jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Francesco Bertolini | Marco Mariani | Nora Lecis
Source: Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Deep learning-powered system for automated detection and quantification of Vickers indentations Hardness testing is a key procedure in materials science for evaluating mechanical properties and process quality. Traditional Vickers hardness measurement relies on manual identification of indentation diagonals, a process that is slow, subjective, and prone to variability. This work introduces a deep learning-based pipeline for fully automated Vickers hardness measurement, combining instance segmentation via Mask R-CNN with sub-pixel geometric fitting for diagonal extraction. A dataset of 403 micrographs of samples under loads from 10 gf to 2000 gf was assembled and annotated for training and validation. Hyperparameter optimisation was performed using a Taguchi design of experiments, and the final model achieved near-perfect segmentation accuracy (overall AP ≈ 90.5%) on the test set. Measurement accuracy was assessed against manual ground truth, yielding mean relative errors of 1.6-1.9% for the two diagonals, with most cases within 2-3%. These results demonstrate that the proposed system provides robust detection, high metrological precision, and reproducible performance across diverse imaging conditions, paving the way for reliable, high-throughput hardness testing in industrial and research settings. Noise (video) ; Reliability (semiconductor) ; Materials testing Machine Learning in Materials Science ; Metal and Thin Film Mechanics ; Image Processing Techniques and Applications
Authors: Antoine Aerts
Source: The Journal of Chemical Physics (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

Systematically improved potential energy surfaces via sinNN models and sparse grid sampling Accurate, global Potential Energy Surfaces (PESs) expressed in sum-of-products (SOP) form are a prerequisite for efficient high-dimensional quantum dynamics simulations using the multi-configuration time-dependent Hartree method. This work introduces a methodology for constructing such surfaces by combining hierarchical sparse grid sampling with a single-layer neural network using sinusoidal activation functions (sinNN). The sparse grid strategy provides a rigorous, unbiased discretization of the configuration space, enabling systematic improvement of the PES fidelity, where accuracy is strictly controlled by the refinement level, while successfully mitigating the curse of dimensionality. The sinNN fitting approach leverages a trigonometric factorization identity to maintain a compact SOP form, offering superior numerical stability compared to "standard" exponential-based networks for the molecular systems investigated. We validate this framework by refitting an analytical PES for nitrous acid (HONO). The flexibility of the sparse grid methodology is demonstrated through a dual-reference strategy, where grids centered on distinct isomers are merged to eliminate topological bias. This optimized sampling yields a global PES that reproduces fundamental vibrational transition energies for both trans- and cis-HONO with spectroscopic precision (<2.5 cm-1) and high data efficiency. Finally, the methodology is applied to fit potential energies computed via the AI-enhanced quantum mechanical method (AIQM2). The resulting AIQM2-based PES for HONO reproduces experimental vibrational frequencies with a root mean square deviation of ∼16 cm-1, a performance comparable to high-level ab initio methods. The robustness of the approach is further confirmed on larger molecules, formic acid (HCOOH) and carbamic acid (H2NCOOH), establishing the combination of sparse grid sampling and sinNN fitting as a powerful, automated tool for generating topologically sound, spectroscopic-quality potential energy surfaces. Sampling (signal processing) ; Artificial neural network ; Grid ; Potential energy ; Quantum ; Energy (signal processing) ; Work (physics) ; Hyperparameter optimization Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum, superfluid, helium dynamics
Authors: H.R. Song | Yingying Guo | Chenxi Cao | Suwei Jin | Shanshan Zhang | Ruile Pan | Yunfeng Zhou | Mingzhu Yan | Qi Chang
Source: Journal of Food Composition and Analysis (article, 2026-02-28)
Field: Chemistry — Analytical Chemistry

