jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Biqing Chen | Xiaohong Qiu
Source: Analytical Methods (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep learning-empowered SERS: deciphering the multidimensional information code of complex biological samples Surface-enhanced Raman spectroscopy (SERS), an analytical technique characterized by fingerprint-like identification capabilities and high sensitivity, has demonstrated significant application value across diverse fields such as biomedicine, environmental protection, and food safety. However, spectral noise inherent in the detection process introduces potential interference, thereby reducing the signal-to-noise ratio (SNR) and compromising the accuracy and reliability of sample analysis. This limitation has spurred the development of Raman spectral denoising algorithms to encourage the application of Raman spectroscopy in more complex domains of analytical chemistry. Deep learning (DL) has demonstrated remarkable autonomy in learning high-level representations and recognizing complex patterns. Consequently, facing intertwined influencing factors and exponentially growing data volumes, AI is finding increasing application in the aforementioned aspects of SERS. In this review, by integrating deep learning with surface-enhanced Raman scattering technology, we summarize its recent advancements and provide novel insights into the challenges and future prospects, aiming to propel SERS technology towards more advanced development. Identification (biology) ; Code (set theory) ; Value (mathematics) ; Field (mathematics) ; Multidimensional data ; Raman spectroscopy Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biosensors and Analytical Detection
Authors: Amalya C. Johnson | Chris Fajardo | Leena Sansguiri | Weike Ye | Steven B. Torrisi
Source: Open MIND (preprint, 2026-03-06)
Field: Chemistry — Analytical Chemistry

Spectra-Scope : A toolkit for automated and interpretable characterization of material properties from spectral data Spectroscopy is a central pillar of materials characterization, providing useful information on properties like structure, composition, or excited state dynamics of a system. However, many spectroscopic techniques present challenges in development of interpretable, performant, and reliable supervised learning models due to the wide range of possible nonlinear correlations that can exist between the signal and the response variable (target) of interest. Here, we present Spectra-Scope, an open-source AutoML framework for automatic characterization of material properties from spectroscopy data using interpretable machine learning (ML) models. The software is implemented in Python and a no-code web application. It comprises tools for data preprocessing, nonlinear feature extraction, machine learning model training, and feature downselection. Users can easily train different types of simple, interpretable ML models on a set of feature transformations quickly and with modest computational resources. In this work, we outline the methods of Spectra-Scope and its effectiveness across diverse datasets, with applications to materials and agricultural spectroscopy data. We show that Spectra-Scope can reproduce performance of comparable models in the literature, and highlight how our emphasis on interpretability can be used to rationalize the behavior of individual models and understand the physical processes behind spectral features. Interpretability ; Python (programming language) ; Feature (linguistics) ; Spectral properties ; Feature selection ; Characterization (materials science) ; Nonlinear system ; Software ; Set (abstract data type) Spectroscopy and Chemometric Analyses ; Machine Learning in Materials Science ; Remote Sensing in Agriculture
Authors: Maolin Yu | Yangming Gui | Zhiqiang Zhao | Jidong Li | Xiang Guo | Zhuhua Zhang
