jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Guru, Mahish K. | Bohlen, Jan | Aydin, Roland C. | Ben Khalifa, Noomane
Source: PubData (article, 2025-05-30)
Field: Materials Science — Biomaterials

Machine learning pipeline for Structure–Property modeling in Mg-alloys using microstructure and texture descriptors Identifying the relationships between material structure and mechanical properties has been crucial for accelerating the exploration of the material design space for advanced alloys. However, traditional approaches for magnesium (Mg) alloys often fall short in providing quantitative and broadly applicable structure–property linkages. To address this challenge, a comprehensive machine learning pipeline is presented for structure–property modeling in extruded Mg-alloys, leveraging both microstructure and texture descriptors derived from experimental data. The pipeline encompasses a robust workflow for data extraction from optical microscopy and X-ray diffraction, advanced image processing and deep learning techniques for microstructure binarization and grain statistics, and the computation of statistical descriptors including n-point spatial correlations, gram matrices for microstructure, and generalized spherical harmonics (GSH) for texture. Dimensionality reduction techniques such as principal component analysis (PCA), isomap, and autoencoders are employed to manage the high-dimensionality of the descriptor space. Subsequently, non-linear regression models—Gaussian Process, XGBoost, and Multi-Layer Perceptron regressors—are evaluated to predict mechanical properties, specifically strain hardening exponent (n) and yield stress (σy). Our results demonstrate that XGBoost consistently outperforms other regressors, achieving a notably low mean absolute percentage error (MAPE) of 6.67% for strain hardening exponent and 7.01% for yield stress, using a combination of PCA-reduced 3-point spatial correlations and isomap-reduced gram matrices as microstructure descriptors, and isomap-reduced GSH coefficients as texture descriptors at a 150μm length scale. Shapley Additive exPlanations (SHAP) analysis further reveals that texture descriptors and aspect ratio distribution are the most influential features in predicting mechanical properties. This established ML framework for structure–property modeling in Mg-alloys, surpasses state-of-the-art benchmarks and provides a valuable template for materials design and discovery. Dimensionality reduction ; Extreme learning machine ; Pattern recognition (psychology) ; Principal component analysis ; Multilayer perceptron ; Pipeline (software) ; Texture (cosmology) ; Computation ; Microstructure Magnesium Alloys: Properties and Applications ; Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties
Authors: Zhili Chang | Guangquan Li | Wei‐Jun Cai | Haolan Liu | Guangcheng Zhang | Wei‐Tao Ou
Source: Catalysis Letters (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Application of Machine Learning in High-throughput Screening of Binary Alloys for the Hydrogenation of Benzene Boosting (machine learning) ; Gradient boosting ; Support vector machine ; Benzene ; Binary number ; Adsorption ; Multilayer perceptron ; Hydrogen Machine Learning in Materials Science ; Catalysts for Methane Reforming ; Hydrogen Storage and Materials
Authors: Yongzhi Wu | Haoxiang Zhang | Hanqing Li | Keyuan Chen | Jinlong Chen | Xingkao Zhang | Ju Rong | Yudong Sui | Xiaohua Yu | Jing Feng
Source: The Journal of Physical Chemistry C (article, 2025-04-30)
Field: Materials Science — Materials Chemistry

Deep Learning-Enhanced Titanium Potential Models for Accurate Molecular Dynamics Deep potential (DP) is an advanced deep learning-based method that can extract complex material interactions from density functional theory (DFT) calculations to construct highly accurate potential energy functions. Among various material systems, the transition metal titanium (Ti) has garnered significant attention in fields such as energy storage and conversion and catalysis, due to its unique electronic structure and catalytic activity. However, the complex interatomic interactions of Ti pose challenges for traditional potential models, making accurate descriptions difficult. To address this challenge, we developed a DP method capable of effectively establishing the interatomic potential functions required for molecular dynamics (MD) simulations of Ti, thereby enhancing simulation accuracy. This study systematically compares the predictions of the DP model with results from DFT, demonstrating that the DP model achieves DFT-level accuracy in predicting key properties. These properties