Multi-regional HPLC fingerprinting of Nelumbinis stamen flavonoids in China and machine learning-guided analysis of geographical environment factors Nelumbinis stamen (NS), the dried stamen of the aquatic vegetable lotus, is consumed as a popular herbal tea rich in bioactive flavonoids. Given limited research on NS flavonoid determination, a validated HPLC method was established for the simultaneous quantification of ten major flavonoids: rutin, hyperoside, isoquercitrin, trifolin, narcissin, astragalin, isorhamnetin-3- O -glucoside, kaempferol, quercetin, and isorhamnetin. The flavonoids in NS from 18 geographic origins across China were analyzed. HPLC fingerprints comprising 18 common peaks were established for similarity assessment and clustering, which classified the samples into two distinct clusters. The classification was primarily driven by trifolin, isorhamnetin-3- O -glucoside, astragalin, narcissin, and isoquercitrin. Machine learning (Random Forest / XGBoost) identified the seasonal contrast index, e.g. the discrepancy between summer and annual precipitation / temperature, as the key environmental driver. Correlation analysis confirmed that high June precipitation negatively impacts flavonoid accumulation. The HPLC method enables robust chemical characterization of NS, while the environmental insights provide actionable guidance for its cultivation. • An HPLC method was established for quantifying ten flavonoids in Nelumbinis stamen. • Flavonoid profiling was performed on 18 NS samples from multiple regions in China. • HPLC fingerprints plus chemometrics classified NS samples into two clusters. • Machine learning identified key environmental drivers of geographical variation. • Seasonal contrast index and June precipitation may affect the flavonoid content. Flavonoid ; Chemometrics ; High-performance liquid chromatography ; Stamen ; Principal component analysis Chromatography in Natural Products ; Traditional Chinese Medicine Analysis ; Plant-based Medicinal Research
Authors: Takuya Ehiro
Source: Discover Artificial Intelligence (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

Leveraging SHAP values for superior prediction and efficient Bayesian optimization in material chemistry Abstract In recent years, machine learning has played a crucial role in data-driven material development. This study presents a feature extraction method for enhancing the predictive accuracy of regression models. The proposed method is examined using SHapley Additive exPlanations (SHAP) values, which are commonly used for interpreting black-box models, to determine whether it can transfer the high expressiveness of an accurate regression model to underfitting regression models. It was revealed that SHAP values can capture valuable information for regression analysis, resulting in improved predictive accuracy. The results also underscore the importance of base model selection to extract SHAP values, whose effectiveness is significantly influenced by the base model. Random forest demonstrated superior performance for SHAP-based feature extraction, presumably because of its ability to capture complex non-linear relationships, regardless of the specific SHAP explainer used. In addition, the proposed method can improve material exploration efficiency during Bayesian optimization. Graphical abstract Feature selection ; Random forest ; Regression ; Bayesian probability ; Feature (linguistics) ; Base (topology) ; Linear regression ; Regression analysis ; Selection (genetic algorithm) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Data Classification
Authors: Timothy Zaklama | Max Geier | Liang Fu
Source: arXiv (Cornell University) (preprint, 2026-03-02)
Field: Materials Science — Materials Chemistry

Large Electron Model: A Universal Ground State Predictor We introduce Large Electron Model, a single neural network model that produces variational wavefunctions of interacting electrons over the entire Hamiltonian parameter manifold. Our model employs the Fermi Sets architecture, a universal representation of many-body fermionic wavefunctions, which is further conditioned on Hamiltonian parameter and particle number. On interacting electrons in a two-dimensional harmonic potential, a single trained model accurately predicts the ground state wavefunction while generalizing across unseen coupling strengths and particle-number sectors, producing both accurate real-space charge densities and ground state energies, even up to $50$ particles. Our results establish a foundation model method for material discovery that is grounded in the variational principle, while accurately treating strong electron correlation beyond the capacity of density functional theory. Wave function ; Hamiltonian (control theory) ; Ground state ; Electron ; Representation (politics) ; Electronic correlation ; Electron density ; Harmonic oscillator Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Physical and Chemical Molecular Interactions
Authors: Qixing Tang | Dong Wei | Longbao Liu | Juan Liao | Leizi Jiao | Yachao Liu | Ke Wang | Rui Wang | Daming Dong
Source: Food Control (article, 2026-01-20)
Field: Chemistry — Analytical Chemistry

