jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Arthur Souesme | René Guinebretière | O. Castelnau | Alexandre Boulle
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2025-11-19)
Field: Materials Science — Materials Chemistry

Neural network determination of crystallite size and microstrain from X-ray powder diffraction data The present work sets out the development of a convolutional neural network (CNN) dedicated to the determination of crystallite size and microstrain in polycrystalline materials, from powder X-ray diffraction (XRD) data. The CNN was trained using synthetic data generated with a dedicated code based on the GSAS-II software. We employ the example of monoclinic ZrO2 to critically examine the theoretical performances of the CNN. The accuracy of the predictions was systematically investigated within a crystallite size range of 5 -1000 nm and a microstrain range of 0.05-2%. In this context, the role of the resolution of the diffractometer and the XRD peak profile shape is discussed in details. For instance, on a low-resolution laboratory diffractometer it possible to determine the crystallite size within a 5-100 nm with 99.7% accuracy in the absence of microstrain, and 97.3% accuracy when microstrains are present. Higher accuracies are obtained with a highresolution diffractometer and over an extended range of size and microstrain. The potential of the CNN is demonstrated by the analysis of the crystallite size and microstrains in MgAl O formed by a ₂ ₄ solid-state reaction between MgO and Al O using ₂ ₃ in situ XRD at 1200 °C at the BM01 beamline at the ESRF. The CNN can determine the crystallite size and microstrain in 0.004 s per diagram, with values in remarkable agreement with those obtained by a conventional Rietveld refinement. We further demonstrate that the same training dataset can be used for other regression problems, for instance phase fraction quantification, with no additional computational cost and only minor modifications to the network architecture. This work paves the way for real-time data analysis at synchrotron facilities. Crystallite ; Diffractometer ; Diffraction ; Rietveld refinement ; Powder Diffractometer ; Monoclinic crystal system ; X-ray crystallography ; Powder diffraction X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Nuclear Physics and Applications
Authors: Takunda Happison Nyenyewa
Source: Synthesis and Sintering (article, 2025-09-30)
Field: Materials Science — Ceramics and Composites

Numerical insights into thermal behavior of advanced ceramics using COMSOL Multiphysics: A mini review This paper provides an in-depth overview of heat transfer in advanced ceramics using COMSOL Multiphysics, aiming to achieve accurate simulations and analytical insights for ultra-high-temperature ceramics (UHTCs). COMSOL Multiphysics has applications across various industries that require advanced investigations into the performance of materials under extreme heat conditions. The finite element method (FEM) implemented in the software serves as an effective tool for solving governing equations and addressing heat transfer problems in a wide range of cutting-edge applications. The focus is not only on current methodological approaches but also on the future evolution of thermal behavior analysis in ceramics, such as the integration of machine learning. Overall, the results highlight the importance of numerical methods as a bridge between materials science and high-level engineering applications. Multiphysics ; Finite element method ; Heat transfer ; Thermal engineering ; Ceramic ; Thermal ; Software Advanced ceramic materials synthesis ; Thermal properties of materials ; Numerical methods in inverse problems
Authors: Yasir Fathi Mahmood
Source: مجلة كلية الاسراء الجامعة للعلوم والهندسة. (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

Generative AI for Method Development in Analytical Chemistry: A New Paradigm in Experimental Design and Optimization The fast development of artificial intelligence (AI), especially generative AI models, is changing the environment of analytical chemistry. As classical method generation in analytical methods relies on manual trial-and-error methodology as well as statistical methods, generative AI is a new paradigm with automated generation of experimental methodology and optimization. In this paper, the authors discuss the use of generative AI-based technologies, including large language models (LLMs) and neural network-based generators, to create new, efficient, and customized methods of analysis. The paper examines existing applications, technology frameworks, and issues and offers a roadmap with regards to the future incorporation of generative AI into daily analytical processes. Finally, the review indicates the promise of generative AI to reinvent analytical chemistry as a highly empirical research area into a predictive, data-driven research area. Generative grammar ; Generative Design ; Development (topology) ; Generative model ; Empirical research Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Data Classification
Authors: Yakun Yang | Yu Chen | Yukang Xu | Yudong Wang | Guangwu Sun
Source: Polymer (article, 2025-11-20)
Field: Materials Science — Biomaterials

