jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xinliang Yu
Source: Chemical Physics (article, 2026-02-15)
Field: Materials Science — Materials Chemistry

Standardized repeating unit definition enhances deep learning prediction of glass transition temperatures in polyimides Convolutional neural network ; Deep learning ; Test set ; Polyimide ; Artificial neural network ; Multilayer perceptron ; Ambiguity ; Glass transition ; Mean squared error Machine Learning in Materials Science ; Synthesis and properties of polymers ; Thermal properties of materials
Authors: James R. Chelikowsky | Cai-Zhuang Wang | Timothy Liao | Zhao Tang | Qi Zhang | Weiyi Xia
Source: Journal of Magnetism and Magnetic Materials (article, 2026-03-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

AI-driven and quantum-informed searches for rare-earth free magnets: Applications to Fe–Co–X (X=B, C, P, Si, S) compounds Magnetocrystalline anisotropy ; Magnet ; Ferromagnetism ; Curie temperature ; Dysprosium ; Ternary operation ; Density functional theory ; Intermetallic ; Anisotropy Magnetic Properties of Alloys ; Heusler alloys: electronic and magnetic properties ; Magnetic properties of thin films
Authors: Yangyang Zhou | Chun Guo | Zhiyong Zhou
Source: ACS Applied Materials & Interfaces (article, 2026-01-09)
Field: Materials Science — Materials Chemistry

Machine-Learning-Assisted Composition Design of High-Entropy CaBi <sub>4</sub> Ti <sub>4</sub> O <sub>15</sub> -Based Piezoceramics with Ultrahigh Piezoelectricity High-entropy strategy presents a promising approach for enhancing the piezoelectric properties of bismuth layer-structured ferroelectrics, yet the vast compositional space poses significant challenges for traditional trial-and-error methods. In this work, a machine learning (ML)-assisted design strategy is proposed to accelerate the discovery of high-performance, high-entropy CaBi<sub>4</sub>Ti<sub>4</sub>O<sub>15</sub> ceramics. Based on a small data set containing 114 compositions, seven key elements closely related to the piezoelectric coefficient (<i>d</i><sub>33</sub>) were identified through feature engineering. Using the eXtreme Gradient Boosting regression model, the <i>d</i><sub>33</sub> values of 22500 candidate compositions were predicted, thereby guiding the synthesis of Ca<sub>1-<i>x</i></sub>(Na<sub>1/6</sub>Bi<sub>1/6</sub>Li<sub>1/6</sub>Ce<sub>1/6</sub>K<sub>1/6</sub>Bi<sub>1/6</sub>)<sub><i>x</i></sub>Bi<sub>4</sub>Ti<sub>3.9</sub>(Nb<sub>1/2</sub>Mn<sub>1/2</sub>)<sub>0.1</sub>O<sub>15</sub> ceramics. The optimal composition (<i>x</i> = 0.3) achieved a record-high <i>d</i><sub>33</sub> of 28.1 pC/N, along with outstanding temperature stability (93% retention after 500 °C annealing). Structural characterization indicates that the high-entropy-induced chemical disorder promotes the formation and switching of nanodomains. On the other hand, the significant lattice distortion it induces enhances the spontaneous polarization, with both aspects synergistically improving the piezoelectric performance. This work demonstrates the effectiveness of ML in navigating complex composition-performance relationships in high-entropy piezoceramics and provides a viable route for designing piezoelectric materials with superior integrated performance for high-temperature applications. Piezoelectricity ; Bismuth ; Characterization (materials science) ; Ceramic ; Boosting (machine learning) ; Piezoelectric coefficient ; Poling Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices ; Machine Learning in Materials Science
Authors: Ryotaro Sahashi | Po-Yen Chen | Teruyasu Mizoguchi
Source: arXiv (Cornell University) (preprint, 2026-03-16)
Field: Materials Science — Materials Chemistry

