jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mehmet Korkmaz
Source: Engineering Applications of Artificial Intelligence (article, 2026-03-21)
Field: Materials Science — Materials Chemistry

Mathematically grounded state of charge estimation via Gershgorin-based feature engineering and ensemble learning from electrochemical impedance spectroscopy data Ensemble learning ; Feature (linguistics) ; State (computer science) ; Charge (physics) ; Dielectric spectroscopy ; Electrical impedance ; Feature engineering ; Ensemble forecasting Machine Learning in Materials Science ; Machine Learning and ELM ; Advanced Memory and Neural Computing
Authors: Minhee Kim | Yong Yang
Source: ArXiv.org (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

Quantifying Uncertainty in Void Swelling Prediction: A Conformal Prediction Framework for Reactor Safety Margins Irradiation-induced void swelling is a critical degradation mechanism for structural materials in nuclear reactors, dictating component operational lifespan and safety. While recent machine learning (ML) approaches have improved the accuracy of swelling rate predictions, they often fail to account for the inherent stochasticity of radiation damage, providing point estimates without rigorous uncertainty quantification. This lack of probabilistic context limits their applications in materials qualification, reactor licensing and risk assessment. In this work, we develop a framework that integrates ensemble ML models with Conformal Prediction (CP) to generate statistically calibrated prediction intervals. Unlike standard error estimation or Bayesian methods that often rely on rigid distributional assumptions, this approach specifically addresses the physical heteroscedasticity of swelling data, where variance transitions from the nucleation-dominated incubation regime to the growth-dominated steady-state regime. We demonstrate that log-transformed conformal prediction inference provides valid empirical coverage consistent with target confidence levels even in sparse data regimes. This framework offers a pathway to replace overly conservative upper-bound curves with Probabilistic Risk Assessment (PRA) tools for high-dose reactor core internals. Uncertainty quantification ; Probabilistic logic ; Bayesian probability ; Inference ; Context (archaeology) ; Bayesian inference ; Conformal map ; Point estimation Machine Learning in Materials Science ; Nuclear Materials and Properties ; Model Reduction and Neural Networks
Authors: QI Dongli | Zhao Yu | Peng Peijin | Wu Yiyao | Yuan Yu | Yang Yaguang | An Guangtao | Wang Yajing
Source: Journal of Pharmaceutical Innovation (article, 2026-03-31)
Field: Chemistry — Analytical Chemistry

Application of Machine Learning in Predicting Critical Granule Quality Attributes for Modeling of Relationship Between the Properties of Natural Medicine and Process Attributes Support vector machine ; Hyperparameter ; Multilayer perceptron ; Random forest ; AdaBoost ; Artificial neural network ; Boosting (machine learning) ; Dimensionality reduction ; Hyperparameter optimization Spectroscopy and Chemometric Analyses ; Drug Solubulity and Delivery Systems ; Computational Drug Discovery Methods
Authors: Mengyin Tian | Y. M. | H. C. Zang
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2026-03-10)
Field: Chemistry — Analytical Chemistry

Research on data-driven rapid nondestructive quality evaluation method, Calculus Bovis as an example Quality assurance ; Fusion ; Quality (philosophy) ; Sensor fusion ; Value (mathematics) Spectroscopy and Chemometric Analyses ; Cell Image Analysis Techniques ; Remote-Sensing Image Classification
Authors: Brian H. Lee | Chunyu Li | Aidan D. Pantoya | James P. Larentzos | J. William Brennan | Alejandro Strachan
Source: ArXiv.org (article, 2026-01-05)
Field: Materials Science — Materials Chemistry