Inverse prediction of micro-nano fiber membrane preparation process based on deep learning Spinning ; Deep learning ; Fabrication ; Process (computing) ; Inverse ; Transformer ; Microfluidics ; Inverse problem Electrospun Nanofibers in Biomedical Applications ; Epoxy Resin Curing Processes ; Machine Learning in Materials Science
Authors: Takeyuki Kato (277950) | Ryuki Kayano (19787715) | Takahiro Ohkubo (1273047)
Source: Figshare (article, 2025-08-08)
Field: Materials Science — Ceramics and Composites

Machine-LearningMolecular Dynamics Study on the Structureand Glass Transition of Calcium Aluminosilicate Glasses Calcium aluminosilicate (CAS) glass systems represent an important class of materials for industrial applications due to their superior thermal and mechanical properties. Although CAS glasses have been extensively studied, the structure–property relationships, particularly in the peraluminous region (Al<sub>2</sub>O<sub>3</sub>/CaO > 1), remain insufficiently understood. Experimental studies have identified the presence of five-coordinated aluminum (Al<sup>5</sup>) depending on the Al<sub>2</sub>O<sub>3</sub> content; however, classical molecular dynamics simulations have struggled to accurately reproduce the aluminum coordination environment. To address this limitation, we developed a machine-learning potential tailored for CAS systems, trained on a comprehensive molecular dynamics simulation based on density functional theory data set and refined using a fine-tuning approach. Melt-quench simulations were then carried out to model CAS glass structures. The resulting structures from machine learning-based molecular dynamics (MLMD) accurately reproduced both experimental glass densities and the fractions of Al<sup>5</sup>, including the observed increase in Al<sup>5</sup> and oxygen triclusters (TBOs) in the peraluminous region. In addition, we performed heating simulations to evaluate enthalpy changes and structural evolution as a function of temperature. Analogous to differential scanning calorimetry experiments, the glass transition temperature (<i>T</i><sub>g</sub>) was determined from the MLMD data. The compositional dependence of Al<sup>5</sup> and TBO near the <i>T</i><sub>g</sub> was quantitatively analyzed, providing insights into the role of aluminum in structural rearrangements. These findings demonstrate that MLMD enables the accurate modeling of CAS glass structures and yields valuable insights into their thermal behavior. This approach offers a robust framework for understanding structure–property relationships in complex glass systems. Molecular dynamics ; Glass transition ; Aluminosilicate ; Differential scanning calorimetry ; Calcium aluminosilicate ; Aluminium ; Thermal ; Enthalpy Glass properties and applications ; Material Dynamics and Properties ; Thermal and Kinetic Analysis
Authors: Bobbinpreet Kaur | Shashi Prakash Dwivedi | Kamaljeet Kaur | Shagufta Praveen | Rashmi Priyadarshini | Zahraa Alkhafajy | Sumit Pundir | Bhawna Goyal
Source: Journal of Physics Conference Series (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Evaluating Regression Algorithms for Accurate Fatigue Strength Prediction in Structural Steels Abstract The accurate prediction of steel fatigue strength is vital for the design and reliability assessment of load-bearing components in industries such as automotive, aerospace, and civil engineering. Traditional experimental methods for fatigue strength determination are often costly and time-intensive. Material informatics utilizing the capabilities of advanced Machine learning models has evolved as a significant alternative in materials science, offering solutions to previously intractable issues and expediting outcomes. In this study, we explore the application of regression-based machine learning models from the Scikit-learn library to predict the fatigue strength of steel based on material composition and mechanical properties. Using a publicly available dataset, we implement and compare multiple regression algorithms including Gradient Boosting, Random Forest, XGBoost, Linear Regression, Decision Tree, RidgeCV, AdaBoost, k-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), and Stochastic Gradient Descent (SGD). Feature selection is performed through correlation analysis and recursive feature elimination to identify key attributes influencing fatigue performance. Models are trained and validated using k-fold cross-validation, and their performance is evaluated using R 2 , Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE).The novelty of this work lies in providing a systematic, comparative evaluation of a broad range of machine learning regressors for fatigue strength prediction, offering a data-driven alternative to experimental testing and highlighting the critical features that govern fatigue behavior in steels. Feature selection ; Support vector machine ; Random forest ; Feature (linguistics) ; Linear regression ; Stochastic gradient descent ; Regression ; Perceptron ; Mean squared error Machine Learning in Materials Science ; Fatigue and fracture mechanics ; Gaussian Processes and Bayesian Inference
Authors: Delanie Lynch | N. A. W. Holzwarth
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

