jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Huỳnh Thanh Nhẫn | Nguyen Duy Thong | Lê Hoàng Minh | Trần Thiện Thanh | Châu Văn Tạo
Source: IEEJ Transactions on Electrical and Electronic Engineering (article, 2025-07-28)
Field: Materials Science — Materials Chemistry

Use Machine Learning to Classify Materials Based on Gamma Scattering Spectra In this study, machine learning is used to determine materials and thickness of materials based on gamma scattering spectra. Materials used in this study are: Al, Si, Fe, Mn, Mg, Co, Cu, Zn, and Ti, which have thicknesses varying from 1 mm to 50 mm. In order to estimate thickness as well as material simultaneously, 1‐scattering spectrum and 2‐scattering spectrum are used. The Random Forest algorithm was used in training and evaluating the machine learning model. Results of this study provided a coefficient of determination R 2 = 0.990 and mean squared error MSE = 1.250. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. Spectral line ; Scattering ; Computer science ; Artificial intelligence ; Machine learning ; Materials science ; Physics ; Optics ; Astronomy X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Advanced X-ray and CT Imaging
Authors: Oleg Totosko | Petro Stukhliak | Danylo Stukhlіak | Oleh Yasniy
Source: Advances in Materials Science and Engineering (article, 2025-01-01)
Field: Materials Science — General Materials Science

Comprehensive Research of Physical and Mechanical Characteristics of Composite Materials Using Neural Networks The paper presents the results of studies of the physical and mechanical properties of materials: compressive failure stress, tensile failure stress, impact strength, and residual stresses in the material. To determine these parameters, we used modern methods of analyzing experimental studies, neural networks, to ensure a high measurement accuracy. Particular attention is paid to the research of the impact strength of residual stresses in the material, which allows for predicting the behavior of products in service. Impact toughness is one of the main indicators for evaluating materials that are operated under complex stress–strain conditions. It is proved that the values of both the compressive and tensile destructive stresses, impact strength, and residual stresses significantly affect the ability of the material to resist fracture under the action of both static and dynamic alternating loads. Critical values of residual stresses have been determined, the excess of which reduces the resistance of materials to fracture. The research has established the regularities of residual stresses and their magnitude in materials with different structural organizations. The research results are important from a scientific and technical point of view for the creation of new composite materials with high resistance to fracture, which will increase the operational reliability of the product as a whole. Materials science ; Composite number ; Artificial neural network ; Composite material ; Artificial intelligence ; Computer science Material Properties and Applications ; Advanced machining processes and optimization
Authors: Safa Enes Turkoglu | Yusuf Uzunoğlu
Source: Advances in Materials Science (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Interpretable Machine Learning and Genetic Algorithm-Based Optimization of Ultimate Tensile Strength And Melting Completion In Alloys Abstract In this study, we present a comprehensive machine learning-based approach for optimizing alloy compositions with the goal of simultaneously maximizing Ultimate Tensile Strength (UTS) and approaching a target Melting Completion temperature. Using a dataset comprising elemental compositions of various alloys and their corresponding mechanical properties, we developed predictive models based on the Random Forest Regressor algorithm. SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) were employed to interpret the feature contributions and determine the most influential elements on both UTS and melting behavior. The analysis revealed that elements such as Fe, Mn, Co, and Mo significantly contribute to optimal alloy performance, while elements like C and V play a critical role in enhancing UTS. A multi-objective optimization was conducted using a Genetic Algorithm (GA), yielding an optimal composition that achieved a predicted UTS of 1520.7 psi and a Melting Completion temperature of 1407.4°C, closely aligned with the target of 1460°C. Our approach demonstrates the potential of combining interpretable machine learning with evolutionary optimization to accelerate intelligent alloy design and discovery. Ultimate tensile strength ; Genetic algorithm ; Alloy ; Random forest ; Lime ; Evolutionary algorithm ; Support vector machine Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; High Entropy Alloys Studies
Authors: Qing Zhu | Yikui Xie | Weidong Xuan | Songzhe Xu | Zhongming Ren
Source: Journal of the Ceramic Society of Japan (article, 2025-10-31)
Field: Materials Science — Ceramics and Composites