include crystal structural characteristics, defect properties, surface and interface properties, as well as mechanical properties. The α-phase Hexagonal Close-Packed structure automatically transforms into the β-phase Body-Centered Cubic structure at 1189 K, exhibiting a melting temperature of 1864 K. These values closely align with the experimental phase transition temperature of 1209 K and the melting point of 1941 K, confirming the DP model’s accuracy under high-temperature conditions. Increasing temperature enhances Ti atom diffusivity, with a self-diffusion coefficient observed at the melting temperature of 2.51 × 10–10 m2/s, matching those of other Face-Centered Cubic metals. The successful application of this method lays a foundation for multiscale simulations and performance predictions of complex materials, with potential to advance high-throughput screening and precise modeling of new materials. Molecular dynamics ; Titanium ; Dynamics (music) ; Computer science ; Deep learning ; Artificial intelligence ; Materials science ; Psychology ; Chemistry ; Computational chemistry ; Metallurgy Machine Learning in Materials Science ; Nuclear Physics and Applications ; Crystallography and molecular interactions
Authors: Yan Zhang | Fei Gao | Guofeng Cui | Kuang Ya-fei | Shuotong Zong | fenghua chen | Hongxia Liu | Zhigang Sun | Baogen Shen
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning of Heavy Rare Earth-Based Low-Temperature Magnetocaloric Materials Under Complex Classification Methods Magnetic refrigeration ; Rare earth ; Materials science ; Artificial intelligence ; Computer science ; Metallurgy ; Physics Magnetic and transport properties of perovskites and related materials ; Machine Learning in Materials Science
Authors: Javier Covarrubias-Garcia | Francisco Alfredo García-Pastor | Mario Castelán
Source: The International Journal of Advanced Manufacturing Technology (article, 2025-11-08)
Field: Materials Science — Materials Chemistry

Deep Learning-Based classification of aluminium alloy microstructures using synthetic metallographic data Abstract The present study investigates the integration of synthetic metallographic data into machine learning workflows for the automated analysis of aluminium alloy microstructures in manufacturing contexts. A bespoke algorithm was developed to generate synthetic micrographs that replicate key metallurgical features associated with various manufacturing processes, including sand casting, die casting, forging, extrusion, and different heat treatment conditions. Convolutional Neural Networks (CNNs) trained exclusively on synthetic datasets were evaluated using real microscopy images subjected to conditions simulating real-world acquisition challenges, demonstrating strong generalization performance. The models achieved a classification accuracy of 99.3% across 21 distinct microstructural classes under optimal imaging conditions. The findings indicate that synthetic data can serve as a substitute for real images in training applications, thereby facilitating the development of robust models that are capable of generalising effectively across process-induced microstructural variations. The proposed approach offers a scalable, cost-efficient solution for microstructural classification and enables the integration of data-driven analysis into advanced manufacturing pipelines, which contributes to enhanced process monitoring and quality control. Convolutional neural network ; Workflow ; Microstructure ; Aluminium alloy ; Process (computing) ; Generalization ; Artificial neural network ; Alloy Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Metallurgy and Material Forming
Authors: Yan Zhang | Fei Gao | Guofeng Cui | Shuotong Zong | fenghua chen | Hongxia Liu | Kuang Ya-fei | Zhigang Sun | Baogen Shen
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning of Heavy Rare Earth-Based Low-Temperature Magnetocaloric Materials Under Complex Classification Methods Magnetic refrigeration ; Rare earth ; Materials science ; Artificial intelligence ; Computer science ; Metallurgy ; Physics ; Magnetization Magnetic and transport properties of perovskites and related materials ; Machine Learning in Materials Science
Authors: Paolo Ferro | Saveria Spiller | Kwei Tang | Francesco Berto | Franco Bonollo
Source: Engineering Applications of Artificial Intelligence (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Sustainable ductile iron design: Leveraging machine learning and genetic algorithms for critical raw materials reduction Raw material ; Ductile iron ; Cast iron ; Supply chain ; European union ; Genetic algorithm ; Alloy ; Sustainable development Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Iron and Steelmaking Processes