A Systematic Investigation of Reliable Methods for Calculating Ionic Conductivity in Solid Electrolytes by Combining Machine-Learned Potentials Trained from First Principles Molecular Dynamics and Statistical Analysis Recent advancements in machine learning have created new tools for running molecular dynamics simulations using learned potential functions with near first principles accuracy and an increase in simulation performance, allowing for longer time scales and larger system sizes to be simulated. This increased accessibility of long time and large system simulations opens the possibility of calculating ionic conductivity more accurately, by allowing for significantly more ionic diffusion events to occur as well as introducing ionic correlation effects, which are typically seen at macroscopic time scales. Calculating the ionic conductivity, even with these new machine-learned methods, remains challenging due to the significant levels of inherent noise present in the ionic trajectories, especially at long time scales. To more realistically calculate ionic conductivity using these machine-learned potentials, we must employ more accurate descriptions of the full ionic conductivity from the Green-Kubo formalism [1] as well as utilize statistical methods [2] for analyzing the ionic trajectories in an effort to reduce this noise. This work details an assessment of combining these methods to calculate the ionic conductivity more reliably, determining their ability to do so by training Allegro Machine Learned Potentials [3] on test systems of solid electrolyte alloys of Li 3 PO 4 -Li 4 SiO 4 [4]. References: [1] Marcolongo and Marzari 2017. DOI: 10.1103/PhysRevMaterials.1.025402 [2] Pegolo, Drigo, Grasselli, Baroni 2025. DOI: 10.1063/5.0249677 [3] Musaelian, Batzner, et. al. 2023. DOI: 10.1038/s41467-023-36329-y [4] Deng, Eame, et. al. 2015. DOI: 10.1021/jacs.5b04444 Acknowledgements: NSF Grant DMR-2242959 WFU DEAC Cluster Ionic conductivity ; Ionic bonding ; Molecular dynamics ; Electrolyte ; Conductivity ; Fast ion conductor ; Noise (video) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: Yuhao Zhao | Liya Zhu | Yihang Li | Heng Pan | Ming Chen | Wenhao Li | Guozhi Jia | Yu‐Bin Chen | Youjun Lu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Data-Driven Discovery of Thermo-Mechanically Compatible Mn-Based Air Electrodes for High-Performance Reversible Protonic Ceramic Cells Ceramic ; Materials science ; Electrode ; Composite material ; Nanotechnology ; Chemical engineering ; Chemistry ; Engineering ; Physical chemistry Advancements in Solid Oxide Fuel Cells ; Catalytic Processes in Materials Science ; Gas Sensing Nanomaterials and Sensors
Authors: Yue Yang (52715) | Shiang-Tai Lin (1283010)
Source: Figshare (article, 2025-09-22)
Field: Materials Science — Materials Chemistry

