jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Asuna Hagiya | Tetsuo Asakura | T. Sekiya | T. Kataoka | Keiji Komatsu | Keisuke Sato
Source: Journal of Magnetism and Magnetic Materials (article, 2025-08-20)
Field: Materials Science — Materials Chemistry

Thallium substitution effects on Co valence states and room-temperature ferromagnetism in co-doped In2O3: Experimental and machine-learning insights • Tl substitution in In2O3:Co increases the Co3+ fraction before annealing. • Vacuum annealing partly reduces Co3+ to Co2+, but higher Tl weakens ferromagnetism. • RT ferromagnetism appears after annealing for x=0.0 and diminishes with Tl content. • Ridge‑regression model: larger ionic radius favors Co3+, high ionization energy suppresses RTF. • ML suggests Pr, Lu, Ce, Nd as promising trivalent dopants for stronger room‑temperature magnetism. We systematically investigate the effects of thallium (Tl) substitution in Co-doped indium oxide ( I n 1.9 - x T l x C o 0.1 O 3 ), focusing on its influence on Co valence states and magnetic properties. X-ray diffraction (XRD) confirmed that the bixbyite structure was preserved across a wide Tl composition range ( x = 0.0–0.6). X-ray photoelectron spectroscopy (XPS) revealed that Tl incorporation increased the C o 3 + / C o 2 + ratio before annealing. Vacuum annealing induced partial reduction of C o 3 + to C o 2 + , but higher Tl substitution led to weaker enhancement of ferromagnetic order. Magnetization measurements showed that the pristine ( x = 0.0) composition transformed from a paramagnetic to a room-temperature ferromagnetic state upon annealing, while higher Tl content systematically weakened ferromagnetism. These results suggest that the larger ionic radius of T l 3 + modifies crystal-field environments and magnetic exchange interactions, promoting C o 3 + formation yet suppressing long-range ferromagnetism. A machine-learning model using ridge regression predicted the C o 3 + ratio and room-temperature ferromagnetism (RTF) based on ionic radius and ionization energy with high accuracy (R 2 ≥ 0.85). Predictions for hypothetical dopants in I n 1.6 M 0.3 C o 0.1 O 3 suggested rare-earth elements such as Pr, Lu, Ce and Nd could enhance C o 3 + content and ferromagnetic properties. This study highlights how ionic size and ionization energy influence valence states and magnetism in I n 2 O 3 -based diluted magnetic semiconductors, demonstrating the synergy of experiments and machine learning in spintronics material design. Valence (chemistry) ; Materials science ; Ferromagnetism ; Thallium ; Doping ; Substitution (logic) ; Condensed matter physics ; Substitution effect ; Computer science ; Optoelectronics ; Metallurgy ; Chemistry ; Physics ZnO doping and properties ; Gas Sensing Nanomaterials and Sensors ; Magnetic and transport properties of perovskites and related materials
Authors: Lucas
W. Souza (17615056) | Nathan D. Ricke (4510837) | Braden C. Chaffin (18601646) | Mike E. Fortunato (21407145) | Shutian Jiang (11910216) | Cihan Soylu (12915842) | Thomas C. Caya (1325148) | Sii Hong Lau (4456573) | Katherine A. Wieser (21407148) | Abigail G. Doyle (1318335) | Kian L. Tan (1323000)
Source: Figshare (article, 2025-05-22)
Field: Materials Science — Materials Chemistry

