jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dong Fan | Ke Zheng | Hongfang Li | Junjie He | Pengbo Lyu
Source: npj Computational Materials (article, 2025-09-02)
Field: Materials Science — Materials Chemistry

Machine learning augmented design of 2D magnet with planar cyclo-tetranitrogen: ambient thermal stability from quantum to mesoscale Synthesizing polynitrogen compounds that remain stable at ambient conditions is particularly challenging because species beyond the N ≡ N triple bond are inherently unstable. In this study, we combine first-principles calculations with a machine-learning potential (MLP) to investigate the ambient stability of planar cyclo-N4 units embedded in a two-dimensional t-FeN4 monolayer. Our results show that strong Fe–N coordination inhibits N ≡ N reformation, enabling the square cyclo-N4 motif to remain dynamically stable and covalently bonded without high-pressure synthesis. Furthermore, this structure exhibits tunable magnetic anisotropy and a Néel temperature above 600 K, indicating potential for room-temperature spintronic applications. The MLP also enables the simulation of systems comprising over 100,000 atoms, including periodic sheets, nanoribbons, nanomatrices and nanosheets, revealing their structural integrity under thermal fluctuations. These results demonstrate that two-dimensional confinement provides a promising route to stabilize exotic nitrogen topologies, linking quantum-mechanical accuracy with mesoscale modelling for future spin-based technologies. Mesoscale meteorology ; Planar ; Magnet ; Quantum ; Thermal stability ; Stability (learning theory) ; Thermal ; Materials science ; Mechanical engineering ; Aerospace engineering ; Computer science ; Physics ; Engineering ; Meteorology ; Computer graphics (images) ; Quantum mechanics Machine Learning in Materials Science ; Quantum and electron transport phenomena ; Graphene research and applications
Authors: Guillermo Marcos-Ayuso (14290640) | David Quesada (22674955) | María Y. Cobos-Abad (22674958) | Carlos Lendínez (22674961) | Sara Fernández-Moyano (11412839) | Pablo Mauleón (1591954) | Ramón Gómez Arrayás (1591966)
Source: Figshare (article, 2025-11-24)
Field: Chemistry — Organic Chemistry

Decoding Regioselectivityin Cu-Catalyzed Borylationof Alkynes: Insights from Machine Learning and Artificial Intelligence Cu<sup>I</sup>-catalyzed hydroboration of alkynes is a cost-effective route to <i>trans</i>-alkenyl boronates (valuable intermediates for C–C cross-coupling) but controlling regioselectivity in these reactions is challenging. Here, we address this challenge by developing a machine learning model, using high-throughput computational-derived descriptors and a combined experimental/literature data set to predict regioselectivity (expressed as ΔΔ<i>G</i><sup>‡</sup>). The resulting support vector regression (SVR) model achieved high accuracy (cross-validated <i>R</i><sup>2</sup> > 0.8, RMSE ∼0.4–0.6 kcal/mol) and revealed mechanistically relevant trends through feature importance analysis. For example, the model rationalizes why certain <i>N</i>-donor ligands that fail to promote Cu-catalyzed hydroboration can effectively catalyze alkyne hydrosilylation, linking this divergence to differences in geometric ligand descriptors. Moreover, ML-guided screening identified promising ligands that improved hydroboration outcomes (increased yield and regioselectivity), as confirmed in experiments (e.g., enabling reduced catalyst loading without sacrificing selectivity). Overall, this integrated ML strategy offers a powerful tool for understanding and predicting regioselectivity in Cu<sup>I</sup>-mediated reactions and, with appropriate calibration, could be extended to other organocopper systems. Regioselectivity ; Hydroboration ; Support vector machine ; Feature (linguistics) ; Borylation ; Yield (engineering) Organoboron and organosilicon chemistry ; Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions
Authors: Hossein Tahmasbi | Michael Beerbaum | Bartosz Brzoza | Attila Cangi | Thomas D. Kühne
Source: The Journal of Chemical Physics (article, 2025-12-04)
Field: Materials Science — Materials Chemistry