Authors: Džonatans Miks Melgalvis | Toms Rekis
Source: Acta Crystallographica Section A Foundations and Advances (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

On artificial crystal structure generation for solving the phase problem with deep learning We discuss and present approaches for generating artificial crystal structures for training neural networks to solve the phase problem. Structure generation is considered as a two-step process involving sampling unit-cell parameters and filling the unit cell with atoms. The former step includes generating lattice basis vectors from randomly sampled unit-cell volume. Apart from randomly placing atoms, we use database data to guide fast and scalable generation of molecule-like fragments. The recently developed neural network PhAI is then used as a benchmark and retrained with various sets of training data to assess how the corresponding models perform on experimental crystal structure data. We found a significant improvement in PhAI retrained on a new kind of artificial data to generalize the phase problem solution for larger unit-cell structures. Artificial neural network ; Deep learning ; Scalability ; Phase (matter) ; Lattice (music) ; Sampling (signal processing) ; Network structure ; Benchmark (surveying) Machine Learning in Materials Science ; Crystallography and molecular interactions ; Enzyme Structure and Function
Authors: Xiao Tang | Liangcai Wu | Ziang Xu | Lei Liu | Zhitang Song | Wenxiong Song
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Thermal conductivity of selenium crystals based on machine learning potentials Selenium, as an important semiconductor material, exhibits significant potential for understanding lattice dynamics and thermoelectric applications through its thermal transport properties. Conventional empirical potentials are often unable to accurately describe the phonon transport properties of selenium crystals, which limits in-depth understanding of their thermal conduction mechanisms. To address this issue, this study developed a high-precision machine learning potential (MLP), with training datasets generated <i>via ab initio</i> molecular dynamics simulations. Validation demonstrated that the phonon dispersion relations calculated by the MLP showed excellent agreement with density functional theory results. Using this potential, we systematically investigated the thermal transport properties of trigonal (t-Se) and monoclinic selenium (m-Se). The results demonstrate that t-Se exhibits higher thermal conductivity. Phonon density of states analysis reveals that this originates from its chain-like structure (where intrachain atoms are connected by strong covalent bonds while interchain interactions occur through weaker van der Waals forces), which enables stronger thermal transport compared to the ring-like structure of m-Se. The electronic structure calculations further reveal that the bandgap of t-Se is significantly smaller than that of m-Se (by approximately 0.7 eV). Therefore, although t-Se exhibits a relatively large lattice thermal conductivity, its higher electrical conductivity (<i>σ</i>) (six orders of magnitude difference) and Seebeck coefficient compensate for this disadvantage, enabling t-Se to achieve a high <i>ZT</i> (<i>σ</i>/<i>κ</i> ratio) and superior thermoelectric potential. Selenium ; Thermal conductivity ; Thermal ; Materials science ; Chemistry ; Nanotechnology ; Chemical physics ; Physics ; Thermodynamics ; Composite material ; Metallurgy Machine Learning in Materials Science
Authors: Nikita Volzhin | Soowhan Yoon
Source: arXiv (Cornell University) (preprint, 2025-12-22)
Field: Materials Science — Materials Chemistry

Kolmogorov-Arnold graph neural networks for chemically informed prediction tasks on inorganic nanomaterials The recent development of Kolmogorov-Arnold Networks (KANs) has found its application in the field of Graph Neural Networks (GNNs) particularly in molecular data modeling and potential drug discovery. Kolmogorov-Arnold Graph Neural Networks (KAGNNs) expand on the existing set of GNN models with KAN-based counterparts. KAGNNs have been demonstrably successful in surpassing the accuracy of MultiLayer Perceptron (MLP)-based GNNs in the task of molecular property prediction. These models were widely tested on the graph datasets consisting of organic molecules. In this study, we explore the application of KAGNNs towards inorganic nanomaterials. In 2024, a large scale inorganic nanomaterials dataset was published under the title CHILI (Chemically-Informed Large-scale