Study on cross-material property prediction of mineralizer fiber ceramic core materials based on deep learning In this study, silicon-based ceramic cores were prepared using aluminum silicate fiber and mullite fiber as mineralizing agents through injection molding. A comprehensive properties evaluation model was proposed, optimizing the weight coefficient. The back-propagation (BP) neural networks were employed to predict the effects of different mineralizers on ceramic core properties, establishing a cross-material property mapping and prediction model. The results showed an abnormal increase in mean square error with the addition of 1 wt.% aluminum silicate fiber, indicating complex material behavior. Blending model predictions demonstrated the neural network’s strong capability in cross-material property prediction. The predictions closely matched experimental results, confirming the model’s accuracy. Mullite ; Ceramic ; Fiber ; Core (optical fiber) ; Silicate ; Artificial neural network ; Aluminium silicate Advanced ceramic materials synthesis ; Recycling and utilization of industrial and municipal waste in materials production ; Aerogels and thermal insulation
Authors: А. F. Schmidt | А. А. Курохтина | Е. В. Ларина | Н. А. Лагода
Source: Research Square (preprint, 2025-10-22)
Field: Materials Science — Materials Chemistry

Beyond Тraining Data Volume: Predicting Nonlinear Effects in Catalytic Reactions with Multiple Kinetic Regimes via Machine Learning Nonlinear system ; Training set ; Work (physics) ; A priori and a posteriori ; Experimental data ; Scarcity ; Kinetic energy Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Benítez, Pol | López, Cibrán | Saucedo, Edgardo | Mizoguchi, Teruyasu | Cazorla, Claudio
Source: ArXiv.org (article, 2025-11-19)
Field: Materials Science — Materials Chemistry

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties with Phonon-Informed Datasets Machine learning (ML) methods have become powerful tools for predicting material properties with near first-principles accuracy and vastly reduced computational cost. However, the performance of ML models critically depends on the quality, size, and diversity of the training dataset. In materials science, this dependence is particularly important for learning from low-symmetry atomistic configurations that capture thermal excitations, structural defects, and chemical disorder, features that are ubiquitous in real materials but underrepresented in most datasets. The absence of systematic strategies for generating representative training data may therefore limit the predictive power of ML models in technologically critical fields such as energy conversion and photonics. In this work, we assess the effectiveness of graph neural network (GNN) models trained on two fundamentally different types of datasets: one composed of randomly generated atomic configurations and another constructed using physically informed sampling based on lattice vibrations. As a case study, we address the challenging task of predicting electronic and mechanical properties of a prototypical family of optoelectronic materials under realistic finite-temperature conditions. We find that the phonons-informed model consistently outperforms the randomly trained counterpart, despite relying on fewer data points. Explainability analyses further reveal that high-performing models assign greater weight to chemically meaningful bonds that control property variations, underscoring the importance of physically guided data generation. Overall, this work demonstrates that larger datasets do not necessarily yield better GNN predictive models and introduces a simple and general strategy for efficiently constructing high-quality training data in materials informatics. Artificial neural network ; Property (philosophy) ; Predictive modelling ; Predictive power ; Ensemble learning ; Training set ; Task (project management) ; Experimental data ; Material properties ; Graph Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Thermal properties of materials
Authors: Thabang R. Somo | Kwena D. Modibane | Thabiso C. Maponya | Daniel M. Teffu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Theoretical Hydrogen Storage Properties of High Entropy Alloys: A Combined Dft and Machine Learning Approach Hydrogen storage ; High entropy alloys ; Entropy (arrow of time) ; Computer science ; Materials science ; Thermodynamics ; Machine learning ; Metallurgy ; Physics ; Alloy Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Catalytic Processes in Materials Science
Authors: Benítez, Pol | López, Cibrán | Saucedo, Edgardo | Mizoguchi, Teruyasu | Cazorla, Claudio
Source: arXiv (Cornell University) (preprint, 2025-11-19)
Field: Materials Science — Materials Chemistry

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties with Phonon-Informed Datasets Machine learning (ML) methods have become powerful tools for predicting material properties with near first-principles accuracy and vastly reduced computational cost. However, the performance of ML models critically depends on the quality, size, and diversity of the training dataset. In materials science, this dependence is particularly important for learning from low-symmetry atomistic configurations that capture thermal excitations, structural defects, and chemical disorder, features that are ubiquitous in real materials but underrepresented in most datasets. The absence of systematic strategies for generating representative training data may therefore limit the predictive power of ML models in technologically critical fields such as energy conversion and photonics. In this work, we assess the effectiveness of graph neural network (GNN) models trained on two fundamentally different types of datasets: one composed of randomly generated atomic configurations and another constructed using physically informed sampling based on lattice vibrations. As a case study, we address the challenging task of predicting electronic and mechanical properties of a prototypical family of optoelectronic materials under realistic finite-temperature conditions. We find that the phonons-informed model consistently outperforms the randomly trained counterpart, despite relying on fewer data points. Explainability analyses further reveal that high-performing models assign greater weight to chemically meaningful bonds that control property variations, underscoring the importance of physically guided data generation. Overall, this work demonstrates that larger datasets do not necessarily yield better GNN predictive models and introduces a simple and general strategy for efficiently constructing high-quality training data in materials informatics. Artificial neural network ; Property (philosophy) ; Predictive modelling ; Predictive power ; Ensemble learning ; Training set ; Task (project management) ; Experimental data ; Material properties ; Graph Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Thermal properties of materials
Authors: Carlos R. Jacinto-Mejía | Loriano Storchi | Giovanni Bistoni
Source: Journal of Chemical Theory and Computation (article, 2025-10-29)
Field: Materials Science — Materials Chemistry

