jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nurdaulet Izmailov | Meirambek Shaimerden | Azamat Toishybek | Kaisar Kassymzhanov | Araylim Mukangalieva | Nurzhan Ultarakov | Alma Turganbayeva
Source: Journal of problem in computer science and information technologies (article, 2025-12-30)
Field: Materials Science — Materials Chemistry

MASS-CONSERVING PHYSICS-INFORMED AUGMENTATION AND FOURIER FEATURE NETWORKS FOR SMALL-DATA PREDICTION OF MOLYBDENITE (MO₂S) LEACHING KINETICS Molybdenum remains a strategic metal for advanced steels and catalysis, while environmental and energy pressures are accelerating interest in hydrometallurgical leaching routes for molybdenite (MoS₂). Predicting leaching kinetics is difficult because the process is highly nonlinear and strongly influenced by reagent chemistry and gas–liquid conditions, yet experimental datasets in metallurgical laboratories are often extremely small. This manuscript develops a hybrid, data-efficient machine-learning approach designed specifically for small-data settings. The method combines physics-informed data augmentation that enforces strict mass conservation with a Fourier Feature Network intended to reduce spectral bias and better capture sharp kinetic transitions. Using only six experimental measurements, the resulting model achieves high predictive accuracy on held-out data (R² = 0.9793, MAE = 1.61%) and maintains stable generalization without evidence of train–test divergence. The study concludes that physically admissible augmentation coupled with Fourier-enriched representations can produce reliable kinetic surrogates from minimal data, supporting in-silico screening and optimization of leaching conditions for process design and control. Leaching (pedology) ; Nonlinear system ; Molybdenite ; Kinetic energy ; Reagent ; Fourier transform ; Kinetics Machine Learning in Materials Science ; Iron and Steelmaking Processes ; Metallurgical Processes and Thermodynamics
Authors: Wei, Ting-Ju | Su, Tung-Huan | Chen, Chuin-Shan
Source: arXiv (Cornell University) (preprint, 2025-12-07)
Field: Materials Science — Materials Chemistry

A Texture-Generalizable Deep Material Network via Orientation-Aware Interaction Learning for Polycrystal Modeling and Texture Evolution Machine learning surrogate models have emerged as a promising approach for accelerating multiscale materials simulations while preserving predictive fidelity. Among them, the Orientation-aware Interaction-based Deep Material Network (ODMN) provides a hierarchical homogenization framework in which material nodes encode crystallographic texture and interaction nodes enforce stress equilibrium under the Hill--Mandel condition. Trained solely on linear-elastic stiffness data, ODMN captures intrinsic microstructure--mechanics relationships, enabling accurate prediction of nonlinear mechanical responses and texture evolution. However, its applicability remains fundamentally limited by the absence of a parametric mapping from arbitrary microstructures to the ODMN parameter space. This limitation necessitates retraining for each new microstructure. To address this challenge, we reformulate ODMN generalization as a microstructure-to-parameter inference problem and propose the TACS--GNN--ODMN framework. The proposed framework combines a Texture-Adaptive Clustering and Sampling (TACS) scheme for texture representation with a Graph Neural Network (GNN) for inferring micromechanical equilibrium parameters. This strategy enables the construction of fully parameterized ODMNs for previously unseen microstructures without retraining. Numerical results demonstrate that the proposed framework accurately predicts nonlinear mechanical responses and texture evolution across diverse texture distributions. The predicted responses show close agreement with direct numerical simulations (DNS), highlighting the framework as a generalizable and physically interpretable surrogate model for microstructure-informed multiscale materials simulations. Initialization ; Nonlinear system ; Artificial neural network ; Texture (cosmology) ; Set (abstract data type) ; Generalization ; Cluster analysis ; Network model Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Graph Neural Networks
Authors: Z.F. Zhang | Dong Liang | Gongbin Tang | Hua Yang | Bangshao Dong | Zhongwei Liang
Source: Engineering Applications of Artificial Intelligence (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

Data-driven multi-objective synergistic to targeted enhancement framework for high-performance aluminum alloy design Simulated annealing ; Controllability ; Inverse ; Generative grammar ; Generative model ; Boosting (machine learning) ; Aerospace ; Classifier (UML) ; Evolutionary algorithm Machine Learning in Materials Science ; Topology Optimization in Engineering ; Advanced Multi-Objective Optimization Algorithms
Authors: Theodoros Anagnostopoulos | Anna Milionis | Chara Kottara
Source: WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT (article, 2025-06-19)
Field: Chemistry — Analytical Chemistry