Physics-Embedded Machine Learning Model for PhaseEquilibrium Prediction in Multicomponent Systems We present TeNNet-SAC (<b>T</b>hermodynamics-<b>e</b>mbedded <b>N</b>eural <b>Net</b>work for <b>S</b>egment <b>A</b>ctivity <b>C</b>oefficient) model, a novel machine learning framework for predicting activity coefficients in liquid mixtures using only the SMILES representations of the constituent molecules. Inspired by the quantum chemistry-based COSMO-SAC model, TeNNet-SAC evaluates activity coefficients by summing contributions from molecular surface segments. The model comprises three core components: (1) a σ-profile predictor, which generates molecular fingerprints (i.e., surface segment charge histogram or σ-profile) directly from SMILES; (2) a geometry predictor, which estimates molecular volume and surface area from SMILES; and (3) a Γ predictor, which computes the activity coefficients of surface segments in solution. The σ-profile and geometry predictors are trained on 39,745 quantum solvation calculations. The Γ predictor is initially pretrained on one million synthetic data points to capture physically consistent behavior and is subsequently fine-tuned end-to-end using experimental activity coefficient data to improve predictive accuracy. The base TeNNet-SAC model achieves accuracy comparable to COSMO-SAC, while the fine-tuned version consistently outperforms COSMO-SAC across benchmark systems. By treating segment activity coefficients as intermediate variables, TeNNet-SAC naturally generalizes to multicomponent mixtures and satisfies thermodynamic consistency, offering a robust and scalable solution for activity coefficient prediction. Benchmark (surveying) ; Surface (topology) ; Scalability ; Quantum ; Histogram ; Activity coefficient ; Data point ; Core (optical fiber) Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Computational Drug Discovery Methods
Authors: Ademola Soyemi (12246034) | Khagendra Baral (4488256) | Tibor Szilvási (1355940)
Source: Figshare (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Modeling EquilibriumSolid–Liquid Interfacesunder Effective Constant Chemical Potential Using Machine LearningInteratomic Potentials The chemical potential (μ) of species in solution is essential for understanding various chemical processes at interfaces. Molecular dynamics (MD) simulations, constrained by fixed compositions, cannot maintain constant chemical potential with reference to a targeted concentration or chemical potential under nonequilibrium or dynamic conditions, as solute species can migrate to the interface and deplete (or enrich) the bulk due to solute-interface interactions. In this study, we introduce a simple and computationally efficient approach named iterative quasi-constant chemical potential molecular dynamics (iqCμMD) simulation, which helps simulate targeted molar concentrations of species in solution. iqCμMD overcomes the limitations of conventional MD by adjusting the number of species in the solution to reach a target bulk concentration (chemical potential), which allows simulation of the interface under the bulk conditions comparable to experiment. We demonstrate our approach using machine learning interatomic potential (MLIP)-based MD simulations of the Na<sub>2</sub>SO<sub>4,aq</sub>–graphene interface, and to show the transferability of our approach, we also perform classical force field-based MD simulations of NaCl<sub>aq</sub>–air and NaCl<sub>aq</sub>–graphite interfaces, which produce comparable results to previous CμMD simulations. Our results also show that the iqCμMD approach efficiently achieves the desired bulk ion concentration within two iterations, and by utilizing MLIPs, we can achieve converged results using relatively small-scale simulations compared to previous CμMD simulations. By combining iqCμMD with MLIP-driven simulations, solid–liquid interfaces can be modeled under an effective constant chemical potential with DFT-level accuracy. We show that iqCμMD offers a robust and simple computational framework for constant chemical potential simulations, as its only requirement is to be able to converge interfacial simulations with a measurable bulk region. Interatomic potential ; Molecular dynamics ; Transferability ; Constant (computer programming) ; Non-equilibrium thermodynamics ; Chemical species ; Simple (philosophy) Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Nanopore and Nanochannel Transport Studies
Authors: Fernando Arrais Romero Dias Lima | A.C. Silva | Marcellus G.F. de Moraes | Evaristo C. Biscaia | Argimiro R. Secchi | Idelfonso B. R. Nogueira | Maurício B. de Souza
Source: Systems and Control Transactions (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