Fusing cross-source spectral data and component-driven analysis for maize grain quality detection Random forest ; Support vector machine ; Component (thermodynamics) ; Workflow ; Pattern recognition (psychology) ; Generalization ; Quality (philosophy) ; Kernel (algebra) ; Hyperspectral imaging ; Set (abstract data type) Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Soil Geostatistics and Mapping
Authors: Xiaolong He | Haoyan Wei | Wei Hu | Henan Mao | C. T. Wu
Source: ArXiv.org (article, 2026-02-06)
Field: Materials Science — Materials Chemistry

Systematic Performance Assessment of Deep Material Networks for Multiscale Material Modeling Deep Material Networks (DMNs) are structure-preserving, mechanistic machine learning models that embed micromechanical principles into their architectures, enabling strong extrapolation capabilities and significant potential to accelerate multiscale modeling of complex microstructures. A key advantage of these models is that they can be trained exclusively on linear elastic data and then generalized to nonlinear inelastic regimes during online prediction. Despite their growing adoption, systematic evaluations of their performance across the full offline-online pipeline remain limited. This work presents a comprehensive comparative assessment of DMNs with respect to prediction accuracy, computational efficiency, and training robustness. We investigate the effects of offline training choices, including initialization, batch size, training data size, and activation regularization on online generalization performance and uncertainty. The results demonstrate that both prediction error and variance decrease with increasing training data size, while initialization and batch size can significantly influence model performance. Moreover, activation regularization is shown to play a critical role in controlling network complexity and therefore generalization performance. Compared with the original DMN, the rotation-free Interaction-based Material Network (IMN) formulation achieves a 3.4x - 4.7x speed-up in offline training, while maintaining comparable online prediction accuracy and computational efficiency. These findings clarify key trade-offs between model expressivity and efficiency in structure-preserving material networks and provide practical guidance for their deployment in multiscale material modeling. Regularization (linguistics) ; Extrapolation ; Pipeline (software) ; Initialization ; Key (lock) ; Generalization ; Nonlinear system ; Data modeling ; Software deployment ; Weighting Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Block Copolymer Self-Assembly
Authors: Jesper Byggmästar
Source: Open MIND (preprint, 2026-03-04)
Field: Materials Science — Materials Chemistry

Machine-learned Interatomic Potential for Ti$_{n+1}$C$_n$ MXenes: Application to Ion Irradiation Simulations A computationally efficient and accurate machine-learned (ML) interatomic potential is developed for Ti$_{n+1}$C$_n$ MXenes. With a diverse set of structures computed with density functional theory, the trained ML potential demonstrates good accuracy and robustness to a wide range of bond distances and environments, making it a useful tool for molecular dynamics simulations of MXenes subjected to mechanical load or irradiation. The ML potential is applied to simulations of light and heavy ion irradiation, gathering insight into the statistics and probabilities of sputtering, reflection, defect creation, and implantation into Ti$_{n+1}$C$_n$ MXene sheets. The results provide guidelines for defect engineering of MXenes through ion irradiation and implantation. Additionally, the ML potential development provides a landmark recipe for enabling machine-learning-driven atomistic simulations of other MXenes. MXenes ; Molecular dynamics ; Interatomic potential ; Ion ; Robustness (evolution) ; Range (aeronautics) MXene and MAX Phase Materials ; Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Xiaolong He | Haoyan Wei | Wei Hu | Henan Mao | C. T. Wu
Source: Open MIND (preprint, 2026-02-06)
Field: Materials Science — Materials Chemistry

