jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pungponhavoan Tep | Marc Bernacki
Source: arXiv (Cornell University) (preprint, 2026-03-25)
Field: Materials Science — Materials Chemistry

Predicting Grain Growth Evolution Under Complex Thermal Profiles with Deep Learning through Thermal Descriptor Modulation Predicting microstructure evolution during thermomechanical treatment is essential for determining the final mechanical properties of a material, yet conventional simulations based on Partial Differential Equations (PDEs) remain computationally expensive. Our prior Deep Learning (DL) framework using Convolutional Long Short-Term Memory (ConvLSTM) has proven effective in accelerating grain growth prediction, though its applicability was limited to constant-temperature or single-rate thermal profiles. As the model was trained exclusively under constant thermal conditions, it cannot account for the thermal history dependence of grain boundary kinetics, fundamentally limiting its applicability to the time-varying thermal profiles characteristic of industrial heat treatment processes. This study extends the previous framework by incorporating Feature-wise Linear Modulation (FiLM) for thermal conditioning to predict grain growth under complex, time-varying thermal profiles. The model was trained on a large dataset of grain growth evolution under thermal profiles with heating and cooling rates ranging from 0.01 kelvin per second to 10 kelvin per second. The results demonstrate that the proposed thermal conditioning mechanism enables the model to capture the influence of variable thermal profiles on grain boundary migration kinetics. Across the three test scenarios of increasing complexity, the model achieved a Structural Similarity Index Measure (SSIM) of up to 0.93 and mean grain size error below 3.2%. Despite the architectural extensions, inference time remains on the order of seconds per prediction sequence, preserving the computational advantage over PDE-based simulations. Thermal ; Grain growth ; Grain boundary ; Grain size ; Heat transfer ; Thermal resistance ; Deep learning ; Thermal conduction Machine Learning in Materials Science ; Metallurgy and Material Forming ; Solidification and crystal growth phenomena
Authors: Xiaowei Xia | Haiyang Cai | Boqiang Fan | Peng Zhuang | Yun Jiang | Yu Zhang | Yihan Miao | Boshi Kang
Source: Optical Engineering (article, 2026-03-18)
Field: Chemistry — Spectroscopy

Accurate quantitative gas sensing through UV absorption spectra using a hybrid variational mode decomposition–CNN approach Ultraviolet absorption spectroscopy, recognized for its rapid, nondestructive, and noninvasive nature, has been widely employed in fields such as environmental monitoring and chemical analysis. However, the accuracy of quantitative spectral analysis remains challenged by data distortions arising from various factors in the instrumental transmission process, including particle scattering, spectral drift, excessive denoising, and fitting errors. To overcome these issues, we propose a specific regression approach that integrates variational mode decomposition (VMD) with a convolutional neural network (CNN) for absorption spectra, termed VMD-CNN. The method first decomposes the spectrum into a series of intrinsic mode functions, from which mode components associated with gas absorption characteristics are extracted. A one-dimensional CNN is then trained on a large dataset of spectra with known concentrations to establish a quantitative inversion model. Experiments conducted on NO and SO2 absorption spectra within the 200 to 226 nm range demonstrate the effectiveness of the proposed approach. Comparative analyses with conventional techniques—including nonlinear least squares, partial least squares, and fast Fourier transform amplitude methods—before and after noise reduction and background elimination show that traditional methods heavily depend on data quality and may produce distorted results under low-concentration conditions due to over-optimization. By contrast, the proposed VMD-CNN model, which requires no preprocessing, consistently achieves the lowest error metrics across broad concentration ranges, specifically yielding root mean square error as low as 12.0 for NO and 2.0 for SO2, with mean absolute percentage error maintained 1.8% and 2.35%, respectively. Mean squared error ; Spectral line ; Absorption spectroscopy ; Fourier transform ; Approximation error ; Amplitude ; Absorption (acoustics) ; Nonlinear system ; Noise (video) ; Noise reduction Spectroscopy and Laser Applications ; Water Quality Monitoring and Analysis ; Gas Sensing Nanomaterials and Sensors