Source: ACS Nano (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Mechanism for Borophene Phase Transition on Substrate Borophene on the mostly used Ag(111) substrate undergoes a perplexing transition from the <i>v</i><sub>1/6</sub> to the <i>v</i><sub>1/5</sub> phases as temperature increases, but the underlying mechanism remains elusive, hindering fine control over borophene synthesis. Here, we propose that the phase transition is driven by a critical synergy between in-plane migration of boron atoms and their sinking into the substrate. <i>Ab initio</i> calculations show that atoms in the <i>v</i><sub>1/6</sub> phase can migrate to form patches with higher coordination numbers, where atoms are easier to sink into the substrate. Atomic sinking further promotes boron migration, locally nucleating <i>v</i><sub>1/5</sub> domains that eventually expand into a perfect sheet via iterative sinkings and migrations. This temperature-driven, stepwise transition is substantiated by machine-learning-assisted molecular dynamics simulations with an enhanced sampling technique. Moreover, our simulations rationalize the experimental temperature window for synthesizing a series of intermixed <i>v</i><sub>1/6</sub> and <i>v</i><sub>1/5</sub> phases. These findings can inform experimental efforts to achieve structure- and layer-controllable borophene synthesis. Borophene ; Phase transition ; Boron ; Molecular dynamics ; Substrate (aquarium) ; Phase (matter) ; Nucleation Boron and Carbon Nanomaterials Research ; Boron Compounds in Chemistry ; Organoboron and organosilicon chemistry
Authors: Ravi Kiran Bollineni | Zhifei Deng | Michael S. Kesler | Michael R. Tonks | Ling Li | Reza Mirzaeifar
Source: Computational Materials Science (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Inverse design of hypoeutectoid pearlite steel microstructures using a deep learning and genetic algorithm optimization framework Finite element method ; Microstructure ; Pearlite ; Deep learning ; Genetic algorithm ; Inverse ; Optimal design ; Design of experiments Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Multi-Objective Optimization Algorithms
Authors: Haining Li | Jianyu Bai | Z. Zhang | Xun Sun
Source: Chinese Journal of Analytical Chemistry (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

AI-Driven Control of Large Potassium Dihydrogen Predictive Modeling and Assisted Optimization of DKDP Crystal Growth Using Gradient Boosted Regression Trees and Crystal Height Features This work develops a machine‑learning assisted approach to model and optimize the growth of deuterated potassium dihydrogen phosphate (DKDP) crystals using industrial‑scale data. Conventional models based only on temperature and supersaturation fail to capture multi‑field coupling and size effects in large crystals. Here, crystal height is introduced as an additional physical feature and combined with a Gradient Boosted Regression Trees (GBRT) algorithm to predict growth rate. On 15,881 daily records from long‑term DKDP runs, adding crystal height increases the test‑set R² from 0.4916 to 0.8427 and significantly reduces MSE, RMSE and MAE. Feature‑importance analysis shows that temperature, saturation temperature and crystal height contribute 22.39%, 42.58% and 35.03%, highlighting the key role of height in size‑dependent heat‑ and mass‑transfer. The trained GBRT model enables real‑time prediction and anomaly detection, where large prediction deviations are associated with “hair” inclusions and lateral‑expansion defects. This grey‑box strategy offers a practical route to intelligent monitoring and assisted optimization of large DKDP crystal growth. Crystal (programming language) ; Supersaturation ; Crystal growth ; Potassium nitrate ; Work (physics) ; Potassium ; Regression ; Regression analysis Crystallization and Solubility Studies ; Crystallography and molecular interactions ; Nonlinear Optical Materials Research
Authors: Wahyu Aji Eko Prabowo | Muhamad Akrom | Supriadi Rustad | Wise Herowati | Harun Al Azies | Hermawan Kresno Dipojono | Subagjo | Febdian Rusydi | Hideaki Kasai
Source: Iran Journal of Computer Science (article, 2026-03-10)
Field: Chemistry — Inorganic Chemistry

Robust ensemble learning for classification and accurate prediction of pore-limiting diameter in metal–organic frameworks Ensemble learning ; Pattern recognition (psychology) ; Robustness (evolution) ; Noise (video) ; Feature (linguistics) ; Training set Metal-Organic Frameworks: Synthesis and Applications ; Block Copolymer Self-Assembly ; Zeolite Catalysis and Synthesis
Authors: Wenchao Li | Teppei Imaizumi