Systematic Performance Assessment of Deep Material Networks for Multiscale Material Modeling Deep Material Networks (DMNs) are structure-preserving, mechanistic machine learning models that embed micromechanical principles into their architectures, enabling strong extrapolation capabilities and significant potential to accelerate multiscale modeling of complex microstructures. A key advantage of these models is that they can be trained exclusively on linear elastic data and then generalized to nonlinear inelastic regimes during online prediction. Despite their growing adoption, systematic evaluations of their performance across the full offline-online pipeline remain limited. This work presents a comprehensive comparative assessment of DMNs with respect to prediction accuracy, computational efficiency, and training robustness. We investigate the effects of offline training choices, including initialization, batch size, training data size, and activation regularization on online generalization performance and uncertainty. The results demonstrate that both prediction error and variance decrease with increasing training data size, while initialization and batch size can significantly influence model performance. Moreover, activation regularization is shown to play a critical role in controlling network complexity and therefore generalization performance. Compared with the original DMN, the rotation-free Interaction-based Material Network (IMN) formulation achieves a 3.4x - 4.7x speed-up in offline training, while maintaining comparable online prediction accuracy and computational efficiency. These findings clarify key trade-offs between model expressivity and efficiency in structure-preserving material networks and provide practical guidance for their deployment in multiscale material modeling. Regularization (linguistics) ; Extrapolation ; Pipeline (software) ; Initialization ; Key (lock) ; Generalization ; Nonlinear system ; Data modeling ; Software deployment ; Weighting Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Block Copolymer Self-Assembly
Authors: I Dewa Made Oka Dharmawan | Jinyi Lee
Source: Measurement (article, 2026-01-05)
Field: Materials Science — Materials Chemistry

Integrated memory-efficient framework for grain-level surface residual stress mapping via optical microscopy Subpixel rendering ; Residual stress ; Speckle pattern ; Finite element method ; Grain boundary ; Residual ; Image resolution ; Stress (linguistics) ; Optical microscope Microstructure and mechanical properties ; Optical measurement and interference techniques ; Fatigue and fracture mechanics
Authors: Namkyung Yoon | Sanghong Kim | Hwangnam Kim
Source: Open MIND (preprint, 2026-01-29)
Field: Chemistry — Analytical Chemistry