Scalable machine learning model for energy decomposition analysis in aqueous systems Energy decomposition analysis (EDA) based on absolutely localized molecular orbitals provides detailed insights into intermolecular bonding by decomposing the total molecular binding energy into physically meaningful components. Here, we develop a neural network EDA model capable of predicting the electron delocalization energy component of water molecules, which captures the stabilization arising from charge transfer between occupied absolutely localized molecular orbitals of one molecule and the virtual orbitals of another. Exploiting the locality assumption of the electronic structure, our model enables accurate prediction of electron delocalization energies for molecular systems far beyond the size accessible to conventional density functional theory calculations, while maintaining its accuracy. We demonstrate the applicability of our approach by modeling hydration effects in large molecular complexes, specifically in metal-organic frameworks. Delocalized electron ; Atomic orbital ; Localized molecular orbitals ; Density functional theory ; Molecular orbital ; Molecular dynamics ; Electronic structure ; Intermolecular force ; Scalability Machine Learning in Materials Science ; Crystallography and molecular interactions ; Organic and Molecular Conductors Research
Authors: О.Ю. Агапитова | Д.А. Двойников | А. Г. Залазинский | А. В. Нестеренко | В.Г. Титов | В. П. Швейкин
Source: Metallurg (article, 2025-02-25)
Field: Materials Science — Materials Chemistry

ИМИТАЦИОННОЕ МОДЕЛИРОВАНИЕ И СОВЕРШЕНСТВОВАНИЕ КОНСТРУКЦИИ КОМБИНИРОВАННОЙ ПРЕГРАДЫ ПРИ УДАРНОМ НАГРУЖЕНИИ С целью определения направлений совершенствования комбинированной преграды реализовано имитационное моделирование процесса взаимодействия жесткого ударника с комбинированной металлополимерной преградой, для чего с использованием нейросетевого прогнозирования выполнена оптимизация ее конструкции. При этом в качестве параметров выбраны: энергия удара, предел прочности материала пористого слоя, его толщина и относительная плотность до деформации, а в роли критериев – плотность пористого слоя после деформации, перемещение ударника и запреградное усилие. In order to determine the directions of improvement of the combined barrier, simulation modeling of the process of interaction of a rigid impactor with a combined metal–polymer barrier was implemented, for which its design was optimized using neural network forecasting. At the same time, the following parameters were selected impact energy, the tensile strength of the porous layer material, its thickness and relative density before deformation, and the density of the porous layer after deformation, the displacement of the impactor and the retrograde force as criteria. Computer science High-Velocity Impact and Material Behavior ; Geotechnical and Geomechanical Engineering ; Engineering and Environmental Studies
Authors: Pol Febrer | Peter Bjørn Jørgensen | Miguel Pruneda | Alberto Garcı́a | Pablo Ordejón | Arghya Bhowmik
Source: ChemRxiv (preprint, 2025-03-25)
Field: Materials Science — Materials Chemistry

Graph2Mat: Universal graph to matrix conversion for electron density prediction The electron density is a fundamental observable of an atomic system from which all ground-state properties can be computed. As a prediction target for machine learning models, electron density is often represented on a dense real space grid, which is data heavy, or through density fitting approximations. In this work, we show the power of targeting the density matrix, a linear-scaling sparse SE(3) equivariant matrix that encodes the exact density. We introduce Graph2Mat, a universal function for converting molecular graphs into equivariant matrices. We demonstrate how a machine learning model that combines this Graph2Mat approach with state-of-the-art molecular graph representations can accurately predict the density matrix of molecular systems. The models achieve state-of-the-art performance on electron density prediction by matching the accuracy of grid-based methods, while using datasets that are at least one order of magnitude smaller. Accurately predicted electron densities can also accelerate Density Functional Theory (DFT) calculations by reducing the number of self-consistent field (SCF) iterations needed to converge. In this work, we get an average 40% reduction on the number of SCF steps in DFT calculations of QM9 molecules with SIESTA. The novel prediction model also allows for two new and promising measures of uncertainty (total charge error and self-consistency error) that will facilitate its practical usage, e.g. within active learning workflows. These results open the door for many applications using hybrid ML-accelerated DFT methodologies, and uncertainty aware single iteration ab initio molecular dynamics. Graph ; Computer science ; Theoretical computer science Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Jakiya Sultana | Md Mazedur Rahman | Gyula Varga | Szabolcs Szávai | Saiaf Bin Rayhan
Source: Journal of Experimental and Theoretical Analyses (article, 2025-11-11)
Field: Materials Science — Polymers and Plastics

