g. OntoCompChem[3] or MolSSI QCSchema[4]). In the process of building the “comprehensive ontology” we should align with these existing vocabularies to maximise interoperability. For community inclusion an iterative, open development on collaborative platforms (e.g., GitHub) employing continuous integration should be enfostered. Early adoption can be catalysed by embedding identifiers into widely used computational packages (e.g., ORCA) and data repositories (e.g., Chemotion, NOMAD, QCArchive), thus ensuring immediate practical value. By uniting theoretical chemists around a shared semantic framework, the proposed ontology will accelerate method innovation, improve software sustainability, and unlock new possibilities for machine learning. [1] https://github.com/rsc-ontologies/rsc-cmo [2] Hastings J., Chepelev L., Willighagen E., Adams N., Steinbeck C., Dumontier M., PLoS One, 6(10):e25513 (2011) [3] Krdzavac N., Mosbach S., Nurkowski D., Buerger P., Akroyd J., Martin J., Menon A., Kraft M., J Chem Inf Model., 22;59(7):3154-3165 (2019) [4] https://molssi.org/software/qcschema-2/ &amp; https://github.com/MolSSI/QCSchema Ontology ; Workflow ; Process ontology ; Identifier ; Software ; Ontology-based data integration ; Process (computing) ; Semantic Web Machine Learning in Materials Science ; Scientific Computing and Data Management ; Semantic Web and Ontologies
Authors: Д. О. Филатов | Ivan V. Mikheev | Мikhail А. Proskurnin
Source: Diamond and Related Materials (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

Decoding the infrared spectrum of size-fractionated graphene oxide with machine learning Graphene ; Oxide ; Fingerprint (computing) ; Particle size ; Analytical Chemistry (journal) ; Infrared ; Feature (linguistics) ; Range (aeronautics) ; Fourier transform infrared spectroscopy Graphene research and applications ; Graphene and Nanomaterials Applications ; Carbon and Quantum Dots Applications
Authors: Hongliang Chang | Yanqing Jia | Yue Wang | Abdullah Alquwayzani | Hang Lu | Wenqing Niu | Heming Lin | Mohamed Ben Hassine | Tien Khee Ng | Boon S. Ooi
Source: IEEE Photonics Technology Letters (article, 2025-12-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