Multi-Fidelity Predictive Model for Shock Response of Energetic Materials Using Conditional U-Net Mapping microstructure to properties is central to materials science. Perhaps most famously, the Hall-Petch relationship relates average grain size to strength. More challenging has been deriving relationships for properties that depend on subtle microstructural features and not average properties. One such example is the initiation of energetic materials under dynamical loading, dominated by energy localization on microstructural features such as pores, cracks, and interfaces. We propose a conditional convolutional neural network to predict the shock-induced temperature field as a function of shock strength, for a wide range of microstructures, and obtained via two different simulation methods. The proposed model, denoted MISTnet2, significantly extends prior work that was limited to a single shock strength, model, and type of microstructure. MISTnet2 can contribute to bridging atomistics with coarse-grain simulations and enable first principles predictions of detonation initiation and safety of this class of materials. Bridging (networking) ; Shock (circulatory) ; Detonation ; Work (physics) ; Function (biology) ; Energetic material ; Field (mathematics) Machine Learning in Materials Science ; Energetic Materials and Combustion ; High-pressure geophysics and materials
Authors: Ata Madanchi | Lena Simine
Source: The Journal of Chemical Physics (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

MAPping dynamic heterogeneity in supercooled glass-formers A central question in glass physics is whether dynamic heterogeneity of supercooled liquids can be inferred from static structure. Successful models based on supervised and unsupervised machine-learning predict mobility from particle positions but either require labels in training and/or do not offer simple interpretation. Here, we propose to diagnose dynamic heterogeneity using an autoregressive generative model called MAP that learns probabilities of particle configurations conditioned on fragmented local environments from molecular-dynamics data. We verify that in its generative capacity, MAP reproduces key benchmarks for the supercooled Kob-Andersen binary system, such as the relevant pair-correlation functions and the point-to-set distance temperature scaling. We then explore the behavior of the MAP-derived reaction coordinate Ω and find that its modulations coincide with dynamic excitations. We tune Ω into a hypersensitive regime and find that it reports more broadly on local dynamic events, which we characterize as concurrent nearby excitations. This unsupervised-learning signal links rare events to atypical states of fragmented local environments, and it offers a new intuitive way to characterize and explore the dynamic heterogeneity in supercooled glass-formers. Supercooling ; Simple (philosophy) ; Binary number ; Generative model ; Autoregressive model ; Generative grammar ; Dynamical heterogeneity Material Dynamics and Properties ; Advanced Physical and Chemical Molecular Interactions ; Machine Learning in Materials Science
Authors: Kourosh Arasteh | Steven Magana-Zook | Colin Ponce | Roald Leif | A. Carsin Vu | Mark Dreyer | Brian Mayer | Audrey M. Williams | Carolyn Fisher
Source: Analytical Methods (article, 2026-01-01)
Field: Chemistry — Spectroscopy

Random forest models accurately classify synthetic opioids using high-dimensionality mass spectrometry datasets Detection of novel threat agents presents several challenges, a principle one being the development of untargeted methods to screen an increasing number of threat chemicals whose exact structures are unknown. With the use of Machine Learning (ML) tools, we can guide the development of analytical methods for broad-spectrum detection of unbounded threat chemical families in complex mixtures. Toward this goal, we used nominal mass and high-resolution mass spectrometry data for hundreds of synthetic opioids and non-opioid compounds. We tested two ML techniques, logistic regression and random forest, to develop models towards a practical, implementable method for opioid detection. We found that of these tested ML methods, random forest models resulted in the highest validation accuracy (95+%) for both nominal mass and high-resolution classification of opioids <i>versus</i> non-opioids, with low false positive and false negative rates. The RF models were then used to successfully predict the classification of 10 compounds-five opioids and five non-opioids not part of the training and validation analysis. This application of ML is a critical step towards the development of field-deployable nominal mass spectrometers with ML-driven analyses for classification of emergent threats. Random forest ; Logistic regression ; Mass spectrometry ; Training set ; Pattern recognition (psychology) ; Regression Mass Spectrometry Techniques and Applications ; Forensic Toxicology and Drug Analysis ; Computational Drug Discovery Methods
Authors: Lahouari Benabou | L. Vivet
Source: Journal of Electronic Packaging (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