Prediction of PDO Kalamata Olive Oil Tasting Quality based on Statistical Machine Learning Techniques Utilizing Real Data from Synchronous Emission-Excitation Fluorescence Spectra and Tasting Analyses Combining domain knowledge provided by analyses of Protected Designation of Origin (PDO) Kalamata olive oil data sources could input multiclass classification statistical machine learning models to output accurate predictions of certain olive oil quality classes. Concretely, machine learning models are trained with both synchronous emission-excitation fluorescence spectra provided by certain spectroscopic techniques with tasting analyses of domain tasting panelists to infer the quality class of the input sample. Such a process enhances tasting panelists specific tasting knowledge by supporting their prediction accuracy with chemical data of certain real olive oil data samples. In this research effort, real PDO Kalamata olive oil data is used from fluorescence spectra and tasting processes to train multiclass classification statistical machine learning models to provide accurate predictions of the examined olive oil samples’ quality classes. Wine tasting ; Artificial intelligence ; Olive oil ; Quality (philosophy) ; Machine learning ; Computer science ; Pattern recognition (psychology) ; Biological system ; Mathematics ; Chemistry ; Wine ; Food science Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Advanced Chemical Sensor Technologies
Authors: M G Gopika | Leona Rachel Varghese | Krishna Yadav | Mohammad Khalid | Beena Saraswathyamma
Source: Critical Reviews in Analytical Chemistry (article, 2025-11-14)
Field: Materials Science — Materials Chemistry

Silver Nanomaterial-Doped Layered Double Hydroxides for Advanced Electrochemical Sensing: A Critical Review Silver-doped layered double hydroxides (Ag-LDHs) have gained prominence as a high-performing class of electroactive materials suited to advanced electrochemical sensors, exhibiting remarkable sensitivity, selectivity, and long-term stability in environmental, biomedical, and food analytical settings. Silver incorporation not only narrows the band gap of the LDH host but also endows the heterostructure with enhanced electrical conductivity, surface reactivity, and rapid redox cycling, clearly outpacing both pure and conventional-metal-doped LDH architectures. These hybrids utilize the inherent ion-exchange capacity and extensive surface area of LDHs, further enhanced by integration with functional materials such as carbon allotropes, metal oxides, and conductive polymers, leading to a synergistic improvement in electrocatalytic performance and mechanical resilience. Despite several studies on these composites, a comprehensive study that critically compares structural designs, synthesis methods, and functional performance in electrochemical sensing is absent. This article addresses that gap by providing a systematic, side-by-side comparison of contemporary Ag-LDH synthetic routes, surface-modification protocols, and sensing metrics. Application-centric evaluations encompass the quantification of pollutants in aqueous matrices, the diagnosis of disease-relevant biomarkers, and the deterrence of unsafe food items. Enduring barriers, including the scale-up of manufacture with consistent quality, stability over extended operational lifetimes, and the realization of cost-competitive fabrication, are rigorously appraised alongside prospective pathways, such as eco-conscious synthesis protocols, hybrid nanoscale architectures, and machine-learning-augmented ideation of sensor designs. Collectively, the compiled findings furnish a coherent roadmap for the translational maturation of Ag-LDH-based electrochemical sensing technologies that transcend proof-of-principle and advance toward the reliable point-of-care deployment beyond controlled laboratory environs. Layered double hydroxides ; Electrochemistry ; Nanocomposite ; Redox ; Surface modification ; Characterization (materials science) ; Electrical conductor ; Aqueous medium Layered Double Hydroxides Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors ; Carbon and Quantum Dots Applications
Authors: Austin Houston | Sumner B. Harris | Hao Wang | Yu‐Chuan Lin | David B. Geohegan | Kai Xiao | Gerd Duscher
Source: ArXiv.org (preprint, 2025-02-13)
Field: Materials Science — Materials Chemistry

