jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Julian
A. Hueffel (21743274) | Quentin P. Bindschaedler (19193309) | Francesco Sala (483023) | Franziska Schoenebeck (1266411)
Source: Figshare (article, 2025-09-02)
Field: Materials Science — Materials Chemistry

Empowering ReactivityPredictions through Noise-BasedData Augmentation Data scarcity is a key obstacle in the pursuit to capitalize on the predictive powers of the A.I. in problems related to bond-making and -breaking at the molecular level. While the generation of artificial data from real data points (known as “data augmentation”) is a widely pursued strategy employed in, for example, image or speech recognition or health data evaluations, among others, to artificially expand existing data sets, it is currently unknown whether this strategy is applicable to reactivity problems at the molecular level, where predictive models are exquisitely sensitive to steric, electronic, and structural nuances. Here, we systematically evaluated the power of data augmentation for a diverse set of reactivity questions ranging from the prediction of activation barriers to stereoselectivities of catalytic transformations. We demonstrate that introducing Gaussian noise to existing data points, which is completed in under a second for a full data set, can dramatically enhance the predictive performance. It can enable model training in low-data regimes where otherwise no meaningful model could be built and achieves accuracy comparable to models built on full data sets, while needing only a fraction of the data. The approach substantially lowers the number of necessary experiments (by 20–50%), conserving time, energy, and resources, while advancing the integration of machine learning in molecular reactivity challenges. Training set ; Predictive power ; Noise (video) ; Set (abstract data type) ; Data set ; Key (lock) ; Reactivity (psychology) ; Data modeling ; Synthetic data Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Computational Drug Discovery Methods
Authors: Jesús Fernando Franco-López | Mireya S. García-Vázquez | Alejandro Álvaro Ramírez‐Acosta
Source:  (article, 2025-09-16)
Field: Materials Science — Materials Chemistry

Failure prediction in advanced materials using unsupervised translation with inheritance from the microscopic images target domains Unsupervised image-to-image translation emerges as a revolutionary tool in materials science, particularly for studying damage and fracture phenomena without destructive experimentation. In this work, we present a framework based on CycleGAN enhanced with attention mechanisms, specifically designed to translate images of tantalum carbide (TaC) obtained through focused ion beam scanning electron microscopy (FIB-SEM), a material renowned for its thermal and mechanical resistance, from one state to another with simulated structural damage. Our method generates realistic representations of cracks, fissures, and fracture patterns directly from microscopic images, preserving the base microstructure while adapting textures and morphologies to the "damaged" domain. The architecture incorporates attention modules that identify and highlight critical regions prone to failure, demonstrating that Cycle-GAN significantly improves the quality and physical coherence of translated images. This enables the simulation of material behavior under extreme conditions, such as mechanical loads or fatigue cycles, which are crucial factors in high-demand applications like aerospace, nuclear, and advanced engineering industries. By eliminating the need for repetitive destructive testing, this approach not only preserves valuable samples but also allows for the exploration of hypothetical damage and failure scenarios without altering the original material. Furthermore, by simulating phenomena such as crack propagation, the model facilitates the identification of weak points in the microstructure, thereby optimizing the design of stronger and more efficient materials. The adaptability of the framework makes it a versatile tool for analyzing a wide range of material systems and microscopy techniques, solidifying the role of machine learning as a key tool in advanced materials characterization. This work establishes a paradigm for data-driven simulation, integrating artificial intelligence with advanced microscopy to accelerate the development of materials optimized for critical applications. It represents a crucial step toward a predictive, sustainable, and globally oriented materials science, aimed at addressing pressing technological challenges. Translation (biology) ; Identification (biology) ; Fracture (geology) ; Coherence (philosophical gambling strategy) ; Proof of concept ; Tantalum carbide ; Adaptability ; Range (aeronautics) Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection
Authors: Romakanta Bhattarai | Peter Minch | Trevor David Rhone
Source: ACS Applied Materials & Interfaces (article, 2025-08-18)
Field: Materials Science — Materials Chemistry