ApplyingActive Learning toward Building a GeneralizableModel for Ni-Photoredox Cross-Electrophile Coupling of Aryl and AlkylBromides When developing machine learning models for yield prediction, the two main challenges are effectively exploring condition space and substrate space. In this article, we disclose an approach for mapping the substrate space for Ni/photoredox-catalyzed cross-electrophile coupling of alkyl bromides and aryl bromides in a high-throughput experimentation (HTE) context. This model employs active learning (in particular, uncertainty querying) as a strategy to rapidly construct a yield model. Given the vastness of substrate space, we focused on an approach that builds an initial model and then uses a minimal data set to expand into new chemical spaces. In particular, we built a model for a virtual space of 22,240 compounds using less than 400 data points. We demonstrated that the model can be expanded to 33,312 compounds by adding information around 24 building blocks (<100 additional reactions). Comparing the active learning-based model to one constructed on randomly selected data showed that the active learning model was significantly better at predicting which reactions will be successful. A combination of density function theory (DFT) and difference Morgan fingerprints was employed to construct the random forest model. Feature importance analysis indicates that key DFT features that are related to the reaction mechanism (e.g., alkyl radical LUMO energy) were crucial for model performance and predictions on aryl bromides outside the training set. We anticipate that combining DFT featurization and uncertainty-based querying will help the synthetic organic community build predictive models in a data-efficient manner for other chemical reactions that feature large and diverse scopes. Yield (engineering) ; Construct (python library) ; Chemical space ; Coupling (piping) ; Function (biology) ; Set (abstract data type) ; Substrate (aquarium) ; Alkyl ; Aryl Machine Learning in Materials Science ; Radical Photochemical Reactions ; CO2 Reduction Techniques and Catalysts
Authors: Bin Cao | Yang Liu | Longhan Zhang | Yifan Wu | Zhixun Li | Ya-Qiao Luo | Hong Cheng | Yang Ren | Tong‐Yi Zhang
Source: ArXiv.org (preprint, 2025-09-26)
Field: Materials Science — Materials Chemistry

Beyond Structure: Invariant Crystal Property Prediction with Pseudo-Particle Ray Diffraction Crystal property prediction, governed by quantum mechanical principles, is computationally prohibitive to solve exactly for large many-body systems using traditional density functional theory. While machine learning models have emerged as efficient approximations for large-scale applications, their performance is strongly influenced by the choice of atomic representation. Although modern graph-based approaches have progressively incorporated more structural information, they often fail to capture long-range atomic interactions due to finite receptive fields and local encoding schemes. This limitation leads to distinct crystals being mapped to identical representations, hindering accurate property prediction. To address this, we introduce PRDNet that leverages unique reciprocal-space diffraction besides graph representations. To enhance sensitivity to elemental and environmental variations, we employ a data-driven pseudo-particle to generate a synthetic diffraction pattern. PRDNet ensures full invariance to crystallographic symmetries. Extensive experiments are conducted on Materials Project, JARVIS-DFT, and MatBench, demonstrating that the proposed model achieves state-of-the-art performance. The code is openly available at https://github.com/Bin-Cao/PRDNet. Property (philosophy) ; Diffraction ; Encoding (memory) ; Invariant (physics) ; Code (set theory) ; X-ray crystallography ; Crystal (programming language) ; Graph Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Enzyme Structure and Function
Authors: Rinske M. Alkemade | Rastko Sknepnek | Frank Smallenburg | Laura Filion
Source: ArXiv.org (preprint, 2025-02-26)
Field: Materials Science — Materials Chemistry

Data package for the paper "Machine learning short-ranged many-body interactions in colloidal systems using descriptors based on Voronoi cells" Data package accompanying the publication "Machine learning short-ranged many-body interactions in colloidal systems using descriptors based on Voronoi cells". It provides all the necessary resources to reproduce the results and analyses presented in the study, which introduces a machine learning (ML) strategy for accurately modeling highly local many-body interactions in colloidal systems. Specifically, for a two-dimensional system consisting of polymers and colloids, we developed a Voronoi-based description of the system and demonstrated that it accurately captures the many-body nature of the system. This data package contains all relevant codes, data and analysis notebooks te reproduce the results found in the paper. The package has the following structure: [FIGURES] contains all figures from the paper, along with the relevant analysis notebooks, Adobe Illustrator files, and additional data to generate the figures. [CODES] contains the scripts to generate the training data, to train the machine learning models, and to run both the reference, brute force system and the machine learning system. [DATA] contains the training data, the pre-trained models, and simulation output from both the brute force system an d the machine learned system. Each directory contains a README.txt file that describes the content of the directory. Scripting language ; Voronoi diagram ; Automation ; Complex system ; Supervised learning ; Data modeling ; R package Machine Learning in Materials Science ; Material Dynamics and Properties ; Advanced Physical and Chemical Molecular Interactions
Authors: Yan‐Peng Feng | Mei‐Xiong Zhu | Ru‐Jian Jiang | Yu‐Jia Wang | Yun‐Long Tang | Yin‐Lian Zhu | Xiu‐Liang Ma
Source: Advanced Science (article, 2025-12-21)
Field: Materials Science — Materials Chemistry