Authors: Yuseon Lee | Dong-Hyeop Kim | Sang-Woo Kim
Source: Composites Part B Engineering (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Data-driven prediction of process-induced deformation in CFRP composites using a convolutional neural network Parametric statistics ; Extrapolation ; Convolutional neural network ; Finite element method ; Stacking ; Deformation (meteorology) ; Principal component analysis ; Flange ; Composite number Machine Learning in Materials Science ; Composite Material Mechanics ; Mechanical Behavior of Composites
Authors: Qandeel Fatima Gillani | Almagul Mentbayeva | Muhammad Faisal Javed | Sandugash Kalybekkyzy
Source: Emerging Science Journal (article, 2026-04-01)
Field: Materials Science — Polymers and Plastics

Mg, Si, Al, and P Particle-Doped Epoxy: A Synergistic Approach for Enhanced Fire Performance This study presents the development of a low-toxicity, high-performance intumescent fire-retardant coating (IFRC) through a hybrid epoxy binder doped with Mg, Si, Al, and P particles. The objective was to improve thermal stability and char cohesion and reduce the toxic aromatic emissions typically released from bisphenol-A epoxy systems during combustion. Modified epoxy resins were prepared by dispersing Mg(OH)₂ and incorporating hydroxyl-terminated PDMS, followed by formulation with APP, melamine, expandable graphite, PER, and nano-alumina. Comprehensive analyses using FTIR, ¹³C NMR, DSC, TGA, SEM–EDS, TEM, XRD, and GC–MS, along with ISO-834 furnace and ASTM E-119 flame tests, were employed to evaluate chemical structure, thermal behavior, char morphology, and fire performance. The optimized formulation produced a dense Mg–Al–silicate–phosphate char network, achieved a 6.1× expansion ratio, limited backside steel temperature to 227°C, and retained 36% char at 800°C, which significantly outperformed the unmodified epoxy system. GC–MS confirmed a substantial (≈53%) reduction in toxic volatile emissions. A machine-learning model further validated char compactness with &gt;94% classification accuracy. Collectively, the results demonstrate that synergistic inorganic–siloxane modification offers a scalable, halogen-free pathway to next-generation epoxy-based IFRCs with enhanced fire resistance and markedly lower toxicity. Char ; Intumescent ; Epoxy ; Thermal stability ; Coating ; Fire retardant Flame retardant materials and properties ; Fire dynamics and safety research ; Fire effects on concrete materials
Authors: Ijaz Shahid | Maxim A. Grebeniuk | Jinbin Zhao | Ergen Bao | Tianye Yu | Xiangyang Liu | Yi-Chi Zhang | Artem R. Oganov | Yan V. Sun | Peitao Liu | Xing‐Qiu Chen
Source: ArXiv.org (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

High-pressure phase stability and superconductivity in La-Zr-H hydrides Hydrogen-rich ternary hydrides are promising candidates for high-Tc superconductivity at megabar pressures, yet their chemical space is vast and largely unexplored. Combining evolutionary structure searches with first-principles calculations, we comprehensively investigate the La-Zr-H ternary system in the 150-300 GPa pressure range. Zero-point energy-corrected convex hull analysis identifies multiple stable superconducting phases, including R3m-Zr2H17 at 300 GPa and P6/mmm-LaZr2H24 at 200 GPa, both of which are thermodynamically and dynamically stable and exhibit strong electron-phonon coupling. Solution of the Eliashberg equations predicts high superconducting transition temperatures of Tc = 209 K for R3m-Zr2H17 at 300 GPa and Tc = 202 K for P6/mmm-LaZr2H24 at 200 GPa. In addition to these stable phases, we identify a high-symmetry metastable compound, P6m2-LaZrH18, which lies just 0.027 eV/atom above the convex hull yet remains dynamically stable and exhibits a high predicted Tc of 206 K at 300 GPa. We find that, across all phases, the elevated Tc correlates with the high-symmetry structure with dense hydrogen cages, favorable electron counts per hydrogen, and a large hydrogen-derived density of states at the Fermi level. Finally, a random- forest machine learning model, trained on diverse hydrides superconductivity data, reproduces these structure-property trends across predicted structures, enabling to identify potential hydrides with high predicted Tc for targeted follow-up calculations and future high-pressure experiments. Superconductivity ; Metastability ; Ternary operation ; Convex hull ; Fermi level ; Stability (learning theory) ; Phase (matter) ; Transition temperature ; Space (punctuation) Hydrogen Storage and Materials ; High-pressure geophysics and materials ; Inorganic Chemistry and Materials