Conditional Generative Framework with Peak-Aware Attention for Robust Chemical Detection under Interferences Gas chromatography-mass spectrometry (GC-MS) is a widely used analytical method for chemical substance detection, but measurement reliability tends to deteriorate in the presence of interfering substances. In particular, interfering substances cause nonspecific peaks, residence time shifts, and increased background noise, resulting in reduced sensitivity and false alarms. To overcome these challenges, in this paper, we propose an artificial intelligence discrimination framework based on a peak-aware conditional generative model to improve the reliability of GC-MS measurements under interference conditions. The framework is learned with a novel peak-aware mechanism that highlights the characteristic peaks of GC-MS data, allowing it to generate important spectral features more faithfully. In addition, chemical and solvent information is encoded in a latent vector embedded with it, allowing a conditional generative adversarial neural network (CGAN) to generate a synthetic GC-MS signal consistent with the experimental conditions. This generates an experimental dataset that assumes indirect substance situations in chemical substance data, where acquisition is limited without conducting real experiments. These data are used for the learning of AI-based GC-MS discrimination models to help in accurate chemical substance discrimination. We conduct various quantitative and qualitative evaluations of the generated simulated data to verify the validity of the proposed framework. We also verify how the generative model improves the performance of the AI discrimination framework. Representatively, the proposed method is shown to consistently achieve cosine similarity and Pearson correlation coefficient values above 0.9 while preserving peak number diversity and reducing false alarms in the discrimination model. Pattern recognition (psychology) ; Reliability (semiconductor) ; Similarity (geometry) ; Artificial neural network ; Cosine similarity ; Sensitivity (control systems) ; Generative model ; Generative grammar ; SIGNAL (programming language) Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Vladislav O. Korostelev | Pjotrs Žguns | Konstantin Klyukin
Source: Open MIND (preprint, 2026-01-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hydrogen in Brownmillerite Perovskites: First-Principles Insights into Energetics and Induced Electronic-Magnetic Changes Hydrogen uptake in brownmillerite perovskites A2B2O5 offers an (electro)chemically accessible route to tune functional properties, but mechanistic understanding and design rules for hydrogen-responsive oxides remain limited. Here we employ density functional theory (DFT) to quantify how H absorption affects electronic structure, magnetic exchange, and anisotropy in representative Sr2Fe2O5 and Sr2Co2O5 oxides. We find that hydrogenation introduces a localized electron that stabilizes near the proton, with B-site-dependent preference. The resulting lattice distortions and redistribution of charge density modify exchange coupling and cant the Neel vector, giving rise to weak ferromagnetism. We also show that absorption energies are highly sensitive to proton-electron arrangements and magnetic order, varying by up to 1 eV across different settings. This sensitivity demands consistent treatment of charge localization and spin states, together with careful choice of computational parameters. Extending to a variety of experimentally reported A2B2O5 compositions, we identify candidates with favorable H uptake and uncover a trend linking more favorable absorption to a higher B-site d-electron count. We also demonstrate that the preferred proton absorption site in these materials is governed by local O-O separations and lattice flexibility, which describe the ability of the framework to accommodate proton-induced distortions. Finally, benchmarks of universal machine-learning interatomic potentials reveal uncertainties of about 1 eV for site-resolved absorption energies, motivating descriptor-based surrogate models and targeted DFT validation. Together, these results establish practical design rules for hydrogen-responsive oxides relevant to iono-electronic devices, sensors, and electrically tunable spin functionality. Brownmillerite ; Density functional theory ; Hydrogen ; Lattice (music) ; Absorption (acoustics) ; Anisotropy ; Electronic structure Magnetic and transport properties of perovskites and related materials ; Multiferroics and related materials ; Advanced Condensed Matter Physics
Authors: Vladislav O. Korostelev | Pjotrs Žguns | Konstantin Klyukin
Source: ArXiv.org (article, 2026-01-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hydrogen in Brownmillerite Perovskites: First-Principles Insights into Energetics and Induced Electronic-Magnetic Changes Hydrogen uptake in brownmillerite perovskites A2B2O5 offers an (electro)chemically accessible route to tune functional properties, but mechanistic understanding and design rules for hydrogen-responsive oxides remain limited. Here we employ density functional theory (DFT) to quantify how H absorption affects electronic structure, magnetic exchange, and anisotropy in representative Sr2Fe2O5 and Sr2Co2O5 oxides. We find that hydrogenation introduces a localized electron that stabilizes near the proton, with B-site-dependent preference. The resulting lattice distortions and redistribution of charge density modify exchange coupling and cant the Neel vector, giving rise to weak ferromagnetism. We also show that absorption energies are highly sensitive to proton-electron arrangements and magnetic order, varying by up to 1 eV across different settings. This sensitivity demands consistent treatment of charge localization and spin states, together with careful choice of computational parameters. Extending to a variety of experimentally reported A2B2O5 compositions, we identify candidates with favorable H uptake and uncover a trend linking more favorable absorption to a higher B-site d-electron count. We also demonstrate that the preferred proton absorption site in these materials is governed by local O-O separations and lattice flexibility, which describe the ability of the framework to accommodate proton-induced distortions. Finally, benchmarks of universal machine-learning interatomic potentials reveal uncertainties of about 1 eV for site-resolved absorption energies, motivating descriptor-based surrogate models and targeted DFT validation. Together, these results establish practical design rules for hydrogen-responsive oxides relevant to iono-electronic devices, sensors, and electrically tunable spin functionality. Brownmillerite ; Density functional theory ; Hydrogen ; Lattice (music) ; Absorption (acoustics) ; Anisotropy ; Electronic structure Magnetic and transport properties of perovskites and related materials ; Multiferroics and related materials ; Advanced Condensed Matter Physics