Direct Observation of Electric‐Field‐Driven Phase Transitions Associated with Energy Storage in Antiferroelectric Films Antiferroelectric materials are promising candidates for high-energy-density capacitors due to their reversible electric-field-induced phase transitions. However, the atomic-scale mechanism underlying the electric-field-driven antiferroelectric-to-ferroelectric (AFE-to-FE) transition, particularly in relation to high-performance energy storage, remains elusive. Here, we employ in situ aberration-corrected scanning transmission electron microscopy (STEM) to directly visualize the electric-field-driven AFE-to-FE transition in epitaxial PbZrO<sub>3</sub> (PZO) thin films. We reveal a sequential electric-field-driven transition pathway involving an intermediate orthorhombic ferrielectric phase (FiE<sub>O</sub>) and a monoclinic ferroelectric phase (FE<sub>M</sub>), ultimately stabilizing into a rhombohedral ferroelectric structure (FE<sub>R</sub>). This transformation is accompanied by a continuous reduction in the polarization modulation period, indicating enhanced dipole-dipole interaction coupling, which may improve their energy storage performance. Our experimental findings are corroborated by machine learning molecular dynamics simulations, providing quantitative insights into the structural evolution. This work illustrated the relationship between microscopic phase evolution dynamics and macroscopic energy storage behavior, offering a powerful strategy for the design and optimization of next-generation antiferroelectric energy storage materials. Antiferroelectricity ; Ferroelectricity ; Phase transition ; Orthorhombic crystal system ; Monoclinic crystal system ; Phase (matter) ; Polarization (electrochemistry) ; Transmission electron microscopy Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices ; Dielectric materials and actuators
Authors: Abhijeet J. Kale | Sanjeev S. Navaratna | Pratik Sahu | Henry Chan | B. R. K. Nanda | Rohit Batra
Source: arXiv (Cornell University) (preprint, 2025-12-24)
Field: Materials Science — Materials Chemistry

Active Learning Guided Computational Discovery of 2D Materials with Large Spin Hall Conductivity Two-dimensional (2D) materials are promising candidates for next-generation spintronic devices due to their tunable properties and potential for efficient spin-charge interconversion. However, discovering materials with intrinsically high spin Hall conductivity (SHC) is hindered by the vast chemical space and expensive nature of conventional experimental and first-principles methods. In this work, we employ an active learning framework to accelerate the discovery of high-SHC 2D materials. Machine learning (ML) models were trained on SHC values computed from density functional theory calculations, incorporating the Kubo formalism via tight-binding Hamiltonians constructed from maximally localized Wannier functions, with explicit treatment of spin-orbit coupling. Starting from random but chemically diverse 24 2D systems, the dataset was expanded to 41 cases (from an overall pool of around 2000 materials) over three active learning loops using an expected improvement acquisition strategy. The ML technique successfully identified several high SHC candidates with the best candidate exhibiting a SHC of 271.52 (hbar/e) Ohm^-1, nearly 23 times higher than the top performer in the initial round. Beyond candidate discovery, several features such as orbital symmetry near the Fermi energy, types of atomic species, material composition, covalent radii, and electronegativity of constituent atoms were found to play critical role in shaping the spin Hall response in 2D systems. The data generated is made publicly available to facilitate further advances in 2D spintronics. Spintronics ; Wannier function ; Spin (aerodynamics) ; Density functional theory ; Spin Hall effect ; Formalism (music) ; Electronegativity ; Spin density Machine Learning in Materials Science ; 2D Materials and Applications ; Topological Materials and Phenomena
Authors: Cindy Trinh (22573197) | Maria Prokopidou (22573200) | Konstantinos Chatzilygeroudis (13946142) | Dimitrios Meimaroglou (22573203)
Source: Figshare (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