Authors: Shuwen Chen | Yuan Gao | W. Wang | Jun Zhu
Source: Physical Chemistry Chemical Physics (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Probing the longest <i>λ</i> <sub>max</sub> of azo compounds in near infrared absorption <i>via</i> integrating protonation, antiaromaticity and substituents: a combined DFT and machine learning study The photoisomerism of azo switches using light in the near-IR region (NIR, 780-1400 nm) is highly preferable for applications in biomedical and pharmacological fields. The common chemical modifications of azobenzene only enable the <i>E</i> ⇆ <i>Z</i> photoswitching wavelengths of azobenzene derivatives close to the red limit of near-infrared light. Here, we demonstrate that the introduction of protons and antiaromaticity, especially diprotonation, enables significantly red-shifted π → π* bands of <i>E</i> and <i>Z</i> forms <i>via</i> time-dependent density functional theory (TD-DFT) calculations, leading to the <i>λ</i><sub>max</sub> of the π → π* band of the <i>E</i> form of 17e.2H+ to be 1258 nm and its <i>Z</i> form to be 1036 nm. Population of π-electrons from the 5MR to the CN bond results in the reduction of their HOMO and LUMO energies, especially for LUMO energies, leading to remarkable red-shifts of the <i>λ</i><sub>max</sub> of the first π → π* absorption bands in heteroazonium ions. Machine learning study suggests that antiaromaticity has the greatest influence on <i>λ</i><sub>max</sub>. Our work demonstrates the longest <i>λ</i><sub>max</sub> of π → π* bands in azo switches through theoretical calculations, inviting experimental verifications of these novel azo species. Azobenzene ; Antiaromaticity ; Density functional theory ; HOMO/LUMO ; Absorption (acoustics) ; Near-infrared spectroscopy ; Absorption band ; Wavelength Photochromic and Fluorescence Chemistry ; Radical Photochemical Reactions ; Click Chemistry and Applications
Authors: Ijaz Shahid | Maxim A. Grebeniuk | Jinbin Zhao | Ergen Bao | Tianye Yu | Xiangyang Liu | Yi-Chi Zhang | Artem R. Oganov | Yan Sun | Peitao Liu | Xing‐Qiu Chen
Source: arXiv (Cornell University) (preprint, 2026-03-12)
Field: Materials Science — Materials Chemistry

High-pressure phase stability and superconductivity in La-Zr-H hydrides Hydrogen-rich ternary hydrides are promising candidates for high-Tc superconductivity at megabar pressures, yet their chemical space is vast and largely unexplored. Combining evolutionary structure searches with first-principles calculations, we comprehensively investigate the La-Zr-H ternary system in the 150-300 GPa pressure range. Zero-point energy-corrected convex hull analysis identifies multiple stable superconducting phases, including R3m-Zr2H17 at 300 GPa and P6/mmm-LaZr2H24 at 200 GPa, both of which are thermodynamically and dynamically stable and exhibit strong electron-phonon coupling. Solution of the Eliashberg equations predicts high superconducting transition temperatures of Tc = 209 K for R3m-Zr2H17 at 300 GPa and Tc = 202 K for P6/mmm-LaZr2H24 at 200 GPa. In addition to these stable phases, we identify a high-symmetry metastable compound, P6m2-LaZrH18, which lies just 0.027 eV/atom above the convex hull yet remains dynamically stable and exhibits a high predicted Tc of 206 K at 300 GPa. We find that, across all phases, the elevated Tc correlates with the high-symmetry structure with dense hydrogen cages, favorable electron counts per hydrogen, and a large hydrogen-derived density of states at the Fermi level. Finally, a random- forest machine learning model, trained on diverse hydrides superconductivity data, reproduces these structure-property trends across predicted structures, enabling to identify potential hydrides with high predicted Tc for targeted follow-up calculations and future high-pressure experiments. Superconductivity ; Metastability ; Ternary operation ; Convex hull ; Fermi level ; Stability (learning theory) ; Phase (matter) ; Transition temperature ; Space (punctuation) Hydrogen Storage and Materials ; High-pressure geophysics and materials ; Inorganic Chemistry and Materials
Authors: Xiaochuang Di | Mushan Yuan | Junyu Lu | Shenquan Yang | Cuiqing Zhou | Yang Chen | Huawei Zou