Decoupling structural and bonding effects on ferroelectric switching in ScAlN via molecular dynamics under an applied electric field ScxAl1-xN has emerged as a promising wurtzite-type ferroelectric material, where increasing the Sc composition reduces both the coercive field (Ec) and remanent polarization (Pr). This composition-dependent behavior is physically attributed to two simultaneous changes: the increase in the internal structural parameter u (structural effect) and the weakening of bond strength (bonding effect). Because these factors are strongly coupled in experiments, their individual contributions to ferroelectric switching remain unclear. In this study, we systematically decoupled these effects using machine-learning force field-based molecular dynamics (MD) simulations under an applied electric field. By artificially tuning u via in-plane strain at a fixed composition, we demonstrated that Pr is determined exclusively by the structural effect, exhibiting a universal linear dependence regardless of the composition. In contrast, Ec deviated from this structural trend, implying an additional compositional contribution. To isolate this, we evaluated configurations with identical u but varying Sc compositions; Pr remained constant, whereas Ec systematically decreased due to bond weakening. Furthermore, static nudged elastic band (NEB) calculations revealed that the static switching barrier depends solely on u, failing to explicitly capture the bonding effect on Ec. These results establish that while Pr is governed strictly by the structural effect, Ec is determined by a superposition of structural and bonding effects. Our findings highlight the necessity of dynamic MD simulations for fully understanding ferroelectric switching in compositionally tunable materials. Ferroelectricity ; Superposition principle ; Electric field ; Molecular dynamics ; Decoupling (probability) ; Polarization (electrochemistry) Ferroelectric and Piezoelectric Materials ; Advanced Sensor and Energy Harvesting Materials ; Ferroelectric and Negative Capacitance Devices
Authors: Massimo Martinelli | Elena Tomassi | Nafiou Arouna | Morena Gabriele | Laryssa Perez Fabbri | Luisa Pozzo | Castiglioni, Bianca | Cremonesi, Paola | Conte, Giuseppe | Davide Moroni | Laura Pucci
Source: arXiv (Cornell University) (article, 2026-02-12)
Field: Chemistry — Analytical Chemistry

Visible and Hyperspectral Imaging for Quality Assessment of Milk: Property Characterisation and Identification Rapid and non-destructive assessment of milk quality is crucial to ensuring both nutritional value and food safety. In this study, we investigated the potential of visible and hyperspectral imaging as cost-effective and quick-response alternatives to conventional chemical analyses for characterizing key properties of cowś milk. A total of 52 milk samples were analysed to determine their biochemical composition (polyphenols, antioxidant capacity, and fatty acids) using spectrophotometer methods and standard gas-liquid and high-performance liquid chromatography (GLC/HPLC). Concurrently, visible (RGB) images were captured using a standard smartphone, and hyperspectral data were acquired in the near-infrared range. A comprehensive analytical framework, including eleven different machine learning algorithms, was employed to correlate imaging features with biochemical measurements. Analysis of visible images accurately distinguished between fresh samples and those stored for 12 days (100 percent accuracy) and achieved perfect discrimination between antibiotic-treated and untreated groups (100 percent accuracy). Moreover, image-derived features enabled perfect prediction of the polyphenols content and the antioxidant capacity using an XGBoost model. Hyperspectral imaging further achieved classification accuracies exceeding 95 percent for several individual fatty acids and 94.8 percent for treatment groups using a Random Forest model. These findings demonstrate that both visible and hyperspectral imaging, when coupled with machine learning, are powerful, non-invasive tools for the rapid assessment of milkś chemical and nutritional profiles, highlighting the strong potential of imaging-based approaches for milk quality assessment. Hyperspectral imaging ; Quality assessment ; Pattern recognition (psychology) ; Identification (biology) ; Chemical imaging ; Polyphenol ; Random forest Spectroscopy and Chemometric Analyses ; Milk Quality and Mastitis in Dairy Cows ; Nutritional Studies and Diet
Authors: Steven B. Hancock | Jason P. Terry | D. P. Landau | Yohannes Habtegiorgis Abate
Source: The Journal of Physical Chemistry C (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Metropolis Sampling of Moiré Superlattices of Transition Metal Dichalcogenide Models We introduce a classical model that enables the investigation of the effects of interlayer coupling parameters on moiré structures in twisted bilayer van der Waals (vdWs) materials. Metropolis sampling with nontraditional boundary conditions and machine learning techniques are used to produce and analyze results. This model employs a parametrization of the Stillinger-Weber potential appropriate for describing the intralayer interactions of transition metal dichalcogenides (TMDCs), and a simple Lennard-Jones potential for the interlayer interactions. To generate equilibrium configurations, we use Metropolis Monte Carlo with unique fluctuating boundary conditions to minimize boundary effects. These methods allow us to recreate superlattices seen in experiment and ab initio calculations.1 Additionally, we sample different values of the van der Waals radii of the interlayer interaction σMo–S′ and σS–S′ to explore how competition of interaction affects morphology of periodic domains seen in reconstructed moiré TMDCs. We then employ simple machine learning procedures to classify and analyze a variety of simulated structures to further extract model parameters that drive domain formation. Finally, we apply our method to the simulation of heterobilayers with different lattice constants, which are difficult to accommodate via experiment or first-principles calculations. In so doing we find both continuous and abrupt differences in moiré superlattice spacing. These generic models, with appropriate boundary conditions, hold great potential for exploring moiré and other reconfigurable atomic scale phenomena in quantum materials. van der Waals force ; Superlattice ; Monte Carlo method ; Parametrization (atmospheric modeling) ; Lattice (music) ; Periodic boundary conditions ; Boundary value problem ; Boundary (topology) ; Interatomic potential 2D Materials and Applications ; Machine Learning in Materials Science ; Advanced Chemical Physics Studies
Authors: Massimo Martinelli | Elena Tomassi | Nafiou Arouna | Morena Gabriele | Laryssa Perez Fabbri | Luisa Pozzo | Castiglioni, Bianca | Cremonesi, Paola | Conte, Giuseppe | Davide Moroni | Laura Pucci
Source: Open MIND (preprint, 2026-02-12)
Field: Chemistry — Analytical Chemistry