Data-Driven Studies of van der Waals Magnetic Heterostructures Magnetic van der Waals (vdW) materials have the potential to revolutionize the semiconductor industry due to their various exotic physical properties. Herein, we use a data-driven framework to investigate magnetic vdW heterostructures of the form A<sup><i>i</i></sup>A<sup><i>ii</i></sup>B<sup><i>i</i></sup><sub>4</sub>X<sup><i>i</i></sup><sub>8</sub>/B<sup><i>ii</i></sup><sub>4</sub>X<sup><i>ii</i></sup><sub>6</sub> based on a well-known heterostructure MnBi<sub>2</sub>Te<sub>4</sub>/Sb<sub>2</sub>Te<sub>3</sub>. Our study shows that combining a nonmagnetic B<sup><i>ii</i></sup><sub>4</sub>X<sup><i>ii</i></sup><sub>6</sub> monolayer with a magnetic A<sup><i>i</i></sup>A<sup><i>ii</i></sup>B<sup><i>i</i></sup><sub>4</sub>X<sup><i>i</i></sup><sub>8</sub> monolayer can alter the magnetic properties as well as the band gap. By training various machine learning (ML) models on the density functional theory (DFT)-generated data set, we rapidly predict the properties of 16,431,660 A<sup><i>i</i></sup>A<sup><i>ii</i></sup>B<sup><i>i</i></sup><sub>4</sub>X<sup><i>i</i></sup><sub>8</sub>/B<sup><i>ii</i></sup><sub>4</sub>X<sup><i>ii</i></sup><sub>6</sub> heterostructures. The results from the ML predictions are used to identify promising candidate heterostructures. Our study aims to accelerate the design of vdW heterostructures that have applications in spintronics, optoelectronics, and topological quantum computing. Materials science ; van der Waals force ; Nanotechnology ; Condensed matter physics ; Physics ; Quantum mechanics ; Molecule 2D Materials and Applications ; Topological Materials and Phenomena ; Heusler alloys: electronic and magnetic properties
Authors: Wei Liu | Ruihao Bi | Wenjie Dou
Source: ArXiv.org (preprint, 2025-06-09)
Field: Materials Science — Materials Chemistry

Scalable Neural Quantum State based Kernel Polynomial Method for Optical Properties from the First Principle Variational optimization of neural-network quantum state representations has achieved FCI-level accuracy for ground state calculations, yet computing optical properties involving excited states remains challenging. In this work, we present a neural-network-based variational quantum Monte Carlo approach for ab-initio absorption spectra. We leverage parallel batch autoregressive sampling and GPU-supported local energy parallelism to efficiently compute ground states of complex systems. By integrating neural quantum ground states with the kernel polynomial method, our approach accurately calculates absorption spectra for large molecules with over 50 electrons, achieving FCI-level precision. The proposed algorithm demonstrates superior scalability and reduced runtime compared to FCI, marking a significant step forward in optical property calculations for large-scale quantum systems. Ground state ; Leverage (statistics) ; Kernel (algebra) ; Quantum ; Quantum algorithm ; Quantum state ; Polynomial ; Quantum computer ; Scalability Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Advanced Chemical Physics Studies
Authors: Kaniz Fatema
Source: ChemRxiv (preprint, 2025-01-27)
Field: Chemistry — Organic Chemistry

Benchmarking of Density Functional Theories for Combustion Reactions in Alkanes by Evaluating Thermodynamic Properties Computational calculations are playing vital role in this era of artificial intelligence and machine learning which is capable to lessen the burden of experimental expenses. Inevitably, to benchmark different theoretical methods is essential to produce accurate results while keeping in mind the computational cost. In this study, different functionals of Density Functional Theory (DFT) with 2 different basis sets were benchmarked to compute thermodynamic properties, enthalpy, Gibbs free energy, and entropy, of alkane combustion reactions. Results indicate a linear relationship between the number of carbon atoms and reaction parameters, with deviations arising from method-dependent approximations. LSDA and dispersion-corrected methods demonstrated closer agreement with experimental values with correlation-consistent basis set, while higher-rung functionals like PBE and TPSS exhibited significant errors with split-valence basis set, especially for larger chain lengths. Notably, convergence issues were observed for n-hexane with PBE and TPSS, attributed to near-degenerate states and SCF instability. These findings highlight the importance of functional choice along with the basis set to define the orbitals to accurately predict thermodynamic properties. Benchmarking ; Density functional theory ; Combustion ; Thermodynamics ; Chemistry ; Computational chemistry ; Biochemical engineering ; Computer science ; Physics ; Organic chemistry ; Economics ; Engineering Chemical Thermodynamics and Molecular Structure ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Akintayo Adeniji | Everardt Andre Burger | Mueez Are | Jacques Snyman | Julius Musyoka Ndambuki | Chris Ackerman
Source: Engineering and Technology Journal (article, 2025-08-16)
Field: Materials Science — Polymers and Plastics