Inorganic Nanomaterials Dataset), and various MLP-based GNNs have been tested on this dataset. We adapt and test our own KAGNNs appropriate for eight defined tasks. Our experiments reveal that, KAGNNs frequently surpass the performance of their counterpart GNNs. Most notably, on crystal system and space group classification tasks in CHILI-3K, KAGNNs achieve the new state-of-the-art results of 99.5 percent and 96.6 percent accuracy, respectively, compared to the previous 65.7 and 73.3 percent each. Graph ; Artificial neural network ; Multilayer perceptron ; Chemical space ; Task (project management) ; Set (abstract data type) ; Property (philosophy) ; Perceptron Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Xiong Lei | Fumin Xu
Source: Materials Letters (article, 2025-09-27)
Field: Materials Science — Biomaterials

Utilizing machine learning to predict and interpret corrosion rate of pickling-treated AZ31 magnesium alloys Interpretability ; Corrosion ; Feature selection ; Magnesium ; Magnesium alloy ; Feature (linguistics) ; Nonlinear regression ; Gradient boosting ; Nonlinear system Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties ; Aluminum Alloys Composites Properties
Authors: T. Clive Lee | Min Ook Kim
Source: Symmetry (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Machine Learning-Enabled Quantification and Interpretation of Structural Symmetry Collapse in Cementitious Materials The mechanical and durability performance of cementitious materials is fundamentally governed by the symmetry, anisotropy, and hierarchical organization of their microstructures. Conventional experimental characterization—based on imaging, spectroscopy, and physical testing—often struggles to capture these multiscale spatial patterns and their nonlinear correlations with macroscopic performance. Recent advances in machine learning (ML) provide unprecedented opportunities to interpret structural symmetry and anisotropy through data-driven analytics, computer vision, and physics-informed models. Furthermore, we summarize cases where symmetry-informed descriptors improve performance prediction accuracy in fiber- and nano-modified composites, demonstrating that ML-based symmetry analysis can substantially complement the limitations of conventional experimental-based characterization. We confirm that image-based models such as CNN and U-Net quantify the directionality and connectivity of pores and cracks, and that physically informative neural networks (PINNs) and heterogeneous data-based models enhance physical consistency and computational efficiency compared to conventional FEM and CFD. Finally, we present the conceptual and methodological foundation for developing AI-based microstructural symmetry analysis, aiming to go beyond simple prediction and establish a conceptual foundation for AI-driven cement design based on microstructure–performance causality. Symmetry (geometry) ; Cementitious ; Anisotropy ; Foundation (evidence) ; Consistency (knowledge bases) ; Nonlinear system ; Finite element method ; Interpretation (philosophy) Machine Learning in Materials Science ; Composite Material Mechanics ; Rock Mechanics and Modeling
Authors: Nada M. Alghamdi | Paolo De Angelis | Pietro Asinari | Eliodoro Chiavazzo
Source: ArXiv.org (preprint, 2025-11-27)
Field: Materials Science — Materials Chemistry

Screening novel cathode materials from the Energy-GNoME database using MACE machine learning force field and DFT The development of new battery materials, particularly novel cathode chemistries, is essential for enabling next generation energy storage technologies. In this work, we employ a multi-fidelity screening protocol combining the Energy-GNoME confident criteria, foundational MACE machine-learning force fields (MLFF), and physically motivated heuristic filters to identify novel intercalation cathodes for post-lithium batteries, namely: Na-, K-, Mg-, and Ca-ion batteries. Foundational MACE models are used to efficiently asses dynamical stability, thermodynamical stability, average voltage, and theoretical specific energy, enabling a rapid screening of candidates. For the most promising cathodes, voltage predictions are refined using DFT+U calculations. This work delivers three key outcomes: i) establishing and validating a robust high-throughput screening approach for cathode materials with foundational MLFF models; ii) suggestions for cathode candidates for the development of next-generation of batteries; iii) a fair comparison between the MACE predictions and the readily available figures of merit reported in the Energy-GNoME database on the examined materials. Cathode ; Mace ; Heuristic ; Field (mathematics) ; Battery (electricity) ; Voltage ; Figure of merit Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Rajagopal, Deepalaxmi | Cierpka, Adrian | Nestler, Britta | Koeppe, Arnd Hendrik