Visible and Hyperspectral Imaging for Quality Assessment of Milk: Property Characterisation and Identification Rapid and non-destructive assessment of milk quality is crucial to ensuring both nutritional value and food safety. In this study, we investigated the potential of visible and hyperspectral imaging as cost-effective and quick-response alternatives to conventional chemical analyses for characterizing key properties of cowś milk. A total of 52 milk samples were analysed to determine their biochemical composition (polyphenols, antioxidant capacity, and fatty acids) using spectrophotometer methods and standard gas-liquid and high-performance liquid chromatography (GLC/HPLC). Concurrently, visible (RGB) images were captured using a standard smartphone, and hyperspectral data were acquired in the near-infrared range. A comprehensive analytical framework, including eleven different machine learning algorithms, was employed to correlate imaging features with biochemical measurements. Analysis of visible images accurately distinguished between fresh samples and those stored for 12 days (100 percent accuracy) and achieved perfect discrimination between antibiotic-treated and untreated groups (100 percent accuracy). Moreover, image-derived features enabled perfect prediction of the polyphenols content and the antioxidant capacity using an XGBoost model. Hyperspectral imaging further achieved classification accuracies exceeding 95 percent for several individual fatty acids and 94.8 percent for treatment groups using a Random Forest model. These findings demonstrate that both visible and hyperspectral imaging, when coupled with machine learning, are powerful, non-invasive tools for the rapid assessment of milkś chemical and nutritional profiles, highlighting the strong potential of imaging-based approaches for milk quality assessment. Hyperspectral imaging ; Quality assessment ; Pattern recognition (psychology) ; Identification (biology) ; Chemical imaging ; Polyphenol ; Random forest Spectroscopy and Chemometric Analyses ; Milk Quality and Mastitis in Dairy Cows ; Nutritional Studies and Diet
Authors: Shuai Lyu | Yu-Fan Yao | Yan-Hui Sun | Wen-Tiao Wu | Wei Hu
Source: Journal of Computer Science and Technology (article, 2026-01-21)
Field: Materials Science — Materials Chemistry

Model Knowledge Transfer for Ab Initio Molecular Dynamics Simulations Molecular dynamics ; Benchmark (surveying) ; Overhead (engineering) ; Scalability ; Ab initio ; Reduction (mathematics) ; Density functional theory ; Computation Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: Igor Wachter | Veronika Kvorková | Peter Rantuch | Tomáš Štefko | JOZEF MARTINKA | Lenka Blinová | Oisik Das | Elif Kaynak-Uraz | Chia-Feng Lin
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Polymers and Plastics