α-Ga <sub>2</sub> O <sub>3</sub> Molecular Beam Epitaxy for Photodetection and Authentication With the growing demand for secure and tamper-resistant solutions in optical communication and authentication, optoelectronic devices that can simultaneously perform high-performance photodetection and security functions are of particular significance. In this context, physical unclonable functions (PUFs) implemented using wide- and ultrawide-bandgap semiconductors are especially attractive, as they combine the ability to detect optical signals with the intrinsic randomness needed for robust anti-cloning capabilities. Exploring the inherent variation in UV-visible light optoelectronic devices for PUF implementation can be promising for applications that potentially need authentication and encryption. Herein, we present the first proof-of-concept demonstration of a machine learning assisted authentication scheme utilizing α-Ga2O3 based photodetector (PD), demonstrating its dual functionality, including photodetection and its potential toward PUF implementation. Transmission electron microscope analysis confirmed that the epitaxially grown α-Ga2O3 exhibits both single-crystalline and amorphous regions. This, together with the fabrication variance, serves to enhance the device authentication capability. The fabricated PD shows a distinct light-to-dark current response and a pronounced persistent photoconductivity effect, with a paired-pulse facilitation index of ~130%, attributed to the intrinsic defects in the α-Ga2O3. Furthermore, the photo-response of the fabricated PD under chopped 260-nm illumination at different voltage biases serves as digital fingerprints for authentication. By employing a convolutional neural network, the system achieves a high authentication accuracy of 99.58%. The seminal work lays the foundation for developing a full-scale device identification and authentication framework based on multiple challenge parameters, such as wavelength, pulse width, and beyond. Photodetection ; Photodetector ; Authentication (law) ; Photoconductivity ; Optical communication ; Physical unclonable function ; Photodiode ; Convolutional neural network ; Optical switch Ga2O3 and related materials ; GaN-based semiconductor devices and materials ; Semiconductor materials and devices
Authors: Richa Dubey | Ravi Prakash Dwivedi | Nilanjan Tewari | Velmathi Guruviah
Source: Array (article, 2025-08-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ensemble approach assisted grade capacitance prediction of biomass-derived electrode materials: New insights and implications for high performance supercapacitors Biomass derived carbon precursors have emerged as the promising candidates for electrode materials in supercapacitors. Beyond structural diversity, biomass-based materials offer various advantages of sustainability, biodegradability, heteroatom abundance, hierarchical structure along with eco friendliness. Activated carbon electrode synthesis and operational parameters play significant role in affecting the electrochemical performance of biomass-based supercapacitors. In the current research, grade prediction of specific capacitance for biomass derived activated carbon (BDAC) supercapacitor was performed using ten ensemble approaches. Proposed models M2, M9 and M10 showed highest kappa values of 0.996, 0.977 and 0.981 respectively and lowest RMSE values of 0.179, 0.212 and 0.201 respectively by selecting 14 top rank features on the basis of chemical activation, electrode preparation procedures, structural parameters and electrode operational parameters. Further, sensitivity and specificity parameters were evaluated to provide effectiveness in predicting the actual capacitance values. Ensemble approach facilitated to improve the overall prediction accuracy by capturing a more explicit understanding of the considered dataset. Principal Component Analysis and Attribute polarization analysis showed prominent effect of activation ratio, conductive material ratio and the gas adsorption parameters (V 0.1 -V 0.9 ) on biomass-based supercapacitors. This multi-data approach supported in attaining cognizance of biomass derived materials contributing to electrode fabrication for achieving higher capacitance. ➢ 10 ensemble machine learning approach and 14 principal variables specifically: chemical activation, principal structural features, electrode preparation and operational parameters provided better accuracy. ➢ Proposed models M2 (RF+AB), M9 (RF+Bagging) and M10 (RF+CVR) predicted capacitance accurately shown by highest kappa values of 0.996, 0.977 and 0.981 respectively and lowest RMSE values of 0.179, 0.212 and 0.201 respectively. ➢ Impact of synthesizing and modifying parameters on performance of biomass-based supercapacitors was carried out ➢ Grade prediction was performed by classifying the extracted 400 specific capacitance values into four grades. ➢ Attribute prioritization analysis showed prominent effect of activation ratio, conductive material ratio and the gas adsorption parameters (V 0.1 -V 0.9 ) on biomass-based supercapacitors. Supercapacitor ; Capacitance ; Materials science ; Electrode ; Biomass (ecology) ; Nanotechnology ; Environmental science ; Chemistry ; Ecology ; Biology Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Arash Vaghef‐Koodehi
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Microstructural Engineering and Advanced Conduction Mechanisms in ZnO-Based Varistors: From Atomic Interfaces to Device Performance Varistor ; Materials science ; Optoelectronics ; Engineering physics ; Thermal conduction ; Nanotechnology ; Composite material ; Electrical engineering ; Engineering ; Voltage Electronic and Structural Properties of Oxides ; ZnO doping and properties ; Nanowire Synthesis and Applications
Authors: Guosong Zhu | Sen Yang | Wenqian Pan | Wei Zhou | Heng Luo | Yue Hu | Xiaomeng Fan | Li Yang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electromagnetic parameter prediction and multi-band absorption design of ZnO@YCO composite materials based on an SSA-GRNN-PSO data-driven framework Particle swarm optimization ; Absorption (acoustics) ; Inverse ; Inverse problem ; Electromagnetics ; Artificial neural network ; Coupling (piping) ; Electromagnetic radiation Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies
Authors: Mohsen Dehbashi | Jerzy Bodzenta | Anna Kaźmierczak-Bałata
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

A Hybrid Approach Combining Machine Learning and Classical Methods for Precise Electrical Conductivity Estimation in Thin Films Electrical resistivity and conductivity ; Thin film ; Computer science ; Materials science ; Artificial intelligence ; Machine learning ; Engineering ; Electrical engineering ; Nanotechnology Machine Learning in Materials Science
Authors: Aurore Jullin | Nicolas Hascoët | Francisco Chinesta | Éliane Espuche
Source: Food Packaging and Shelf Life (article, 2025-03-24)
Field: Materials Science — Biomaterials

Data driven modelling as a new route to design PLA based materials with improved barrier properties Materials science ; Nanotechnology ; Computer science biodegradable polymer synthesis and properties ; Additive Manufacturing and 3D Printing Technologies ; Manufacturing Process and Optimization
Authors: Mario Wolter
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

(Towards) An Ontology for Theoretical Chemistry Presentation given at the Ontologies4Chem Workshop 2025. Abstract:Surprisingly, no comprehensive ontology exists for theoretical chemistry. Several partly overlapping projects already address fragments of theoretical chemistry or focus on specific software packages (e.g. OntoCompChem or MolSSI QCSchema). In the process of building a comprehensive ontology for theoretical chemistry we should align with these existing vocabularies to maximise interoperability. Early adoption can be catalysed by embedding identifiers into widely used computational packages (e.g., ORCA) and data repositories (e.g., Chemotion, NOMAD, QCArchive), thus ensuring immediate practical value. By uniting theoretical chemists around a shared semantic framework, the proposed ontology will accelerate method innovation, improve software sustainability, and unlock new possibilities for machine learning. Ontology ; Presentation (obstetrics) ; Software ; Process ontology ; Focus (optics) ; Process (computing) ; Identifier ; Ontology-based data integration Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Biomedical Text Mining and Ontologies
Authors: Kazuaki Tokuyama | Souta Miyamoto | Taichi Masuda | Katsuaki Tanabe
Source: ArXiv.org (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

Composition-Based Machine Learning for Screening Superconducting Ternary Hydrides from a Curated Dataset We present an ensemble machine-learning approach for composition-based, structure-agnostic screening of candidate superconductors among ternary hydrides under high pressure. Hydrogen-rich hydrides are known to exhibit high superconducting transition temperatures, and ternary or multinary hydrides can stabilize superconducting phases at reduced pressures through chemical compression. To systematically explore this vast compositional space, we construct an ensemble of 30 XGBoost regression models trained on a curated dataset of approximately 2000 binary and ternary hydride entries. The model ensemble is used to screen a broad set of A-B-H compositions at pressures of 100, 200, and 300 GPa, with screening outcomes evaluated statistically based on prediction consistency across ensemble members. This analysis highlights several high-scoring compositional systems, including Ca-Ti-H, Li-K-H, and Na-Mg-H, which were not explicitly included in the training dataset. In addition, feature-importance analysis indicates that elemental properties such as ionization energy and atomic radius contribute significantly to the learned composition-level trends in superconducting transition temperature. Overall, these results demonstrate the utility of ensemble-based machine learning as a primary screening tool for identifying promising regions of chemical space in superconducting hydrides. Ternary operation ; Superconductivity ; Consistency (knowledge bases) ; Binary number ; Set (abstract data type) ; Ensemble learning ; Construct (python library) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Hydrogen Storage and Materials
Authors: Tigany Zarrouk | Miguel A. Caro
Source: ArXiv.org (preprint, 2025-08-23)
Field: Chemistry — Physical and Theoretical Chemistry

Molecular augmented dynamics: Generating experimentally consistent atomistic structures by design A fundamental objective of materials modeling is identifying atomic structures that align with experimental observables. Conventional approaches for disordered materials involve sampling from thermodynamic ensembles and hoping for an experimental match. This process is inefficient and offers no guarantee of success. We present a method based on modified molecular dynamics, that we call molecular augmented dynamics (MAD), which identifies structures that simultaneously match multiple experimental observables and exhibit low energies as described by a machine learning interatomic potential (MLP) trained from ab-initio data. We demonstrate its feasibility by finding representative structures of glassy carbon, nanoporous carbon, ta-C, a-C:D and a-CO$_x$ that match their respective experimental observables -- X-ray diffraction, neutron diffraction, pair distribution function and X-ray photoelectron spectroscopy data -- using the same initial structure and underlying MLP. The method is general, accepting any experimental observable whose simulated counterpart can be cast as a function of differentiable atomic descriptors. This method enables a computational "microscope" into experimental structures. Observable ; Molecular dynamics ; Function (biology) ; Experimental data ; Nanoporous ; Embedded atom model ; Process (computing) ; Sampling (signal processing) Various Chemistry Research Topics
Authors: Junxiang Chen | Tao Wang
Source: ChemRxiv (article, 2025-08-30)
Field: Materials Science — Materials Chemistry

DPχ: Constant-Potential Machine-Learning Potentials for Large-Scale Electrocatalysis We introduce DPχ, a machine-learning potential that enables electrocatalysis simulations at spatiotemporal scales 10^3-10^4 beyond AIMD while retaining near first-principles accuracy. DPχ adopts a Bader-basin-centroid (BC) representation and explicitly decomposes interfacial charge into a neural-predicted chemical component and a conductor component confined to metal sites. The conductor charges are determined on-the-fly by a Siepmann-Sprik-type polarizable-electrode model under an electroneutrality constraint, which adaptively apportions charge between electrodes and yields constant-potential dynamics without imposing the Fermi level. Replacing Wannier-center sites with BCs preserves the far-field accuracy of Wannier-based schemes while permitting fractional charge transfer across electrified metal-water interfaces. On Pt(111)-H2O, DPχ reproduces DFT-level forces, electrode potential, hydrogen-evolution barriers, and vibrational spectra, opening a route to predictive electrocatalysis under realistic conditions. Electrocatalyst ; Constant (computer programming) ; Scale (ratio) ; Computer science ; Artificial intelligence ; Chemistry ; Geography ; Physical chemistry ; Cartography ; Electrochemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Mario Wolter
Source: Open MIND (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

(Towards) An Ontology for Theoretical Chemistry Presentation given at the Ontologies4Chem Workshop 2025. Abstract:Surprisingly, no comprehensive ontology exists for theoretical chemistry. Several partly overlapping projects already address fragments of theoretical chemistry or focus on specific software packages (e.g. OntoCompChem or MolSSI QCSchema). In the process of building a comprehensive ontology for theoretical chemistry we should align with these existing vocabularies to maximise interoperability. Early adoption can be catalysed by embedding identifiers into widely used computational packages (e.g., ORCA) and data repositories (e.g., Chemotion, NOMAD, QCArchive), thus ensuring immediate practical value. By uniting theoretical chemists around a shared semantic framework, the proposed ontology will accelerate method innovation, improve software sustainability, and unlock new possibilities for machine learning. Ontology ; Presentation (obstetrics) ; Software ; Process ontology ; Focus (optics) ; Process (computing) ; Identifier ; Ontology-based data integration Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Biomedical Text Mining and Ontologies
Authors: E. Rodríguez | Luis Eduardo Castrillón Díaz | Stephen Hernández
Source: ArXiv.org (preprint, 2025-02-27)
Field: Materials Science — Materials Chemistry

Phase Diagrams Construction Using Mean-Field Renormalization and Neural Network Fitting Employing the mean-field renormalization group (MFRG) method, we analyzed the $Fe_p Mn_{0.6-p}Al_{0.4}$ and $Fe_{p}Al_{1-p}$ alloys, incorporating second-neighbor interactions in the latter for the first time within this framework. Our analysis utilized neural network fitting, yielding promising results in both the phase diagram adjustments and the estimation of bond energies. Artificial neural network ; Renormalization group ; Phase diagram ; Phase (matter) ; Renormalization ; Diagram Machine Learning in Materials Science ; Magnetic Properties and Applications ; Model Reduction and Neural Networks
Authors: Anita Donlic | Troy J. Comi | Sofia A. Quinodoz | Nima Jaberi-Lashkari | Krist Helen Antunes | Lifei Jiang | Lennard W Wiesner | Ai Ing Lim | Clifford P. Brangwynne
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-08-19)
Field: Materials Science — Materials Chemistry

Deep Learning of Functional Perturbations from Condensate Morphology Abstract Biomolecular condensates compartmentalize the interior of living cells to spatiotemporally organize complex functions, yet linking molecular interactions within condensates to their mesoscale organization remains a major challenge. To bridge this gap, we developed a neural network-based framework - Deep-Phase - that uses microscopy images to quantitatively classify condensate morphology changes resulting from pharmacological alterations in associated biochemical processes. We use Deep-Phase to precisely quantify time- and concentration-dependent structural perturbations to the multiphase nucleolus and show that they are tightly coupled to potencies of drugs inhibiting rRNA transcription and processing. Applying Deep-Phase in a chemical screen, we identify a unique nucleolar morphology and discover a role for a DNA topoisomerase in rRNA processing. Mechanistic studies of this morphology provide insights into how interfaces between nucleolar subcompartments are maintained. We demonstrate Deep-Phase’s adaptability to diverse cell lines, labels, and condensates, offering a powerful platform for uncovering cellular organizing principles and therapeutic targets. Highlights A deep learning framework, Deep-Phase, classifies and quantifies drug-induced changes in morphologies of nucleoli, nucleolar speckles, and viral cytoplasmic condensates, directly from images. Time- and concentration-dependent morphological responses to perturbation predict associated disruptions in RNA transcription and processing. Using Deep-Phase in a high-content small molecule screen reveals a unique nucleolar morphology induced by TOP1 inhibition. TOP1 inhibition leads to reduced levels and processing of large ribosomal subunit precursors and provides a mechanism for maintenance of nucleolar phase boundaries. Morphology (biology) ; Artificial intelligence ; Chemistry ; Computer science ; Biological system ; Biology ; Zoology Machine Learning in Materials Science
Authors: Lokendra Thakur | Gurpreet Bharj | Manish Saroya
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-09-19)
Field: Materials Science — Biomaterials

Interpretable Machine Learning and Comparative Genomics Reveal Microbial Plastic-Degrading (Microbeyt) Potential Abstract Plastic pollution poses a critical environmental threat, and microbial enzymes represent a sustainable strategy for polymer degradation. We present a computational pipeline that integrates orthogroup-based genomic analysis with machine learning and interpretable feature importance to identify microbial strains with high plastic-degrading potential. Using presence or absence matrices and SHAP-derived feature contributions to the MTP visualization, the workflow highlights conserved gene modules driving predictive classification. Application to a single genus revealed strains harboring versatile enzymatic repertoires capable of targeting diverse polymers, including polyethylene, polyethylene terephthalate, polyurethane, and polyhydroxyalkanoates. These findings provide a rational framework for prioritizing candidate strains for experimental validation and bioremediation strategies. Overall, this study demonstrates how integrating comparative genomics with interpretable machine learning can guide the systematic discovery of microbial solutions to plastic pollution. Workflow ; Pipeline (software) ; Feature (linguistics) ; Genomics ; Comparative genomics ; Metagenomics biodegradable polymer synthesis and properties