Source: Acta Physico-Chimica Sinica (article, 2026-02-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Navigating the continuous parameter space for ultra-broadband microwave absorption: a sequence-aware deep learning and evolutionary optimization approach Scalability ; Genetic algorithm ; Reflection loss ; Deep learning ; Microwave ; Fabrication ; Evolutionary algorithm ; Bandwidth (computing) ; Artificial neural network Electromagnetic wave absorption materials ; Microwave-Assisted Synthesis and Applications ; Metamaterials and Metasurfaces Applications
Authors: Saurab Singh
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-03-05)
Field: Materials Science — Materials Chemistry

Geometric Encoding of Thermal History in Glasses: Strain Topology as a Learnable Structural Signature The glass transition poses a fundamental question in condensed matter physics: how does thermal history become encoded in the atomic structure of disordered solids? Here we demonstrate that the memory of cooling rate is geometrically encoded in the strain topology the spatial distribution of local mechanical distortions rather than in simple density or coordination metrics. Using graph neural networks (GNNs) as structural probes and a systematic ablation methodology on a Lennard-Jones model system, we achieve 95% classification accuracy between fast-cooled and slow-cooled glasses with full geometric information, and maintain 90-94% accuracy even when feature magnitudes are instance-normalized to remove trivial separability. Pure topological features (connectivity alone) fail at near-random accuracy (52%), while local Hessian eigenvalues and bond-length distributions together carry the discriminative signal. Beyond classification, we show that GNNs can partially recover ordered initial geometries from thermally scrambled glasses with approximately 7-8% reconstruction error. Mapping reconstruction fidelity as a function of scrambling duration reveals a sharp transition: geometric memory collapses abruptly within 500 scrambling steps and plateaus thereafter, consistent with rapid correlation-length fragmentation in this model system. Visualization of strain-weighted contact networks confirms the physical picture fast-cooled glasses exhibit fragmented, short-range stress patterns, while slow-cooled glasses develop extended, percolating force chains. These results establish that thermal history manifests as a learnable geometric pattern written in bond-strain distributions, with the specific signature depending on material composition and interaction potential. Topology (electrical circuits) ; Scrambling ; Hessian matrix ; Signature (topology) ; Discriminative model ; Thermal ; Substructure ; Visualization Material Dynamics and Properties ; Machine Learning in Materials Science ; Metallic Glasses and Amorphous Alloys
Authors: Ashna Jose | Emilie Devijver | Martin Uhrin | Noel Jakse | Roberta Poloni
Source: ArXiv.org (article, 2026-03-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Predicting Spin-Crossover Behavior in Metal-Organic Frameworks from Limited and Noisy Data Using Quantile Active Learning Spin-crossover (SCO) metal-organic frameworks (MOFs) hold great promise for sensing, spintronics, and gas-related applications, however, only a small number of SCO-active examples are known among the thousands of MOFs already synthesized. Computational screening enhanced by machine learning offers a powerful route to uncover these hidden candidates much more rapidly than trial-and-error experiments. However, progress is limited by the computational complexity of obtaining accurate adiabatic energy differences, as these typically require separate geometry optimizations for both spin states, a process that is technically challenging, prone to convergence failures, and difficult to automate at scale. To mitigate these issues, we introduce a data-efficient strategy based on Quantile Regression Tree-based Active Learning, designed to navigate large chemical spaces while remaining robust to noisy and scarce labels obtained from unrelaxed geometries. After actively selecting a 200-sized subset of representative MOFs for electronic-structure evaluation, a Random Forest regressor trained on this data accurately identifies SCO-relevant candidates despite label noise, recovering 82% of true positives with only two false negatives. Applying the model to the unlabeled dataset yields a new collection of high-confidence SCO MOFs, which we denote pSCO-105. This work shows that spin crossover can be reliably identified from limited and imperfect data through smart training-set selection, enabling accelerated screening of SCO MOFs. Process (computing) ; False positive paradox ; Quantile ; Noisy data ; Random forest ; False positives and false negatives ; Convergence (economics) ; Noise (video) ; Active learning (machine learning) Magnetism in coordination complexes ; Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science