Catalytic Heat Treatment of Wood for Enhanced Fire and Leach Resistance Thermogravimetric analysis ; Limiting oxygen index ; Moisture ; Char ; Smoke ; Catalysis ; Pyrolysis ; Combustion ; Raw material Flame retardant materials and properties ; Fire dynamics and safety research ; Wood Treatment and Properties
Authors: Soumyadip Hazra | Sraboni Dey | Arijit Kayal | Narendra Shah | Renjith Nadarajan | Joy Mitra
Source: arXiv (Cornell University) (preprint, 2026-03-31)
Field: Materials Science — Materials Chemistry

Machine Learning Assisted Reconstruction of Local Electronic Structure of Non-Uniformly Strained MoS2 Wrinkles and nanobubbles are an integral and often unavoidable part of integrating 2D van der Waals semiconductors into actual device architectures. Despite their ubiquitous nature, quantitative correlation between such spatially non-uniform strain and modifications to the local electronic structure remains challenging. Here, density functional theory is combined with a recurrent neural network to reconstruct the local electronic structure of monolayer MoS2 from strain maps derived from atomic force microscopy (AFM) topography and Raman spectral maps. The analysis reveals that biaxial bending induced strain is significantly more effective than both uniaxial bending or in-plane strain in modifying electronic and dielectric properties. A ~ 0.35% strain induced by biaxial bending results in ~ 22% reduction in band gap and ~ 7% increase in dielectric constant, compared to a ~ 5% reduction in band gap and ~ 1% increase in dielectric constant under comparable uniaxial bending. The modified band structure reveals band edge states that concentrate charge in regions of high curvature or strain. While conductive AFM measurements indicate increased local conductance (carrier density) at wrinkles and nanobubbles, the spatial band gap maps predicted by the model are validated against experimental photoluminescence peak energy maps. The results indicate that strained features like wrinkles and nanobubbles commonly present in real devices influence the band gap, carrier distribution, and dielectric response, which favourably affects electrical transport in such systems. The framework developed here can be readily extended to other 2D materials and heterostructures, offering a computationally efficient route for studying and exploiting strain effects. Dielectric ; Band gap ; Electronic structure ; Semiconductor ; Bending ; van der Waals force ; Band bending ; Raman spectroscopy ; Electronic band structure 2D Materials and Applications ; Graphene research and applications ; Surface and Thin Film Phenomena
Authors: Kaili Liu | Shiling Li | W. Shi | Zhen Guo | Xijun Shao | Yemin Guo | Jicheng Zhao | Xun Sun | N. Khujamshukurov | Fangling Du
Source: Sensors (article, 2026-03-14)
Field: Chemistry — Analytical Chemistry

Early Detection and Classification of Gibberella Zeae Contamination in Maize Kernels Using SWIR Hyperspectral Imaging and Machine Learning Early-stage fungal contamination in maize kernels is difficult to identify visually and it can cause severe quality and safety risks during storage and transportation. Short-wave infrared (SWIR) hyperspectral imaging offers a rapid, non-destructive approach by capturing chemical information related to water, proteins, and lipids. This study investigates the early detection and classification of <i>Gibberella zeae</i> contamination in maize kernels using SWIR hyperspectral imaging combined with machine learning. Two maize varieties were artificially inoculated and cultured under controlled conditions, followed by hyperspectral data collection over six contamination stages. Various preprocessing techniques including standard normal variate (SNV), second derivative (SD), multiplicative scatter correction (MSC), and derivatives were evaluated to enhance data quality. Feature wavelength selection was performed using successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE), significantly reducing redundancy and improving classification performance. Multiple models, including linear discriminant analysis (LDA), multilayer perceptron (MLP), support vector machine (SVM), a convolutional neural network (CNN), long short-term memory (LSTM) network, and a hybrid architecture Transformer that integrated a CNN, a LSTM network, and a Transformer (abbreviated as CLT), were constructed for both binary (healthy vs. contaminated) and multiclass classification tasks. Specifically, the multiclass task consisted of six contamination stages corresponding to contamination time from Day 0 to Day 5. The best binary classification task accuracy of 100% was achieved using SNV-preprocessed data with the MLP model. For multiclass classification task, the SD-preprocessed LDA model reached a test accuracy of 92.56%. Combined with appropriate preprocessing, feature selection and modeling, these results demonstrate that hyperspectral imaging is a powerful tool for the non-destructive, early-stage identification of fungal contamination in maize kernels, offering strong support for food safety and quality monitoring. Hyperspectral imaging ; Pattern recognition (psychology) ; Support vector machine ; Feature selection ; Linear discriminant analysis ; Preprocessor ; Random forest ; Convolutional neural network ; VNIR Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Gregor Häfner | M. Busch | Adel Dabah | Jiayu Xie | Niklas Blagojevic | Shibananda Das | Sonja Happ | Simon Pickartz | Larissa Großmann | Maryam Radjabian | Volker Abetz | Christian J. Cyron | Andreas Herten | Roland C. Aydin | M. Müller
Source: ChemRxiv (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