Source: Reviews in Agricultural Science (article, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Raman Spectroscopy for Vegetable Processing Applications: Technologies and Trends Raman spectroscopy, when combined with chemometric techniques, has become a powerful tool for quality assessment, process monitoring, and optimization in the vegetable processing industry. This review provides a comprehensive overview of recent advances in the use of Raman chemometric methods for processed vegetables, emphasizing their distinct advantages over other spectroscopic techniques, such as near-infrared (NIR), Fourier-transform infrared (FT-IR), and ultraviolet-visible (UV-Vis) spectroscopy. Due to its high molecular specificity, minimal water interference, and capacity to resolve detailed information on chemical structure, Raman spectroscopy is particularly well-suited for analyzing complex vegetable matrices. Integrated chemometric approaches, including principal component analysis (PCA), partial least squares regression (PLSR), support vector machines (SVM), and convolutional neural networks (CNN), have significantly improved both data interpretation and predictive performance across tasks such as quality classification, constituent quantification, and real-time process monitoring. Despite these advancements, challenges remain, including overlapping signals, limited sample diversity, and the lack of robust systems for in-line implementation. Future research should prioritize the development of transfer learning strategies, multimodal spectroscopic integration, and establish standardized automated workflows to enhance the generalizability and scalability of the models. These advances will facilitate the commercial adoption of Raman-based technologies in vegetable processing industry. Raman spectroscopy ; Spectroscopy ; Analytical Chemistry (journal) ; Surface-enhanced Raman spectroscopy ; Signal processing Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Edible Oils Quality and Analysis
Authors: Huiju Lee | Zhi Li | Jiangang He | Yi Xia
Source: arXiv (Cornell University) (preprint, 2026-01-14)
Field: Materials Science — Materials Chemistry

Data-Driven Exploration and Insights into Temperature-Dependent Phonons in Inorganic Materials Phonons, quantized vibrations of the atomic lattice, are fundamental to understanding thermal transport, structural stability, and phase behavior in crystalline solids. Despite advances in computational materials science, most predictions of vibrational properties in large materials databases rely on the harmonic approximation and overlook crucial temperature-dependent anharmonic effects. Here, we present a scalable computational framework that combines machine learning interatomic potentials, anharmonic lattice dynamics, and high-throughput calculations to investigate temperature-dependent phonons across thousands of materials. By fine-tuning the universal M3GNet interatomic potential using high-quality phonon data, we improve phonon prediction accuracy by a factor of four while preserving computational efficiency. Integrating this refined model into a high-throughput implementation of the stochastic self-consistent harmonic approximation, we compute temperature-dependent phonons for 4,669 inorganic compounds. Our analysis identifies systematic elemental and structural trends governing anharmonic phonon renormalization, with particularly strong manifestations in alkali metals, perovskite-derived frameworks, and related systems. Machine learning models trained on this dataset identify key atomic-scale features driving strong anharmonicity, including weak bonding, large atomic radii, and specific coordination motifs. First-principles validation confirms that anharmonic effects can dramatically alter lattice thermal conductivity by factors of two to four in some materials. This work establishes a robust and efficient data-driven approach for predicting finite-temperature phonon behavior, offering new pathways for the design and discovery of materials with tailored thermal and vibrational properties. Anharmonicity ; Phonon ; Thermal conductivity ; Interatomic potential ; Lattice (music) ; Molecular vibration ; Ab initio Thermal Expansion and Ionic Conductivity ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Zhaoyu Kang | Yufeng Xia | Yuqiu Ye | Wei Jiang | Lu Li
Source: JOM (article, 2026-01-12)
Field: Materials Science — Biomaterials

Dynamic Recrystallization Kinetics and Its Numerical Modeling for a Mg-6.0 Zn-2.0 Yb-0.5 Zr Alloy Dynamic recrystallization ; Isothermal process ; Alloy ; Microstructure ; Constitutive equation ; Experimental data ; Recrystallization (geology) ; Cellular automaton Magnesium Alloys: Properties and Applications ; Metallurgy and Material Forming ; Microstructure and mechanical properties