An artificial neural network model prediction of mortar strengths reinforced with henequen and sisal fibres for sustainable construction The increasing demand for sustainable building materials has spurred interest in using natural fibers for construction. This study evaluates the performance of henequen and sisal fibers in mortar to enhance their tensile properties and provide alternatives to synthetic fibers. Mortar samples were prepared using a 1:3 mix ratio, with fibers introduced at 0%, 0.5%, 1%, 1.5%, and 2% by weight. The particle size distribution, setting times, consistency, and water ab-sorption and tensile strength of mortar constituents and fibers were determined. Compressive and splitting tensile strength tests were conducted on samples of 75 mm cubes and 50 &times; 100 mm cylinders at 7, 14, 21, and 28 days, respectively. Data were analyzed using ANOVA at &alpha; = 0.05 and a validated Artificial Neural Network (ANN) model. Henequen fiber exhibited lower water absorption and higher tensile strength. The 1% mixture recorded the highest com-pressive strength of 15.08 MPa and 15.41 MPa, and the split tensile strength at 0.5% mixture was 3.43 MPa and 3.51 MPa at 28 days, respectively. ANOVA confirmed significant dif-ferences in tensile strength due to fiber inclusion, and compressive strength results indicated no statistically significant differences across samples. The ANN demonstrated high predic-tive accuracy, with R&sup2; values of 0.8785 and 0.7885, and low mean squared errors of 0.3855 and 0.4855, for compressive and split tensile strengths, supporting its use in performance forecasting. The optimum compressive strength was observed at a 1% fiber content, while the split tensile strength improved up to 0.5%. The experimental results support its application in sustainable construction. Mortar ; Artificial neural network ; SISAL ; Structural engineering ; Computer science ; Artificial intelligence ; Materials science ; Engineering ; Composite material Natural Fiber Reinforced Composites ; Tree Root and Stability Studies
Authors: Haicheng Gao | Jingyi Zheng | Yuan Liu | Bingyu Li | Shixi Tang
Source:  (article, 2025-04-11)
Field: Chemistry — Analytical Chemistry

DBA-GNN: A Sparse and Dense Feature Extraction Bifrequency Graph Neural Network for Drug Property Prediction Graph neural networks (GNNs) have significantly enhanced the analysis of drug molecules represented as graph-structured data. However, existing methods often neglect the sparse or dense characteristics of graphs and struggle with attribute prediction accuracy due to small sample sizes. To address these challenges, we introduce Drug Bifrequency Attention Graph Neural Network (DBA-GNN). DBA-GNN features a two-stage learning process: a warm-up stage where graph data undergo random masking to enhance foundational modeling capabilities, followed by a task-oriented stage using the Bifrequency Attention Mixer (BAM) module, which models sparse or dense graph characteristics to improve adaptability and predictive accuracy. Our experiments on a dataset of 188 compounds show that DBA-GNN achieves an accuracy of 92.4% and sensitivity of 93.6%, outperforming state-of-the-art models by up to 10%. Feature extraction ; Computer science ; Property (philosophy) ; Artificial intelligence ; Pattern recognition (psychology) ; Artificial neural network ; Feature (linguistics) Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Romakanta Bhattarai (10832656) | Peter Minch (22076114) | Trevor David Rhone (11836139)
Source: Figshare (article, 2025-08-18)
Field: Materials Science — Materials Chemistry