Transferable and Transparent Energy Decomposition-Based Machine Learning Models for Computing Accurate Reaction Energetics We present a transferable, interpretable, and modular machine-learning framework that enhances the accuracy of density functional theory (DFT) reaction energies using physically meaningful energy-decomposition descriptors. Reaction energies computed at the DFT level with standard basis sets are first decomposed into chemically intuitive contributions─such as kinetic and potential energy─which are then used to train a library of linear regression (LR) models. This includes a general-purpose model that reduces mean absolute percentage errors (MAPE) relative to gold standard CCSD(T)/CBS reference values by up to 63% compared to uncorrected DFT across extended benchmark sets. In parallel, a series of specialized LR models provide improved accuracy for specific reaction classes. A random forest (RF) classifier dynamically selects the optimal model for each case, pushing accuracy further and achieving a MAPE reduction of up to 123 percentage points, all while maintaining full model interpretability. In a rigorous out-of-distribution stress test on the WCCR10 data set─containing transition-metal complexes absent from training─both the general LR model and the RF/LR pipeline retain robust performance. Unlike typical neural network models, which often face generalization challenges beyond their training set, our framework maintains stable performance outside its training domain. Artificial neural network ; Generalization ; Modular design ; Random forest ; Mean absolute percentage error ; Benchmark (surveying) ; Density functional theory ; Pipeline (software) ; Classifier (UML) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; CO2 Reduction Techniques and Catalysts
Authors: Bohan Zhang | Biyuan Liu | Penghua Ying | Zherui Chen | Yanzhou Wang | Yonglin Zhang | Haikuan Dong | Jinglei Yang | Zheyong Fan
Source: ArXiv.org (article, 2025-12-25)
Field: Materials Science — Materials Chemistry

Thermal conductivities of monolayer graphene oxide from machine learning molecular dynamics simulations Graphene oxide (GO) exhibits rich chemical heterogeneity that strongly influences its structural, thermal, and mechanical properties, yet quantitatively linking reduction chemistry to heat transport remains challenging. In this work, we develop a machine-learned neuroevolution potential (NEP) trained on an existing density functional theory dataset (\textit{Angew.\ Chem.\ Int.\ Ed.}, \textbf{63} , e202410088 (2024)), achieving reasonable accuracy at a computational cost much lower than the existing machine-learned and empirical potentials. Leveraging this potential, we perform large-scale molecular dynamics (MD) simulations to model the thermal reduction of GO across realistic structural domains. Using the homogeneous nonequilibrium MD method with a proper quantum-statistical correction scheme, we find that reduced GO exhibits strongly suppressed thermal conductivities, ranging from a few to tens of Wm$^{-1}$K$^{-1}$, substantially lower than pristine GO without defects and far below graphene. Moreover, the thermal conductivity of reduced GO increases moderately with increasing OH/O ratio, except at the highest oxidation level (O/C=0.5) where this trend inverts, while decreasing significantly with increasing O/C ratio, a trend strongly correlated with the fraction of recovered graphene-like structures. Our work provides a computationally tractable and predictive atomistic machine learning framework for exploring how chemical structure governs heat transport in heterogeneous carbon materials. Graphene ; Thermal conductivity ; Oxide ; Molecular dynamics ; Reduction (mathematics) ; Work (physics) ; Non-equilibrium thermodynamics ; Thermal Machine Learning in Materials Science ; Thermal properties of materials ; Model Reduction and Neural Networks
Authors: Sanocki, Michal | Zavadlav, Julija
Source: ArXiv.org (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

Generalization of Long-Range Machine Learning Potentials in Complex Chemical Spaces The vastness of chemical space makes generalization a central challenge in the development of machine learning interatomic potentials (MLIPs). While MLIPs could enable large-scale atomistic simulations with near-quantum accuracy, their usefulness is often limited by poor transferability to out-of-distribution samples. Here, we systematically evaluate different MLIP architectures with long-range corrections across diverse chemical spaces and show that such schemes are essential, not only for improving in-distribution performance but, more importantly, for enabling significant gains in transferability to unseen regions of chemical space. To enable a more rigorous benchmarking, we introduce biased train-test splitting strategies, which explicitly test the model performance in significantly different regions of chemical space. Together, our findings highlight the importance of long-range modeling for achieving generalizable MLIPs and provide a framework for diagnosing systematic failures across chemical space. Although we demonstrate our methodology on metal-organic frameworks, it is broadly applicable to other materials, offering insights into the design of more robust and transferable MLIPs. Transferability ; Generalization ; Chemical space ; Space (punctuation) ; Training set ; Probably approximately correct learning Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Nanocluster Synthesis and Applications
Authors: Héctor Medina | Rachel Drake | Carson Farmer
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Spectroscopy