Authors: Kaan Kırkağaç | Nadir Subaşı | Ertuğrul Adıgüzel
Source: Istanbul University - Journal of Electrical & Electronics Engineering (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

Reliability Analysis of Al2O3/Epoxy Nanocomposites Under Surface Tracking Degradation Surface tracking resistance and long-term reliability are critical performance parameters for polymeric insulation materials used in high-voltage electrical applications. In this study, the tracking behavior of epoxy/aluminum oxide (Al2O3) nanocomposites was experimentally, statistically, and data-driven investigated under high electrical stress conditions. Epoxy composites containing 0, 1, 3, and 5 wt.% Al2O3 nanoparticles were fabricated and tested according to the International Electrotechnical Commission(IEC) 60112 standard at applied voltage levels of 400 V, 500 V, and 600 V, with repeated experiments conducted to ensure statistical consistency. The results demonstrated that Al2O3 incorporation significantly influences tracking resistance in a concentration-and voltage-dependent manner, where the composite containing 1 wt.% Al2O3 exhibited the highest tracking resistance at moderate voltage levels (400–500 V), achieving higher drop-to-failure values (defined as the number of contaminant droplets required to cause sustained surface tracking for more than 2 seconds or material burn-through, in accordance with IEC 60112) than pure epoxy. However, increasing the filler content beyond 1 wt.% led to a reduction in tracking lifetime, particularly under high electrical stress, which was associated with increased surface erosion and localized degradation. The degradation behavior at 600 V was further analyzed using two-parameter Weibull statistics, revealing a decrease in characteristic life with increasing Al2O3 content, while the 1 wt.% Al2O3 composite exhibited the highest Weibull shape parameter, indicating a stable and predictable wear-out dominated failure mechanism. In addition, machine learning models were developed to predict tracking lifetime with high accuracy (R2 &gt; 98%), and feature importance analysis identified erosion-related mass loss, filler content, and applied voltage as the dominant parameters governing tracking degradation. Overall, the strong agreement between experimental results, Weibull reliability analysis, and machine learning predictions provides a combined experimental and data-driven framework for evaluating and optimizing epoxy-based insulation materials for high-voltage applications. Cite this article as: K. Kırkağaç, N. Subaşı and E. Adıgüzel, “Reliability analysis of Al2O3/epoxy nanocomposites under surface tracking degradation,” Electrica, 26, 0017, doi: 10.5152/electrica.2026.26017. Weibull distribution ; Tracking (education) ; Reliability (semiconductor) ; Nanocomposite ; Composite number ; Degradation (telecommunications) ; Nanoparticle tracking analysis ; Voltage ; Stress (linguistics) High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Thermal Analysis in Power Transmission
Authors: Mandira Dey | Anuj Kumar Ray | Vic Austen | Quan Manh Phung | Takeshi Yanai | Ankan Paul | Debashree Ghosh
Source: Physical Chemistry Chemical Physics (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-quality quantum chemical data for spin state determination in transition-metal complexes Machine learning (ML) models have achieved remarkable success in organic chemistry, where reliable reference data, most commonly from density functional theory (DFT), enable accurate predictions. In contrast, ML approaches remain far less reliable for transition metal complexes, particularly for spin-state energetics (SSE), due to the erratic and system-dependent behavior of DFT. To begin addressing this fundamental data limitation, we present a benchmark dataset of 50 first-row mononuclear octahedral complexes spanning d<sup>4</sup>-d<sup>6</sup>, with spin energy gaps computed at the high-level CASPT2/CC multireference level. Using this high-accuracy dataset, we systematically benchmark a broad range of DFT methods and demonstrate that the optimal fraction of Hartree-Fock exchange is intrinsically dependent on the specific spin-state transition. Furthermore, we introduce an electronic-structure-based descriptor, Des-<i>δ</i>, and employ a Δ-machine-learning (Δ-ML) framework to extrapolate CASPT2/CC-level accuracy to an expanded library of 500 complexes. Benchmark (surveying) ; Quantum chemical ; Density functional theory ; Spin (aerodynamics) ; Range (aeronautics) ; Quantum ; Spin states ; Transition metal ; Energy (signal processing) Magnetism in coordination complexes ; Molecular spectroscopy and chirality ; Advanced NMR Techniques and Applications