Authors: Madhav Kumar | Rakesh Kumar | Abir Chakravorty
Source: Journal of Food Science (article, 2026-01-01)
Field: Chemistry — Analytical Chemistry

A Low‐Cost Image Histogram and Machine Learning Approach for Detection of Cow Milk Adulteration Milk adulteration is a threat to food safety and public health. It is especially tricky in regions where getting quick, inexpensive tests is not always an option. To tackle this, we have come up with a simple, affordable approach. It uses easy image analysis combined with machine learning to detect common adulterants in milk. Instead of relying on laboratory equipment, all we need are straightforward optical images-just photos of milk samples. From there, we look for clues that indicate contamination, making the whole process more accessible and practical. We had taken digital photographs of milk samples mixed with water, detergent, starch, and synthetic milk. From these images, we found key statistics: the average brightness, how much it varies, and higher order features like skewness and kurtosis. These help us understand how the light scattering and turbidity change as adulterants are added. Using principal component analysis (PCA), we simplified the data, and then support vector machines (SVMs) helped classify that samples were adulterated versus pure. The results were not perfect, but they were promising. Our model hit about 85% accuracy in identifying adulteration across the different types and amounts of contaminants. In particular, adding water made the mean intensity increase, whereas detergent, starch, and synthetic milk each produced unique patterns in skewness and kurtosis due to their scattering effects. Compared to expensive spectroscopic solutions, this approach is faster, does not require chemicals, and is economical, making it ideal for quick checks, even right at the point of sale or in rural areas. Histogram ; Kurtosis ; Pattern recognition (psychology) ; Principal component analysis ; Support vector machine ; Skewness ; Extreme learning machine ; Cow milk Spectroscopy and Chemometric Analyses ; Milk Quality and Mastitis in Dairy Cows ; Identification and Quantification in Food
Authors: Yanwei Liang | Jiawei Chen | Yaohai Huang | Junhao Peng | Renhai Wang | Minru Wen | Fugen Wu | Huafeng Dong
Source: Inorganic Chemistry (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

High-Throughput Screening of Bulk Boride Superconductors with Honeycomb–Ruby Frameworks Metal borides are promising phonon-mediated superconductors, yet discovering new high superconducting transition temperature (<i>T</i><sub>c</sub>) bulk phases beyond MgB<sub>2</sub>-type motifs remains challenging. Here we propose a bulk layered boride prototype built from a honeycomb-ruby-honeycomb trilayer boron framework with three inequivalent metal sites for chemical tuning. Using a universal machine learning interatomic potential-accelerated high-throughput workflow combined with first-principles calculations, we screen over 120,000 candidates and identify 38 dynamically stable, near-hull compounds with <i>T</i><sub>c</sub> > 5 K, reaching <i>T</i><sub>c</sub> = 28.9 K for LiTiRuB<sub>18</sub>. Statistical analysis reveals a strong positive correlation between <i>T</i><sub>c</sub> and the B-p density of states at Fermi surfaces. Representative electronic and electron-phonon coupling analyses show multiband Fermi surfaces formed jointly by B-σ and B-π states and demonstrate that, beyond high-frequency B-B modes, metal-regulated soft B-M hybrid modes dominate the variation of electron-phonon coupling. These results expand the structural diversity of layered borides and establish an efficient data-driven route to bulk boride superconductors at ambient pressure. Boride ; Superconductivity ; Inorganic compound ; X-ray crystallography ; Crystal structure Boron and Carbon Nanomaterials Research ; Boron Compounds in Chemistry ; Rare-earth and actinide compounds