From Design of Experiments to Neural Network Models for Predicting Sintered Nano-Silver Joints Degradation Under Thermal Shocks Abstract Sintered materials exhibit complex mechanical behaviours under cyclic loading, necessitating robust models for predicting responses such as dissipated energy, cyclic porosity damage, and stress level. This study integrates Design of Experiments (DoE) alongside neural network approaches to elucidate factor influences and enable accurate predictive modelling. An Optimal Latin-Hypercube sampling in the DoE was used to systematically examine the effects of key factors (initial joint porosity, joint thickness, and maximum loading temperature) and their interactions on the responses, revealing dominant non-linear relationships that inform material optimization.Leveraging the DoE-generated dataset, Feedforward Neural Networks (FFNs) and Radial Basis Function Networks (RBFNs) were compared in multi-output and single-output configurations. The multi-output FFN, optimized with two hidden layers and a balanced training/validation/testing split, underperformed, particularly in mapping cyclic porosity change, highlighting challenges in joint prediction. Switching to three separate single-output FFNs markedly improved accuracy but required optimizing more parameters, escalating computational demands. In contrast, RBFNs excelled, with both single- and multi-output variants yielding strong results. The multi-output RBFN proved the optimal compromise, achieving competitive MSE with far fewer parameters than the single-output FFNs, while minimizing marginal gains from disaggregated models. Using the multi-output RBFN, joint response mappings were finally successfully represented via contour plots, offering insights into factor variations for enhanced sintered material design. These findings underscore RBFNs' efficiency for multi-output tasks and joint properties optimization under thermal fatigue. Joint (building) ; Artificial neural network ; Thermal ; Porosity ; Degradation (telecommunications) ; Stress (linguistics) ; Design of experiments ; Radial basis function ; Feed forward Machine Learning in Materials Science ; Powder Metallurgy Techniques and Materials ; Injection Molding Process and Properties
Authors: Cheuk Hin Ho | Cas van der Oord | James P. Darby | Theo Keane | Raz L. Benson | Cristian Rebolledo Espinoza | Rutvij Pankaj Kulkarni | Elina Spinu | Michail Papanikolaou | Richard Tomsett | Robert M. Forrest | Jonathan J. Bean | Gábor Csányi | Christoph Ortner
Source: arXiv (Cornell University) (article, 2026-04-09)
Field: Materials Science — Materials Chemistry

Equivariant Many-body Message Passing Interatomic Potentials for Magnetic Materials Magnetism governs key properties of materials used in energy, data storage, and spintronic technologies, yet its complex coupling to lattice and electronic degrees of freedom challenges conventional first-principles approaches. We introduce an equivariant message-passing graph neural network that embeds atomic magnetic moments as explicit degrees of freedom, enabling the learning of magnetic interactions beyond collinear approximations. The model learns physically consistent and transferable representations of magnetic behaviour and can incorporate spin-orbit coupling, achieving near density-functional-theory accuracy with strong data efficiency across diverse magnetic systems by fine-tuning from a pre-trained model. Applications to structural transformations, finite-temperature magnetic phenomena, and materials screening for strongly spin-orbit coupled materials demonstrate transferable magnetic behaviour, establishing a practical foundation for data-driven, high-throughput discovery of complex magnetic materials. Magnetism ; Spintronics ; Inductive coupling ; Magnetic moment ; Coupling (piping) ; Degrees of freedom (physics and chemistry) ; Lattice (music) ; Magnet ; Magnetic domain Machine Learning in Materials Science ; Quantum many-body systems ; Topological Materials and Phenomena
Authors: Shalini Kannan | T. S. Kannan | Muruga Boopathi Muthupandi | Karthi Marimuthu
Source: Nordic Pulp & Paper Research Journal (article, 2026-02-06)
Field: Materials Science — Polymers and Plastics