A Novel HybridModeling Framework for Polymer ReactionEngineering with Application to Real-Time Process Monitoring Hybrid modeling frameworks that integrate knowledge-driven and data-driven approaches offer a promising solution for modeling complex physical systems, especially when data are limited and domain knowledge is well established. In this work, a hybrid modeling strategy is proposed, coupling Gaussian Processes with a kinetic polymerization model for the case of the free-radical polymerization of styrene. The kinetic model serves as the mean function of the Gaussian Process prior embedding fundamental system knowledge into the regression process. The proposed framework enables the model to deliver physically consistent predictions in the absence of data while allowing fast adaptation when new data becomes available. This is demonstrated in a real-time process monitoring context, where data points arrive sequentially and the model updates its predictions accordingly, while avoiding the need for extensive training, typically required by DD models, such as neural networks. The approach is validated using two data sets across a wide range of reaction temperatures (85–150 °C), showcasing its performance under varying degrees of model-data agreement. The results highlight the framework’s ability to learn from limited data, maintain physical plausibility through constrained predictions, and incorporate uncertainty for improved robustness. This study, through a simple example, lays the groundwork for broader applications of Gaussian-Process-based hybrid models in polymerization systems, with future work aimed at extending their scope to additional process variables and constraint handling strategies. Gaussian process ; Embedding ; Process (computing) ; Hybrid system ; Process modeling ; Constraint (computer-aided design) ; Domain (mathematical analysis) ; Range (aeronautics) ; Coupling (piping) Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Model Reduction and Neural Networks
Authors: Allison Niu | Christopher Kucha | Ebenezer O. Olaniyi | Priyanka Dahiya | Arthur Young
Source: Food and Humanity (article, 2025-11-21)
Field: Chemistry — Analytical Chemistry

Vibrational spectroscopy and machine learning in bakery production: Toward digital twin integration Bakery processing is a complex, multi-stage process that involves selecting raw materials, mixing, proofing, and baking. These stages encompass numerous interactions between formulation components and fermentative microorganisms, which critically influence the chemical and physical properties of the dough matrix and the quality of the final product. Advancing the understanding of these dynamic processes requires systematic, real-time monitoring. Conventional quality assurance methods are often destructive, labor-intensive, and time-consuming. Vibrational spectroscopic techniques are emerging as rapid, cost-effective, and non-destructive alternatives for process and quality control across bakery production stages. This review synthesizes recent advances in the application of vibrational spectroscopic techniques, combined with machine learning, for quality assessment of various parameters throughout the breadmaking process. The discussion covers spectroscopy principles, modeling, with particular emphasis on near-infrared (NIR) spectroscopy and hyperspectral imaging (HSI). The concept of a digital twin is introduced as a future direction for intelligent bakery systems. A digital twin driven by vibrational spectroscopy and machine learning could enable real-time simulation, adaptive control, and cross-stage coordination. Future research should focus on developing integrated sensing and modeling platforms that support predictive and resource-efficient bakery processing aligned with Industry 4.0 principles. Quality (philosophy) ; Process (computing) ; Quality assurance ; Hyperspectral imaging ; Spectroscopy ; Process analytical technology Spectroscopy and Chemometric Analyses ; Listeria monocytogenes in Food Safety ; Food Drying and Modeling
Authors: Sang-su Na | Soobin Park | Y G Jung | J. H. Park | Jimin Hong | Ji‐Hyun Lee | Albert S. Kim | B. H. KIM | Gyoujin Cho | Eunju Cha | Seung Hyun Song
Source: Advanced Science (article, 2025-12-25)
Field: Materials Science — Materials Chemistry