Atom identification in bilayer moire materials with Gomb-Net Moire patterns in van der Waals bilayer materials complicate the analysis of atomic-resolution images, hindering the atomic-scale insight typically attainable with scanning transmission electron microscopy. Here, we report a method to detect the positions and identities of atoms in each of the individual layers that compose twisted bilayer heterostructures. We developed a deep learning model, Gomb-Net, which identifies the coordinates and atomic species in each layer, effectively deconvoluting the moire pattern. This enables layer-specific mapping of quantities like strain and dopant distributions, unlike other commonly used segmentation models which struggle with moire-induced complexity. Using this approach, we explored the Se atom substitutional site distribution in a twisted fractional Janus WS2-WS2(1-x)Se2x heterostructure and found that layer-specific implantation sites are unaffected by the moire pattern's local energetic or electronic modulation. This advancement enables atom identification within material regimes where it was not possible before, opening new insights into previously inaccessible material physics. Moiré pattern ; Atom (system on chip) ; Identification (biology) ; Net (polyhedron) ; Materials science ; Crystallography ; Nanotechnology ; Physics ; Chemistry ; Geometry ; Mathematics ; Computer science ; Optics ; Biology Quasicrystal Structures and Properties ; Diatoms and Algae Research
Authors: Debdipto Acharya | Omar Abou El Kheir | Simone Marcorini | Marco Bernasconi
Source: ArXiv.org (preprint, 2025-01-30)
Field: Materials Science — Materials Chemistry

Simulation of the crystallization kinetics of Ge$_2$Sb$_2$Te$_5$ nanoconfined in superlattice geometries for phase change memories Phase change materials are the most promising candidates for the realization of artificial synapsis for neuromorphic computing. Different resistance levels corresponding to analogic values of the synapsis conductance can be achieved by modulating the size of an amorphous region embedded in its crystalline matrix. Recently, it has been proposed that a superlattice made of alternating layers of the phase change compound Sb$_2$Te$_3$ and of the TiTe$_2$ confining material allows for a better control of multiple intermediate resistance states and for a lower drift with time of the electrical resistance of the amorphous phase. In this work, we consider to substitute Sb$_2$Te$_3$ with the Ge$_2$Sb$_2$Te$_5$ prototypical phase change compound that should feature better data retention. By exploiting molecular dynamics simulations with a machine learning interatomic potential, we have investigated the crystallization kinetics of Ge$_2$Sb$_2$Te$_5$ nanoconfined in geometries mimicking Ge$_2$Sb$_2$Te$_5$/TiTe$_2$ superlattices. It turns out that nanoconfinement induces a slight reduction in the crystal growth velocities with respect to the bulk, but also an enhancement of the nucleation rate due to heterogeneous nucleation. The results support the idea of investigating Ge$_2$Sb$_2$Te$_5$/TiTe$_2$ superlattices for applications in neuromorphic devices with improved data retention. The effect on the crystallization kinetics of the addition of van der Waals interaction to the interatomic potential is also discussed. Superlattice ; Kinetics ; Crystallization ; Materials science ; Phase-change memory ; Phase (matter) ; Germanium compounds ; Phase change ; Crystallography ; Chemical engineering ; Thermodynamics ; Condensed matter physics ; Germanium ; Chemistry ; Metallurgy ; Optoelectronics ; Physics ; Silicon ; Engineering ; Classical mechanics Phase-change materials and chalcogenides
Authors: Colin Bailey | Thomas D. Roper
Source: VCU Scholars Compass (Virginia Commonwealth University) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Predicting Complex Di-Alkylation Kinetics Using Machine Learning Background: Predicting reaction kinetics for complex chemical systems often presents significant challenges due to intricate reaction pathways, multiple product formations, and competing side reactions. Additionally, the transient nature of reaction intermediates and limited experimental data complicate efforts to capture system dynamics accurately. These complexities necessitate advanced modeling approaches and precise experimental techniques to reliably describe and predict chemical behavior. Methods: This work investigates the kinetics of di-alkylation of 4-Hydroxybenzoic Acid (4-HBA) using multiple machine learning algorithms, including Random Forest Regression, Gradient Boosting, Lasso, Ridge, and XGBoost. By leveraging reaction conditions, such as temperature, sulfuric acid equivalents, and initial concentrations of reactants, the developed models accurately predict concentrations for various reaction species. Results: The predictions demonstrate good correlations with experimental results, achieving high accuracy with R² values for some compounds. Hyperparameter optimization was systematically performed using Bayesian optimization methods and validated through Leave-One-Group-Out (LOGO) cross-validation to mitigate the risk of overfitting. Conclusions: Future work includes changing the scoring method from R² to RMSE, MAE, or a custom scorer to assess if enhanced real-world model prediction performance can be achieved. Hyperparameter ; Bayesian optimization ; Random forest ; Bayesian probability ; Work (physics) ; Chemical kinetics ; Experimental data Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Catalysis and Oxidation Reactions
Authors: Suyue Yuan | Kwangnam Kim | Bo Wang | Wonseok Jeong | Tae Wook Heo | Brandon C. Wood | Liwen F. Wan
Source: Physical Review Research (article, 2025-07-21)
Field: Materials Science — Materials Chemistry