AI-driven development of biodegradable flushable wet wipes from upcycled household organic waste: formulation, performance, and predictive optimization Abstract Innovative, sustainable alternatives are needed in light of the growing environmental concerns surrounding synthetic single-use wipes and the growing amount of organic waste generated in households. This study describes the creation and refinement of biodegradable flushable wet wipes made from recycled Fruit and Vegetable Waste (FVW) combined with naturally occurring antimicrobials such orange peel oil and Coleus amboinicus . Thermochemical pulping, sheet casting, and bio-lotion impregnation were used to create the wipes, which produced a plant-based matrix with encouraging structural and sanitary qualities. The wipes thermal stability, structural integrity, and moisture permeability were validated by physicochemical characterisation using FTIR, DSC, TGA, and WVTR analysis, making them appropriate for flushable use. Significant inhibitory zones against Pseudomonas aeruginosa , Escherichia coli , and Staphylococcus aureus were found in antimicrobial testing, exceeding commercial wipes. In order to forecast important performance outcomes based on material composition, a machine learning framework was also put into place. Fibre and phenolic content emerged as the main predictors for WVTR and antibacterial efficacy, while Random Forest models produced high predictive accuracy (R 2 &gt;0.90). In order to enable scalable, low-impact bio-product creation, effort combines green chemistry, and artificial intelligence in a circular, data-driven approach to the production of eco-conscious hygiene products. Orange (colour) ; Moisture ; Biodegradable waste ; Municipal solid waste ; Water content ; Predictive modelling Natural Fiber Reinforced Composites ; Food Drying and Modeling ; Lignin and Wood Chemistry
Authors: Emmanuel D. Dike | Lucas Ayres | Jorge Barroso | Justin T. Furgala | Daniel C. Whitehead | Carlos D. Garcia
Source: Journal of Food Composition and Analysis (article, 2026-04-01)
Field: Chemistry — Organic Chemistry

Machine Learning-Based Inference of Oxidative Stability of Vegetable Oils The induction period (IP, obtained via standard conditions and a commercial instrument) is nowadays one of the most common tools to investigate oxidative stability in lipid samples. Despite the advantages and widespread use of this methodology in industry, the development of algorithms to predict IP values has been (thus far) impaired by the scarcity of data linking the composition of the sample with the corresponding IP value. Addressing this need and considering the persistent difficulties in bridging experimental and theoretical approaches related to edible fats, this paper reports on the development of a machine learning approach based on extreme gradient boosting (XGBoost) that was trained using a biodiesel database and then fine-tuned using a vegetable oils database, in both cases linking composition their corresponding stability (via rancimat). The resulting model was able to predict accelerated oxidative stability of vegetable oils with a mean average error of only 1.37 h, one of the lowest ever reported. Beyond demonstrating the ability of our model to provide meaningful predictions, this report highlights the importance of incorporating oxidation-related chemical descriptors and the utility of using associated databases and transfer learning to support the development of accurate machine learning models. • Machine learning approach to predict rancimat stability of vegetable oils • Transfer learning applied to fine tune a primary model trained on biodiesel data • Fatty acid composition used to make predictions of olive oil oxidative stability with a mean average error of only 1.37 h • Results underscore the importance of transfer learning to overcome data scarcity Oxidative phosphorylation ; Inference ; Edible oil ; Vegetable oil ; Stability (learning theory) Edible Oils Quality and Analysis ; Lubricants and Their Additives ; Polymer composites and self-healing
Authors: V. Vignesh | S. Sathees Kumar | A. M. Arun Mohan | I. Vijay Arasu | N. Nagaprasad | R. Krishnaraj
Source: Scientific Reports (article, 2026-01-30)
Field: Materials Science — Polymers and Plastics