Unpaired Learning‐Enabled Nanotube Identification from AFM Images Nanotubes, particularly single-walled carbon nanotubes (SWCNTs), represent an important class of materials with valuable electrical, mechanical, and thermal properties. However, accurate characterization of nanotube network morphologies remains a significant challenge. We present a deep learning-based approach for extracting nanotube morphologies from atomic force microscopy (AFM) images utilizing an image-to-image (I2I) translation framework based on cycleGAN with a specialized loss function that learns to transform AFM images containing nanotubes to images of pure substrates. By subtracting these translated substrate images from the original AFM images, we effectively isolated nanotube morphologies even on substrates with roughness exceeding the nanotube diameter. We validate our approach through physics-based simulation studies and application to roll-to-roll printed carbon nanotube transistors on flexible polymeric substrates.Our method outperforms tranditional image processing and supervised learning models in sensitivity and accuracy for CNT network extraction. This improved characterization capability provides useful insights for optimizing the fabrication processes of CNT-TFTs, supporting their development for flexible electronic applications. The methodology extends beyond carbon nanotubes to other nanomaterial-based electronic devices, with future work aimed at expanding the model's generalization and integrating with real-time AFM imaging. Carbon nanotube ; Nanotube ; Characterization (materials science) ; Fabrication ; Substrate (aquarium) ; Atomic force microscopy ; Surface finish ; Surface roughness Carbon Nanotubes in Composites ; Force Microscopy Techniques and Applications ; Nanofabrication and Lithography Techniques
Authors: Kai Qian | Lingyang Meng | Ji Li | Jinze Lyu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Neural Network-Like Ceo2-Loaded Polyvinylidene Fluoride Membrane for Tri-Phase Interface Catalytic Ozonation of Antibiotics Polyvinylidene fluoride ; Catalysis ; Membrane ; Fluoride ; Phase (matter) ; Antibiotics ; Interface (matter) ; Chemical engineering ; Materials science ; Chemistry ; Organic chemistry ; Adsorption ; Inorganic chemistry ; Engineering ; Biochemistry Catalytic Processes in Materials Science
Authors: Salah Ud Din | Chen  UD Peng | Muhammad Kamran Shereen | Sufaid Shah | Liu xianlong | Matin Ashurov | Zecheng Ren | Ji‐Zhao Liang
Source: Journal of Materials Chemistry A (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

The role of AI in the advancement of 2D materials for biosensing and nanoelectronics Significant advancements in biosensing and nanoelectronics have been driven by 2D materials like graphene, MoS 2 , and hBN, integrated with artificial intelligence (AI) and machine learning (ML). Nanoelectronics ; Biosensor ; Key (lock) ; Applications of artificial intelligence ; Nanobiotechnology 2D Materials and Applications ; Graphene research and applications ; Graphene and Nanomaterials Applications
Authors: Xian-Deng Qiu | Hao Tang | Rong Wang
Source: Chinese Journal of Polymer Science (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

Block Copolymer Aided Controllable Design of Colloidal Molecules by DNA-programmable Assembly Rational design ; Copolymer ; Molecule ; Nanomaterials ; Colloid ; Nanoparticle ; Block (permutation group theory) ; Phase (matter) Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization ; Pickering emulsions and particle stabilization
Authors: Alexandros Angelis | Marianna Kotzabasaki | Konstantina Filippou | Chrysanthos Maraveas
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-12-17)
Field: Materials Science — Biomaterials

D2.5 ANIPH AI predictive tool The deliverable D2.5: “ANIPH AI Predictive Tool” presents the development, validation, and deployment of a suite of machine learning (ML) models designed to predict thermal, rheological, biodegradation, cytotoxicity and ecotoxicity properties of polyhydroxyalkanoate (PHA)-based polymers within the ANIPH project. *This deliverable has not yet been officially approved by the European Commission and should be considered a draft. Deliverable ; Software deployment ; Suite ; European commission ; Predictive modelling ; Software biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Polymer crystallization and properties