Data-Driven Studiesof van der Waals Magnetic Heterostructures Magnetic van der Waals (vdW) materials have the potential to revolutionize the semiconductor industry due to their various exotic physical properties. Herein, we use a data-driven framework to investigate magnetic vdW heterostructures of the form A<sup><i>i</i></sup>A<sup><i>ii</i></sup>B<sup><i>i</i></sup><sub>4</sub>X<sup><i>i</i></sup><sub>8</sub>/B<sup><i>ii</i></sup><sub>4</sub>X<sup><i>ii</i></sup><sub>6</sub> based on a well-known heterostructure MnBi<sub>2</sub>Te<sub>4</sub>/Sb<sub>2</sub>Te<sub>3</sub>. Our study shows that combining a nonmagnetic B<sup><i>ii</i></sup><sub>4</sub>X<sup><i>ii</i></sup><sub>6</sub> monolayer with a magnetic A<sup><i>i</i></sup>A<sup><i>ii</i></sup>B<sup><i>i</i></sup><sub>4</sub>X<sup><i>i</i></sup><sub>8</sub> monolayer can alter the magnetic properties as well as the band gap. By training various machine learning (ML) models on the density functional theory (DFT)-generated data set, we rapidly predict the properties of 16,431,660 A<sup><i>i</i></sup>A<sup><i>ii</i></sup>B<sup><i>i</i></sup><sub>4</sub>X<sup><i>i</i></sup><sub>8</sub>/B<sup><i>ii</i></sup><sub>4</sub>X<sup><i>ii</i></sup><sub>6</sub> heterostructures. The results from the ML predictions are used to identify promising candidate heterostructures. Our study aims to accelerate the design of vdW heterostructures that have applications in spintronics, optoelectronics, and topological quantum computing. Heterojunction ; van der Waals force ; Monolayer ; Density functional theory ; Magnetic semiconductor ; Semiconductor 2D Materials and Applications ; Heusler alloys: electronic and magnetic properties ; Boron and Carbon Nanomaterials Research
Authors: Gyamfi Akyianu | Carsten Kneuer | Jung Choi
Source: Toxicology Letters (article, 2025-09-01)
Field: Chemistry — Spectroscopy

P18-10 Exploring in silico tools to characterise oestrogen receptor activity of chemicals for the assessment of endocrine disruption In silico ; Oestrogen receptor ; Quantitative structure–activity relationship ; Set (abstract data type) ; Identification (biology) Analytical Chemistry and Chromatography
Authors: Daniel Sánchez-Martínez | Kazuhiro Shimonomura | Juan A. Micó | Carlos A. Jara | Francisco Gomez‐Donoso
Source: The International Journal of Advanced Manufacturing Technology (article, 2025-05-01)
Field: Materials Science — Polymers and Plastics

Slippage detection during manufacturing toy assembly tasks Abstract This study introduces a novel approach for slip detection during the manipulation of deformable objects, focusing specifically on doll assembly, a process that demands high precision and delicacy. By integrating matrix tactile sponge sensors with long short-term memory (LSTM) networks, the proposed method captures detailed tactile data during handling, detecting subtle interactions with soft materials that are often missed by traditional sensing techniques. The LSTM networks process this data to model temporal dependencies, enabling the system to identify and predict slip events with remarkable accuracy. The approach addresses key challenges in automating tasks that involve deformable objects, bridging the gap between manual precision and robotic consistency. Moreover, the solution is designed to be scalable, providing a versatile framework that can be adapted to other manufacturing sectors requiring delicate manipulation of soft or flexible materials. By combining traditional production techniques with cutting-edge automation, this research supports the evolution of smart factories and demonstrates a pathway to automating complex processes previously thought to require human expertise. The results emphasize the potential of advanced sensor integration and machine learning in transforming manufacturing practices. Slippage ; Industrial and production engineering ; Manufacturing engineering ; Engineering drawing ; Computer science ; Industrial engineering ; Engineering ; Mechanical engineering ; Structural engineering Textile materials and evaluations ; Ergonomics and Musculoskeletal Disorders ; Industrial Vision Systems and Defect Detection
Authors: Anastasiia A. Mikhailova | Alexey P. Maltsev | Paulo C. D. Mendes | Fernando Buendía | Artem R. Oganov | Sergey M. Kozlov
Source: The Journal of Chemical Physics (article, 2025-09-22)
Field: Materials Science — Materials Chemistry