Accelerated Prediction of Molecular Properties for Per- and Polyfluoroalkyl Substances Using Graph Neural Networks with Adjancency-Free Message Passing Artificial neural network ; Computer science ; Message passing ; Graph ; Deep neural networks ; Artificial intelligence ; Theoretical computer science ; Distributed computing Analytical Chemistry and Chromatography ; Synthesis and properties of polymers ; Various Chemistry Research Topics
Authors: Alexis Duval | Eric Robin | Patrick Houizot | Tanguy Rouxel
Source: Acta Materialia (article, 2025-12-29)
Field: Materials Science — Ceramics and Composites

Inverse design of high-performance glasses through an encoder-decoder machine learning approach toward materials discovery: Application to oxynitride glasses Machine learning approaches have shown remarkable potential in elucidating the complex, nonlinear relationships between compositional effects, structural configurations, and macroscopic properties in materials science. Nevertheless, their application within glass science for guiding the inverse design of formulations with properties tailored for high-performance applications remains limited. In the present work, we report the implementation of an autoencoder within glass science. To address scenarios where the dimensionality of the input exceeds that of the output, a case study was selected wherein 34-dimensional formulations were projected into a 4-dimensional latent space through an autoencoder’s encoder. This strategy thereby enabled the prediction of formulations (with up to 34 different elements) from four material parameters (Young’s modulus, hardness, glass transition temperature, and specific mass), whereby an artificial neural network ( ANN ) links these parameters to the latent representation, and the decoder subsequently reconstructs the formulation in its original 34-dimensional form. Two promising candidate compositions generated through this framework, upon synthesis and experimental characterization, yield glasses that rank among the stiffest, hardest yet light large-scale inorganic, non-metallic systems amongst reported literature, exhibiting a unique combination of high Young’s modulus (E = 155 GPa) and hardness (H = 12.2 GPa) at a comparatively low specific mass (ρ = 3.62 g·cm −3 ). These findings emphasize the potential of machine learning methodologies for the systematic quantification and subsequent rationalization of composition-property trends based on physicochemical descriptors, offering novel insights both within and across chemical systems – demonstrated here for oxynitride glasses, where the processing- and content-dependent incidence of nitrogen is revealed. Curse of dimensionality ; Inverse ; Artificial neural network ; Autoencoder ; Inverse problem ; Yield (engineering) ; Parameter space Glass properties and applications ; Metallic Glasses and Amorphous Alloys ; Material Dynamics and Properties
Authors: Ziduo Yang | Yi‐Ming Zhao | Xian Wang | Wei Zhuo | Xiaoqing Liu | Lei Shen
Source: ArXiv.org (preprint, 2025-11-15)
Field: Materials Science — Materials Chemistry

Equivariant Atomic and Lattice Modeling Using Geometric Deep Learning for Crystal Structure Optimization Structure optimization, which yields the relaxed structure (minimum-energy state), is essential for reliable materials property calculations, yet traditional ab initio approaches such as density-functional theory (DFT) are computationally intensive. Machine learning (ML) has emerged to alleviate this bottleneck but suffers from two major limitations: (i) existing models operate mainly on atoms, leaving lattice vectors implicit despite their critical role in structural optimization; and (ii) they often rely on multi-stage, non-end-to-end workflows that are prone to error accumulation. Here, we present E3Relax, an end-to-end equivariant graph neural network that maps an unrelaxed crystal directly to its relaxed structure. E3Relax promotes both atoms and lattice vectors to graph nodes endowed with dual scalar-vector features, enabling unified and symmetry-preserving modeling of atomic displacements and lattice deformations. A layer-wise supervision strategy forces every network depth to make a physically meaningful refinement, mimicking the incremental convergence of DFT while preserving a fully end-to-end pipeline. We evaluate E3Relax on four benchmark datasets and demonstrate that it achieves remarkable accuracy and efficiency. Through DFT validations, we show that the structures predicted by E3Relax are energetically favorable, making them suitable as high-quality initial configurations to accelerate DFT calculations. Equivariant map ; Lattice (music) ; Ab initio ; Bottleneck ; Crystal structure ; Artificial neural network ; Deep learning ; Graph theory Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Graph Neural Networks