Machine Learning–Based Prediction and Comparison of Numerical and Theoretical Elastic Moduli in Plant Fiber–Based Unidirectional Composite Representative Volume Elements Natural fiber-reinforced unidirectional composites are increasingly adopted in modern industries due to their superior mechanical performance and desirable properties from both material and engineering perspectives. Among various approaches, representative volume element (RVE) generation and analysis is considered one of the most suitable and convenient methods for predicting the elastic moduli of composites. The main aim of this study is to investigate and compare the elastic moduli of natural fiber–reinforced unidirectional composite RVEs using theoretical, numerical, and machine learning models. The numerical predictions in this study were generated using the ANSYS Material Designer tool (version ANSYS 19). A comparison was made between experimental results reported in the literature and different theoretical models, showing high accuracy in validating these numerical outcomes. A dataset comprising 1600 samples was generated from numerical models in combination with the well-known theory of RVE, namely rule of mixture (ROM), to train and test two machine learning algorithms: Random Forest and Linear Regression, with the goal of predicting three major elastic moduli—longitudinal Young’s modulus (E11), in-plane shear modulus (G12), and major Poisson’s ratio (V12). To evaluate model performance, mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R2) were calculated and compared against datasets with and without the theoretical values as input variables. The performance metrics revealed that with the theoretical values, both Linear Regression and Random Forest predict E11, G12, and V12 well, with a maximum MSE of 0.033 for G12 and an R2 score of 0.99 for all cases, suggesting they can predict the mechanical properties with excellent accuracy. However, the Linear Regression model performs poorly when theoretical values are not included in the dataset, while Random Forest is consistent in accuracy with and without theoretical values. Mean squared error ; Elastic modulus ; Finite element method ; Moduli ; Random forest ; Shear modulus ; Linear regression ; Coefficient of determination ; Modulus ; Regression analysis Natural Fiber Reinforced Composites ; Composite Material Mechanics ; Mechanical Behavior of Composites
Authors: Shang Wang | Lidan Lu | Lianqing Zhu
Source: Scientific Reports (article, 2025-07-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Long-wave infrared computational multispectral metasurface and spectral reconstruction method We demonstrate a computational multispectral metasurface employing a 3×3 photonic crystal array architecture that operates across the longwave infrared spectrum (8-11.5 [Formula: see text] m). The designed structure achieves remarkable optical performance with peak transmittance reaching 75.8% and broadband energy utilization efficiency of 41.37%. Notably, the inter-channel transmittance correlation coefficient of 0.17 indicates superior spectral discrimination compared to conventional grating-based systems. We also considered the angular dependence of the array on the incident light. Additionally, to evaluate the spectral reconstruction performance of the transmission spectra under different photonic crystals, a spectral reconstruction deep learning network was constructed with the mean squared error is 2.86[Formula: see text]. This architecture establishes a hardware-algorithm co-design framework for next-generation infrared multispectral systems, demonstrating the potential for integrated superlattice detectors with sub-100 [Formula: see text] m[Formula: see text] pixel pitch, which represents a critical advancement for portable spectroscopic applications. Multispectral image ; Infrared ; Computer science ; Remote sensing ; Optics ; Computer vision ; Geology ; Physics Metamaterials and Metasurfaces Applications ; Optical Wireless Communication Technologies ; Random lasers and scattering media
Authors: V. A. Yudaev | P. V. Balabanov | N. M. Grebennikova | A. G. Divin | A. S. Egorov | D. A. Lyubimova
Source: Kontrol Diagnostika (article, 2025-11-01)
Field: Chemistry — Analytical Chemistry