Source: KITopen (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Generative Deep Learning for Advanced Battery Materials Deep learning ; Generative grammar ; Battery (electricity) ; Artificial neural network ; Generative model Machine Learning in Materials Science ; Advanced Battery Technologies Research ; X-ray Diffraction in Crystallography
Authors: Valter Costa | José Manuel Oliveira | Patrícia Ramos
Source: Computation (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Deep Learning-Driven Integration of Multimodal Data for Material Property Predictions Advancements in deep learning have revolutionized materials discovery by enabling predictive modeling of complex material properties. However, single-modal approaches often fail to capture the intricate interplay of compositional, structural, and morphological characteristics. This study introduces a novel multimodal deep learning framework for enhanced material property prediction, integrating textual (chemical compositions), tabular (structural descriptors), and image-based (2D crystal structure visualizations) modalities. Utilizing the Alexandriadatabase, we construct a comprehensive multimodal dataset of 10,000 materials with symmetry-resolved crystallographic data. Specialized neural architectures, such as FT-Transformer for tabular data, Hugging Face Electra-based model for text, and TIMM-based MetaFormer for images, generate modality-specific embeddings, fused through a hybrid strategy into a unified latent space. The framework predicts seven critical material properties, including electronic (band gap, density of states), thermodynamic (formation energy, energy above hull, total energy), magnetic (magnetic moment per volume), and volumetric (volume per atom) features, many governed by crystallographic symmetry. Experimental results demonstrated that multimodal fusion significantly outperforms unimodal baselines. Notably, the bimodal integration of image and text data showed significant gains, reducing the Mean Absolute Error for band gap by approximately 22.7% and for volume per atom by 22.4% compared to the average unimodal models. This combination also achieved a 28.4% reduction in Root Mean Squared Error for formation energy. The full trimodal model (tabular + images + text) yielded competitive, and in several cases the lowest, error metrics, particularly for band gap, magnetic moment per volume and density of states per atom, confirming the value of integrating all three modalities. This scalable, modular framework advances materials informatics, offering a powerful tool for data-driven materials discovery and design. Deep learning ; Moment (physics) ; Property (philosophy) ; Mean squared error ; Modular design ; Volume (thermodynamics) ; Reduction (mathematics) ; Fusion Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Pigment Synthesis and Properties
Authors: Licheng Zhu | Zhanqiang Liu | Minghui Guo | Ziyan Wei | Qiang Shen | Zhijian Shen | Tianzeng Ru
Source:  (article, 2025-08-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning Research on Nickel Phosphide Electrode Material for Supercapacitors The position of electrode materials in super-capacitors is very important, which directly affects the performance, efficiency and application scope of super-capacitors. Nickel phosphide has become a potential electrode material because of its strong redox activity, good conductivity, low cost, high chemical and thermal stability, etc. The purpose of this study is to build a high-precision machine learning model to predict the capacitance of nickel phosphide supercapacitors, extract a large number of characteristic data from published papers, screen abnormal values by quartile data processing method, and fill in missing values by K-nearest neighbor (KNN) data mining technology. Four machine learning regression algorithms, RF, LightGBM, XGBoost and GBDT, are used to predict the capacitance of nickel phosphide, and six different performance indexes are used to evaluate the model. Finally, according to the performance evaluation, the squared error (R2), root mean square error (RMSE) and mean absolute error (MAE) of XGBoost model are 0.96,77.33 and 49.2, respectively, indicating its stability and reliability. Phosphide ; Nickel ; Capacitance ; Electrode ; Mean squared error ; Quartile ; Supercapacitor Supercapacitor Materials and Fabrication ; Machine Learning in Materials Science ; Neural Networks and Applications