Machine learning-based estimation and optimization of phoenix Dactylifera Seed Powder reinforced vinyl ester bio-composites This study aims to develop a robust machine learning (ML) framework for accurately predicting the mechanical and thermal properties of sustainable vinyl ester composites reinforced with Phoenix Dactylifera Seed Powder (PDSP). To achieve this, multiple ML algorithms such as Linear Regression, Support Vector Machine (SVM), Random Forest and Decision Tree were evaluated to predict the mechanical and thermal using both experimental data generated in-house and data collected from past literature review, Among which the SVM model demonstrated superior predictive accuracy, achieving high R<sup>2</sup> values for tensile strength (0.91), flexural strength (0.83), hardness (0.86), and heat deflection temperature (0.85). The analysis confirms that filler weight percentage is the most influential parameter and that ML can reliably forecast long-term property retention under environmental exposure. Consequently, this data-driven approach significantly reduces reliance on extensive experimental testing and enables optimized design of PDSP/VE composites for practical applications in automotive components and civil engineering structures, thereby facilitating the integration of sustainable materials into performance-based standards. Phoenix dactylifera ; Ultimate tensile strength ; Flexural strength ; Support vector machine ; Vinyl ester ; Random forest ; Deflection (physics) ; Filler (materials) ; Automotive industry Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Epoxy Resin Curing Processes
Authors: Cheuk Hin Ho | Cas van der Oord | James P. Darby | Theo Keane | Raz L. Benson | Cristian Rebolledo Espinoza | Rutvij Pankaj Kulkarni | Elina Spinu | Michail Papanikolaou | Richard Tomsett | Robert M. Forrest | Jonathan J. Bean | Gábor Csányi | Christoph Ortner
Source: arXiv (Cornell University) (preprint, 2026-04-09)
Field: Materials Science — Materials Chemistry

Equivariant Many-body Message Passing Interatomic Potentials for Magnetic Materials Magnetism governs key properties of materials used in energy, data storage, and spintronic technologies, yet its complex coupling to lattice and electronic degrees of freedom challenges conventional first-principles approaches. We introduce an equivariant message-passing graph neural network that embeds atomic magnetic moments as explicit degrees of freedom, enabling the learning of magnetic interactions beyond collinear approximations. The model learns physically consistent and transferable representations of magnetic behaviour and can incorporate spin-orbit coupling, achieving near density-functional-theory accuracy with strong data efficiency across diverse magnetic systems by fine-tuning from a pre-trained model. Applications to structural transformations, finite-temperature magnetic phenomena, and materials screening for strongly spin-orbit coupled materials demonstrate transferable magnetic behaviour, establishing a practical foundation for data-driven, high-throughput discovery of complex magnetic materials. Magnetism ; Spintronics ; Inductive coupling ; Magnetic moment ; Coupling (piping) ; Degrees of freedom (physics and chemistry) ; Lattice (music) ; Magnet ; Magnetic domain Machine Learning in Materials Science ; Quantum many-body systems ; Topological Materials and Phenomena
Authors: Taiyo Maeda | Muhammad Aiman B Musa | Toshio Osada | Shingo Ozaki
Source: Figshare (article, 2026-04-09)
Field: Materials Science — Materials Chemistry

Data-driven approach for rapid prediction of strength scatter in brittle ceramics using deep learning and swarm optimization The intrinsic brittleness and high defect sensitivity of ceramics result in significant strength variability, presenting substantial challenges for structural reliability assessments. Experimental characterization of strength scatter in ceramic components is both time-consuming and costly. Conventional physics-based (forward) analyses can model strength scatter based on microstructural data; these methods are computationally intensive. This study introduces a deep learning – based surrogate model that directly predicts the Weibull distribution parameters of ceramic bending strength from equivalent crack length distributions, achieving substantial reductions in computational cost without compromising predictive accuracy. Additionally, an inverse analysis framework is developed by integrating the surrogate model with intelligent swarm optimization, enabling the estimation of defect distributions from reference strength measurements. The proposed approach demonstrates high accuracy and efficiency, achieving over 200-fold speedup in forward analysis and 18,000-fold in inverse analysis. It facilitates a rapid and reliable evaluation of the relationship between defect distributions and strength scatter as characterized by Weibull parameters. This methodology provides a robust tool for accelerating the design and development of high-performance ceramic materials. Innovative deep learning approach predicts Weibull parameters for ceramic strength, enabling rapid, accurate evaluation of defect-strength relationships and accelerating high-performance ceramic material development Weibull distribution ; Brittleness ; Ceramic ; Reliability (semiconductor) ; Inverse ; Sensitivity (control systems) ; Design of experiments ; Particle swarm optimization Machine Learning in Materials Science ; Advanced ceramic materials synthesis ; Composite Material Mechanics