METHODOLOGY FOR DIAGNOSING HERBICIDE INJURIES TO SUNFLOWER BASED ON THE ANALYSIS OF HYPERSPECTRAL IMAGES OF ITS PLANT TISSUES The paper proposes a technique for diagnosing damage to plant tissues of the Spartak sunflower variety caused by a reaction to the herbicide tribenuron-methyl. Diagnostics is based on the analysis of hyperspectral images of control objects obtained in the wavelength range from 350 to 1002 nm. The technique involves collecting and analyzing hyperspectral images at growth stages. An information-measuring system based on the Cubert X20 Plus camera was used to obtain hyperspectral images. As a result of analyzing the obtained images using the PCA method, informative wavelengths of 502, 670, 718, 770 and 930 nm were determined. Using machine learning methods such as linear discriminant analysis, random forest, logistic regression, k-nearest neighbors (kNN) and SVM, machine learning models were obtained for classifying objects in the camera's field of view by category. To select the optimal model, a complex quality indicator was used, including two criteria ‒ time and quality of classification. An optimal machine learning model obtained on the basis of linear discriminant analysis is determined. Hyperspectral imaging ; Linear discriminant analysis ; Pattern recognition (psychology) ; Sunflower ; Quality (philosophy) ; Principal component analysis ; Machine vision Spectroscopy and Chemometric Analyses ; Advanced Scientific Research Methods ; Smart Agriculture and AI
Authors: Idan Fonea | Amra Peles | Sivan Niv | Goren Gordon | Amir Natan
Source: ArXiv.org (preprint, 2025-11-20)
Field: Materials Science — Materials Chemistry

Black-Box Uncertainty Estimation for Deep Learning Models in Atomistic Simulations We analyze an ensemble-based approach for uncertainty quantification (UQ) in atomistic neural networks. This method generates an epistemic uncertainty signal without requiring changes to the underlying multi-headed regression neural network architecture, making it suitable for sealed or black-box models. We apply this method to molecular systems, specifically sodium (Na) and aluminum (Al), under various temperature conditions. By scaling the uncertainty signal, we account for heteroscedasticity in the data. We demonstrate the robustness of the scaled UQ signal for detecting out-of-distribution (OOD) behavior in several scenarios. This UQ signal also correlates with model convergence during training, providing an additional tool for optimizing the training process. Robustness (evolution) ; Uncertainty quantification ; Deep learning ; Artificial neural network ; Heteroscedasticity ; Deep neural networks ; Convergence (economics) ; Scaling ; SIGNAL (programming language) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Adversarial Robustness in Machine Learning
Authors: Shusen Xing | Wei Lu | Yun Wang | Chun Li
Source:  (article, 2025-08-22)
Field: Materials Science — Materials Chemistry

Construction of Cr4+: YAG Crystal Growth Parameter Optimization Model Integrating Meta-Learning Mechanism Cr4+:YAG crystal is a key material for high-performance lasers, and its performance is highly dependent on growth process parameters. Previous parameter optimization methods rely on empirical trial and error, which is inefficient and difficult to obtain the global optimal solution. To this end, this paper proposes a process parameter optimization model based on a meta-learning framework. First, general parameter optimization knowledge is learned on a meta-training set containing multiple growth scenarios; when faced with a small amount of new experimental data, the model parameters can be quickly fine-tuned to adapt to the new environment. The model adopts a deep network architecture that integrates CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory). The fused features are input to the prediction module, and the optimal process parameter combination is directly output. Experimental results show that meta-learning quickly achieves the optimization goal within a small number of experiments. In terms of optical uniformity, the average value of meta-learning is 0.94. In the real-time adaptability experiment, meta-learning only takes 15.2 seconds to achieve 95.7% of optical uniformity ≥ 0.9, which is better than Bayesian optimization and random forest. The results show that this study demonstrates the great potential of meta-learning in materials science and provides new ideas and technical paths for exploring the optimization of other complex materials in the future. Bayesian optimization ; Process (computing) ; Adaptability ; Set (abstract data type) ; Artificial neural network ; Key (lock) ; Global optimization ; Optimization problem ; Process optimization Machine Learning in Materials Science ; Solid State Laser Technologies ; Welding Techniques and Residual Stresses
Authors: Md Mehedi Hasan (8130036) | Omid Tarkhaneh (10207980) | Sharene D. Bungay (22227368) | Raymond A. Poirier (1617958) | Shahidul M. Islam (818803)
Source: Figshare (article, 2025-09-10)
Field: Materials Science — Materials Chemistry