Structure and adsorption properties of Cu–Au nanoparticles in harsh reactive environments The reactivity of nanoparticles is governed by their surface composition, which tends to vary significantly under reactive conditions. In this study, the impacts of temperature and the presence of a CO or O2 atmosphere on the structures of Au79, Cu19Au60, Cu39Au40, Cu60Au19, and Cu79 nanoparticles were investigated using a combination of global optimization, machine learning interatomic potentials, and density functional theory. We find that increasing gold content weakens CO and O adsorption and limits oxygen-induced structural changes, while copper-rich particles undergo pronounced oxidation and reconstruction. Bader charge analysis shows that, in bare nanoalloys, Au withdraws electron density from Cu due to their electronegativity difference. In oxidized Cu-Au nanoalloys, the charge on Cu atoms depends on the number of O neighbors and can reach values typical of Cu2O (0.6 e) and CuO (1.1 e). On average, the Cu atoms exhibit positive effective Bader charges of ∼0.9 e in these nanoalloys. Our results indicate that models of Cu-Au catalysts under oxidizing conditions must incorporate the full ensemble of metallic and oxidized configurations rather than a single most probable structure to predict true catalytic activity. Electronegativity ; Adsorption ; Nanoparticle ; Oxidizing agent ; Catalysis ; Charge density ; Reactivity (psychology) ; Metal Catalytic Processes in Materials Science ; Nanomaterials for catalytic reactions ; Nanocluster Synthesis and Applications
Authors: Mr. Mangesh Nalwade
Source: International Journal for Research in Applied Science and Engineering Technology (article, 2025-07-15)
Field: Materials Science — Materials Chemistry

A Review: Personalized Nano Medicine Personalized nanomedicine represents a revolutionary convergence of nanotechnology, biomedicine, and data science to deliver tailored therapeutic and diagnostic solutions for individual patients. Unlike traditional medicine, which often employs generalized treatment protocols, personalized nanomedicine leverages advances in genomics, proteomics, and metabolomics to design nanoscale systems that dynamically adapt to a patient’s unique biological profile. By integrating smart nanomaterials— such as polymeric nanoparticles, lipid-based carriers, and inorganic nanostructures—with real-time biomarker detection and AI-driven predictive modeling, this approach enables precision targeting, enhanced drug bioavailability, and minimized offtarget effects. Recent breakthroughs include stimuli-responsive nanocarriers that release drugs in response to tumor-specific signals (e.g., pH, enzymes, or redox gradients), theranostic nanoparticles that combine imaging and therapy for real-time treatment monitoring, and CRISPR-loaded nanovehicles for personalized gene editing. Additionally, AI and machine learning are transforming nanomedicine by optimizing nanoparticle design, predicting patient responses, and accelerating drug discovery. Despite these advancements, key challenges persist, including long-term biocompatibility, large-scale manufacturing reproducibility, regulatory complexities, and cost-effectiveness in clinical deployment. Future directions focus on multi-modal nanosystems (e.g., combined chemo-immunotherapy), implantable nanodevices for continuous health monitoring, and patientspecific nanovaccines for cancer and infectious diseases. This review provides a comprehensive analysis of the current state of personalized nanomedicine, highlighting its transformative potential in oncology, neurodegenerative disorders, and infectious disease management, while critically examining the hurdles that must be overcome for widespread clinical adoption. Nanomedicine ; Personalized medicine ; Nanocarriers ; Nanotechnology ; Precision medicine ; Biomedicine ; Computer science ; Medicine ; Drug delivery ; Bioinformatics ; Materials science ; Nanoparticle ; Biology Advanced Nanomaterials in Catalysis ; Innovative Microfluidic and Catalytic Techniques Innovation ; Machine Learning in Materials Science
Authors: Mariia Karabin | Ian S. Armstrong | Leopold Beck | Paulina Apanel | Markus Eisenbach | David B. Mitzi | Hanna Terletska | Hendrik Heinz
Source: ArXiv.org (preprint, 2025-12-05)
Field: Materials Science — Materials Chemistry

Enhancing Dimensionality Prediction in Hybrid Metal Halides via Feature Engineering and Class-Imbalance Mitigation We present a machine learning framework for predicting the structural dimensionality of hybrid metal halides (HMHs), including organic-inorganic perovskites, using a combination of chemically-informed feature engineering and advanced class-imbalance handling techniques. The dataset, consisting of 494 HMH structures, is highly imbalanced across dimensionality classes (0D, 1D, 2D, 3D), posing significant challenges to predictive modeling. This dataset was later augmented to 1336 via the Synthetic Minority Oversampling Technique (SMOTE) to mitigate the effects of the class imbalance. We developed interaction-based descriptors and integrated them into a multi-stage workflow that combines feature selection, model stacking, and performance optimization to improve dimensionality prediction accuracy. Our approach significantly improves F1-scores for underrepresented classes, achieving robust cross-validation performance across all dimensionalities. Curse of dimensionality ; Workflow ; Feature (linguistics) ; Oversampling ; Feature engineering ; Dimensionality reduction ; Benchmark (surveying) ; Class (philosophy) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Inorganic Chemistry and Materials