Strain perturbation method for atomic stress calculation with machine-learning potentials Accurate computation of local atomic virial stress is crucial for predicting materials’ mechanical response in large-scale molecular dynamic simulations, but conventional methods struggle with high-order atomic environment descriptors' encoded machine-learning many-body potentials. Herein we propose the strain perturbation method, a general framework that can efficiently compute high-resolution atomic virial stress. While remaining consistent with pairwise virial formulation, this method is broadly applicable and robust across many-body potentials with various atomic environment descriptors. Perturbation (astronomy) ; Strain (injury) ; Stress (linguistics) ; Stress–strain curve ; Materials science ; Atomic physics ; Physics ; Quantum mechanics ; Composite material ; Biology ; Deformation (meteorology) ; Philosophy Microstructure and mechanical properties ; Machine Learning in Materials Science ; Welding Techniques and Residual Stresses
Authors: Nidhi Joshi | Erhan Guven
Source:  (article, 2025-03-19)
Field: Chemistry — Organic Chemistry

Novel Vector Embeddings by Molecular Attributes helps de-novo Molecular Generation The increasing need for molecules with tailored properties in fields such as drug discovery and chemical engineering has driven the development of advanced computational methods for molecular design. Machine learning (ML) based approaches focused on de-novo molecular generation have recently gained significant attention. This paper presents a new transformer-based vector embedding generator that can be combined with Generative Adversarial Networks (GANs) to generate new molecules with desired properties. The embedding generator introduces a new molecular descriptor instead of SMILES strings, integrating Morgan fingerprints with global molecular attributes, enabling the transformer to capture local functional groups and broader molecular characteristics. The transformer model achieves a reconversion accuracy of 94% while translating the molecular descriptors back to SMILES strings, reinforcing the validity of the new descriptor for generative tasks. The model is validated for generating novel odorant molecules using a labeled dataset of odorant and non-odorant compounds. The GAN generates diverse molecules, maintaining high novelty, uniqueness, and validity. This work highlights the potential of using the proposed advanced molecular descriptors with transformers and generative AI techniques to accelerate the discovery of molecules with tailored properties, offering a powerful tool for a wide range of applications in molecular design. Computer science ; Vector (molecular biology) ; Computational biology ; Biology ; Gene ; Genetics ; Recombinant DNA Click Chemistry and Applications
Authors: Joaquı́n Barroso-Flores
Source: Front Matter (article, 2025-01-28)
Field: Materials Science — Materials Chemistry

How DeepSeek Can Revolutionize Computational Chemistry Research and Education Computational chemistry is a field that thrives on innovation, precision, and the ability to tackle complex problems. With the rise of artificial intelligence (AI) and machine learning (ML), tools like &lt;strong&gt; DeepSeek &lt;/strong&gt; are becoming indispensable for researchers and students alike. Field (mathematics) ; Computational model ; Chemistry education Machine Learning in Materials Science ; Genetics, Bioinformatics, and Biomedical Research ; Scientific Computing and Data Management
Authors: Renlan Liu | Huiying Huang | Zeyu Lu | Ye Tang | Tadayuki Fujii | Yingchao Dong | Xiangyong Zheng | Min Zhao
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