Predicting HOMO–LUMOGaps Using Hartree–FockCalculated Data and Machine Learning Models The calculation of the highest occupied molecular orbital–lowest unoccupied molecular orbital (HOMO–LUMO) gap for chemical molecules is computationally intensive using quantum mechanics (QM) methods, while experimental determination is often costly and time-consuming. Machine Learning (ML) offers a cost-effective and rapid alternative, enabling efficient predictions of HOMO–LUMO gap values across large data sets without the need for extensive QM computations or experiments. ML models facilitate the screening of diverse molecules, providing valuable insights into complex chemical spaces and integrating seamlessly into high-throughput workflows to prioritize candidates for experimental validation. In this study, we leveraged a data set of HOMO–LUMO gap values for small molecules obtained through Hartree–Fock (HF) calculations and developed ML models to predict HOMO–LUMO energy gaps for organic molecules. Molecular descriptors generated from Simplified Molecular Input Line Entry System (SMILES) representations using RDKit were used as input features to train various regression-based ML models. The data set included 46,717 small molecules with carbon chain number ranging from 1 to 8. Among the tested models, LightGBM regressor, Bidirectional LSTM, CatBoost regressor, and Multilayer Perceptron (MLP) achieved mean absolute error (MAE) values below 0.25 eV. Further improvement was achieved by creating a weighted ensemble model combining the LightGBM regressor, Bidirectional LSTM, and MLP, resulting in a prediction accuracy with an MAE of 0.1660 eV. This ensemble model outperformed others across various data sets, with the LightGBM regressor showing better performance for predicting the HOMO–LUMO gap of saturated linear molecules. SHAP analysis was conducted which identified 20 molecular descriptors critical for accurate predictions. Additionally, the models were empirically adapted to estimate experimental HOMO–LUMO gap values for both small and large molecules (up to carbon number 50), demonstrating their versatility and practical applicability. Molecular descriptor ; Set (abstract data type) ; Workflow ; Ensemble learning ; Perceptron ; Data set ; Ensemble forecasting ; Experimental data ; Training set Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Graph theory and applications
Authors: Shuaiwu Tong | Shizhong Wei | Shuaijun Zhang | Chong Chen | Liujie Xu
Source: Materials & Design (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Study on the tribological performance of Cr-V alloy steel assisted by the random forest algorithm • The relative content of C and Cr/V not only determined the total carbide content but also regulated their formation mechanisms. • A new approach for separating and regulating the multi-scale frictional mechanisms suggests that the friction coefficient is governed by contact mechanics, while the wear rate is determined by the microstructure. • Optimizing the spatial distribution of carbides, rather than merely increasing their overall content, is a key strategy for enhancing the tribological performance of Cr-V alloy steel. The factors influencing the tribological performance of wear-resistant alloy steel are characterized by multiple variables, strong interactions, and nonlinearity. Traditional qualitative empirical analysis has limitations in addressing this complexity. This study introduced an Random Forest prediction model that integrates microstructure analysis, tribological experiments, and machine learning. It quantitatively analyzed the relationship between microstructure and performance in Cr-V alloy steel using feature importance weights generated by the model. The analysis revealed that external loads had the most significant impact on the friction coefficient, with the highest weight (0.246). This effect was attributed to changes in surface contact and plastic deformation mechanisms. VC (weight: 0.204) and Cr 23 C 6 (weight: 0.148) followed, showing a stronger influence compared to other carbides. The wear rate was primarily governed—by approximately 80 %—by the internal microstructure, with Cr 7 C 3 (weight: 0.431) and VC (weight: 0.381) being critical factors. Cr 7 C 3 , as the main wear-resistant phase, directly improved spalling resistance, while VC at lower content helped suppress crack initiation by refining the microstructure and enhancing the interface. The new paradigm based on feature weight-assisted analysis had demonstrated the practical value of machine learning in optimizing tribological performance. Tribology ; Microstructure ; Alloy ; Spall ; Carbide ; Friction coefficient ; Refining (metallurgy) ; Alloy steel Metal Alloys Wear and Properties ; Tribology and Wear Analysis ; Advanced materials and composites