Concurrently coupling particle and continuum simulation to study block copolymer membrane fabrication We present a concurrent multiscale simulation framework for membrane fabrication from a block copolymer solution via Self-assembly and Nonsolvent-Induced Phase Separation (SNIPS), combining simulations of a soft, coarse-grained particle model with the continuum Uneyama-Doi model (UDM). The compute-intensive particle model provides a molecularly resolved description of micro-and macrophase separation, including thermal fluctuations, while the computationally efficient continuum model captures the process-driven self-assembly of nonequilibrium membrane morphologies on large length and time scales. Central to the approach is a Machine Learning (ML)-guided adaptive coupling strategy, implemented through a coordinator library, which predicts the evolving spatiotemporal subdomain where high-fidelity particle simulations are required and dynamically allocates computational resources accordingly. The two complementary models are concurrently coupled through a consistent exchange of the solvents' fluxes, enabling the treatment of spatially inhomogeneous, multicomponent systems with diffusive transport, micro- and macrophase separation, and vitrification. This adaptive strategy enables predictive simulations of membrane formation on experimentally and technologically relevant length and time scales, reaching micrometers and minutes. As an application, we examine the influence of polymer concentration in the initial casting solution. The framework is general and extensible, providing a computational tool for investigating nonequilibrium structure formation in complex multicomponent soft-matter systems. Non-equilibrium thermodynamics ; Membrane ; Coupling (piping) ; Particle (ecology) ; Fabrication ; Polymer ; Thermal ; Molecular dynamics ; Block (permutation group theory) Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science
Authors: Reese E. Jones | Asghar Jadoon | D. Thomas Seidl | Jan Fuhg
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

A physics-augmented neural network framework for modeling and detecting thermo-visco-plastic behavior Artificial neural network ; Work (physics) ; Dissipation ; Phenomenology (philosophy) ; Ranging ; Representation (politics) ; Ordinary differential equation Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Elasticity and Material Modeling
Authors: Soumyadip Hazra | Sraboni Dey | Arijit Kayal | Narendra Shah | Renjith Nadarajan | Joy Mitra
Source: arXiv (Cornell University) (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

Machine Learning Assisted Reconstruction of Local Electronic Structure of Non-Uniformly Strained MoS2 Wrinkles and nanobubbles are an integral and often unavoidable part of integrating 2D van der Waals semiconductors into actual device architectures. Despite their ubiquitous nature, quantitative correlation between such spatially non-uniform strain and modifications to the local electronic structure remains challenging. Here, density functional theory is combined with a recurrent neural network to reconstruct the local electronic structure of monolayer MoS2 from strain maps derived from atomic force microscopy (AFM) topography and Raman spectral maps. The analysis reveals that biaxial bending induced strain is significantly more effective than both uniaxial bending or in-plane strain in modifying electronic and dielectric properties. A ~ 0.35% strain induced by biaxial bending results in ~ 22% reduction in band gap and ~ 7% increase in dielectric constant, compared to a ~ 5% reduction in band gap and ~ 1% increase in dielectric constant under comparable uniaxial bending. The modified band structure reveals band edge states that concentrate charge in regions of high curvature or strain. While conductive AFM measurements indicate increased local conductance (carrier density) at wrinkles and nanobubbles, the spatial band gap maps predicted by the model are validated against experimental photoluminescence peak energy maps. The results indicate that strained features like wrinkles and nanobubbles commonly present in real devices influence the band gap, carrier distribution, and dielectric response, which favourably affects electrical transport in such systems. The framework developed here can be readily extended to other 2D materials and heterostructures, offering a computationally efficient route for studying and exploiting strain effects. Dielectric ; Band gap ; Electronic structure ; Semiconductor ; Bending ; van der Waals force ; Band bending ; Raman spectroscopy ; Electronic band structure 2D Materials and Applications ; Graphene research and applications ; Surface and Thin Film Phenomena