A Sustainable Robust Metal-Doped Algal Biochar Catalyst with Enhanced Catalytic Performance for Emerging Pollutants Degradation Biochar ; Pollutant ; Catalysis ; Degradation (telecommunications) ; Environmental science ; Environmental chemistry ; Metal ; Waste management ; Chemistry ; Materials science ; Metallurgy ; Computer science ; Engineering Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Guangyu Liu | Yuntao Li | Yanchang Qi | Chengyong Ma
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Study on Microstructure and Mechanics of Cr/Nb on Nicrmo-Based Fusion Clad Metal Based on BP Artificial Neural Network Artificial neural network ; Microstructure ; Fusion ; Materials science ; Metal ; Artificial intelligence ; Metallurgy ; Computer science ; Philosophy Nuclear Materials and Properties ; Metal Alloys Wear and Properties ; Metallurgy and Material Forming
Authors: Hiroki Koiso (17705362) | Shoei Kawasoe (21367362) | Jinfu Zhang (709197) | Toshihiro Isobe (1932904) | Akira Nakajima (86971) | Yasuhide Mochizuki (8309001)
Source: Figshare (article, 2025-05-15)
Field: Materials Science — Materials Chemistry

Discovery of BiaxialNegative Thermal Expansion inKNbSi<sub>2</sub>O<sub>7</sub> from Computations and Experiments Framework-type negative-thermal expansion (NTE) materials are often composed of corner-sharing octahedra and tetrahedra. Considering the insertion of K into a framework structure, aiming at increasing the average atomic volume (AAV), we have focused on KNbSi<sub>2</sub>O<sub>7</sub> (KNSO). The thermal expansion behavior of KNSO has been predicted by molecular-dynamics (MD) calculations using machine-learning force field (MLFF). We have discovered that the <i>a</i> and <i>b</i> axes of KNSO show NTE behavior, while the <i>c</i> axis possesses positive thermal expansion (PTE) behavior through its synthesis and temperature-dependent X-ray diffraction (XRD) patterns, which are consistent with the MD calculation results. We present that the thermal expansion behavior of KNSO cannot be explained only by the anisotropic negative Grüneisen parameters based on the quasi-harmonic approximation (QHA). Our results imply that (i) anisotropic thermal expansion behaviors can be reproduced by the MD calculations and (ii) the second-order Jahn–Teller (SOJT) effect elongates the <i>c</i>-axis lattice constant, leading to the increase of lattice flexibility that enhances NTE behavior. The present results stimulate the exploration of NTE materials from first-principles calculations combined with MLFF. Negative thermal expansion ; Thermal expansion ; Anisotropy ; Thermal ; Lattice (music) ; Computation ; Diffraction Thermal Expansion and Ionic Conductivity ; Advancements in Battery Materials ; Nuclear materials and radiation effects
Authors: Shuo Yang | Ruoxuan Peng | Leitao Cao | Jing Ren | Jinrong Yao | Xin Chen | Zhengzhong Shao | Shengjie Ling
Source: Advanced Functional Materials (article, 2025-12-05)
Field: Materials Science — Biomaterials

Hierarchical Silk Fibril Networks Exhibit Structure Dependent Mechanical Performance Abstract The remarkable mechanical properties of silk fibroin originate from its hierarchical fibrous architecture spanning multiple length scales; however, how these intricate networks precisely govern macroscopic mechanical behavior remains incompletely understood. Here, experimental characterization with computational modeling is integrated to elucidate the structural and molecular mechanisms underpinning silk fibril network formation and mechanical function. High‐resolution imaging coupled with deep‐learning‐based morphological extraction enables precise quantification of critical architectural features within the networks. Coarse‐grained molecular dynamics simulations reveal that network mechanical properties and stability are predominantly governed by interfibrillar interaction strengths, with hydrophobic forces identified as the primary molecular drivers of fibril bundling. Specifically, weak interfibrillar cohesive interactions result in flexible and deformable networks under applied stresses, whereas strong interactions induce excessive fibril aggregation, reducing structural adaptability and leading to premature mechanical failure. Simulations under spatially confined conditions, mimicking natural spinning processes, further clarify mechanisms essential for fibril alignment. This study significantly advances the fundamental understanding of silk's structure–property relationships, providing valuable guidelines for designing and precisely controlling silk‐based materials for biomedical and structural engineering applications. Fibril ; Fibroin ; SILK ; Characterization (materials science) ; Molecular dynamics ; Multiscale modeling ; Microscale chemistry ; Spinning Silk-based biomaterials and applications ; Collagen: Extraction and Characterization ; Hydrogels: synthesis, properties, applications