Authors: Rick Oerder | Jan Hamaekers
Source: arXiv (Cornell University) (preprint, 2026-03-10)
Field: Materials Science — Materials Chemistry

Flexible Cutoff Learning: Optimizing Machine Learning Potentials After Training We introduce Flexible Cutoff Learning (FCL), a method for training machine learning interatomic potentials (MLIPs) whose cutoff radii can be adjusted after training. Unlike conventional MLIPs that fix the cutoff radius during training, FCL models are trained by randomly sampling cutoff radii independently for each atom. The resulting model can then be deployed with different per-atom cutoff radii depending on the application, enabling application-specific optimization of the accuracy-cost tradeoff. Using a differentiable cost model, these per-atom cutoffs can be optimized for specific target systems after training. We demonstrate FCL with a modified MACE architecture trained on the MAD dataset. For a subset featuring molecular crystals, optimized per-atom cutoffs reduce computational cost by more than 60% while increasing force errors by less than 1%. These results show that FCL enables training of a single general-purpose MLIP that can be adapted to diverse applications through post-training cutoff optimization, eliminating the need for retraining. Cutoff ; Cutoff frequency ; RADIUS ; Training (meteorology) ; Bayesian optimization ; Classifier (UML) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Force Microscopy Techniques and Applications
Authors: Daniel Isaias Vivas Zamora | Jose Alfredo Sanchez Diaz | Yndira Patricia Brito Guerrero
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-03-09)
Field: Chemistry — Physical and Theoretical Chemistry

An Integrated Electrochemical-Dielectric Framework for Multimodal Cellular Triage:Physics-Informed Edge AI on ESP32-S3 Early detection of cellular anomalies remains challenging in resource-limited settings where conventional diagnostic infrastructure is unavailable. This work presents a unified electrochemical-dielectric framework for multimodal (whole blood and buffer) label-free cellular screening that integrates Hydrogen Evolution Reaction (HER) voltammetry with dynamic ohmic drop compensation, Maxwell-Wagner dielectric spectroscopy with global spectral fitting, and machine learning with physics-informed regularization on an edge computing platform. We postulate that metabolic acidosis manifests as shifts in HER onset potential governed by Nernstian thermodynamics with explicit temperature dependence and kinetic/ohmic correction terms, while genomic anomalies, bacteria, and parasites alter complex permittivity through Hanai mixture dynamics with α and β dispersion differentiation. A 1D Convolutional Neural Network (CNN) with dual architecture and physics-informed regularization processes electrochemical signatures on an ESP32-S3 microcontroller, ensuring thermodynamic consistency during inference through explicit loss function formulation. Theoretical analysis indicates that populationlevel measurements (> 10Exp5 cells/mL) can achieve signal-to-noise ratios sufficient for anomaly flagging, though individual cell detection remains infeasible. We discuss implementation requirements, including dynamic iRΩ compensation via highfrequency pulse, external analog front-ends for impedance spectroscopy above 1 MHz, and uncertainty propagation analysis with error budgets for pH and dielectric parameters. This framework targets triage applications rather than definitive diagnosis, offering a sub-$50 portable screening tool for detection of metabolic, genetic, and infectious anomalies in multiple biological matrices. Open-source code and mathematical derivations are provided to facilitate reproducibility and community validation. Convolutional neural network ; Enhanced Data Rates for GSM Evolution ; Population ; Relaxation (psychology) ; Admittance ; Spectroscopy ; Artificial neural network ; Signature (topology) ; Genetic algorithm thermodynamics and calorimetric analyses ; Fractal and DNA sequence analysis ; Advanced Chemical Sensor Technologies