Development of a Hybrid Model for the Paracetamol Batch Dissolution in Ethanol Using Universal Differential Equations Crystallization is a relevant process in the pharmaceutical industry for product purification and particle production. An efficient crystallization is characterized by crystals produced with the desired attributes. Therefore, modeling this process is a key point to achieve this goal. In this sense, the objective of this work is to propose a hybrid model to describe paracetamol dissolution in ethanol. The universal differential equations methodology is considered in the development of this model, using a neural network to predict the dissolution rate combined with the population balance equations to calculate the moments of the crystal size distribution (CSD) and the concentration. The model was developed using experimental batches. The dataset is composed of concentration measurements obtained using attenuated total reflectance-Fourier transform infrared (ATR-FTIR). The objective function of the optimization problem is to minimize the relative absolute difference between the experimental and the predicted concentration. The hybrid model efficiently predicted the concentration compared to the experimental measurements. Furthermore, the hybrid approach made predictions of the moments of the CSD similar to the population balance model proposed by Kim et al. [1], being able to successfully calculate batches not considered in the training dataset. Moreover, the performance of the hybrid model was similar to the phenomenological one based on population balance. Therefore, the universal differential equations approach is presented as an efficient methodology for modeling crystallization processes with limited information. Dissolution ; Ethanol ; Differential equation ; Computer science ; Process (computing) ; Applied mathematics ; Mathematics ; Chemistry ; Chemical engineering ; Engineering ; Mathematical analysis ; Organic chemistry ; Programming language Crystallization and Solubility Studies ; Advanced Control Systems Optimization ; Process Optimization and Integration
Authors: Hui Zhang | Jinlian Hu | Jiale Chen | Jianmei Pan | Jingqi Wang | Xiaofei Wang | Da Chen
Source: Microchimica Acta (article, 2025-10-13)
Field: Materials Science — Materials Chemistry

Machine learning-assisted N-doped carbon dots for sensitive Pd2+ detection and high-performance LED applications in plant cultivation Convolutional neural network ; Hydrothermal circulation ; Quenching (fluorescence) ; Detection limit ; Fluorescence ; Carbon fibers ; Light-emitting diode ; Hyperspectral imaging Carbon and Quantum Dots Applications ; Advanced biosensing and bioanalysis techniques ; Electrochemical sensors and biosensors
Authors: Gaijinliu Gangmei | Santu Rana | Bernard Rolfe | Kishalay Mitra | S. Bhattacharyya
Source: ArXiv.org (preprint, 2025-07-24)
Field: Materials Science — Materials Chemistry

Learning coupled Allen-Cahn and Cahn-Hilliard phase-field equations using Physics-informed neural operator(PINO) Phase-field equations, mostly solved numerically, are known for capturing the mesoscale microstructural evolution of a material. However, such numerical solvers are computationally expensive as it needs to generate fine mesh systems to solve the complex Partial Differential Equations(PDEs) with good accuracy. Therefore, we propose an alternative approach of predicting the microstructural evolution subjected to periodic boundary conditions using Physics informed Neural Operators (PINOs). In this study, we have demonstrated the capability of PINO to predict the growth of $θ^{\prime}$ precipitates in Al-Cu alloys by learning the operator as well as by solving three coupled physics equations simultaneously. The coupling is of two second-order Allen-Cahn equation and one fourth-order Cahn-Hilliard equation. We also found that using Fourier derivatives(pseudo-spectral method and Fourier extension) instead of Finite Difference Method improved the Cahn-Hilliard equation loss by twelve orders of magnitude. Moreover, since differentiation is equivalent to multiplication in the Fourier domain, unlike Physics informed Neural Networks(PINNs), we can easily compute the fourth derivative of Cahn-Hilliard equation without converting it to coupled second order derivative. Partial differential equation ; Fourier transform ; Operator (biology) ; Coupling (piping) ; Fourier series ; Multiplication (music) ; Differential equation ; Artificial neural network ; Fast Fourier transform Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Solidification and crystal growth phenomena
Authors: Ibrahim Nadeem
Source: Research Square (preprint, 2025-11-05)
Field: Materials Science — Materials Chemistry

Leveraging Machine Learning to Predict High-Temperature Superconductors Superconductivity ; Transformative learning ; Maxima ; High-temperature superconductivity ; Supervised learning ; Polynomial ; Maxima and minima ; Power (physics) Machine Learning in Materials Science ; Superconductivity in MgB2 and Alloys ; Sentiment Analysis and Opinion Mining
Authors: Courtney Kunselman | Siya Zhu | ‪Doğuhan Sarıtürk | Raymundo Arróyave
Source: Journal of Phase Equilibria and Diffusion (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Construction and Tuning of CALPHAD Models Using Machine-Learned Interatomic Potentials and Experimental Data: A Case Study of the Pt–W System CALPHAD ; Workflow ; Context (archaeology) ; Work (physics) ; Gibbs free energy ; Phase diagram ; Phase (matter) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems