jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mengqian Hu | Lili Guo | Mengmei Huang | Lu Yin | Wei Li | Qingyou Zhang | Xiang Guo
Source: Propellants Explosives Pyrotechnics (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

Prediction of Detonation Heat for Nitrogen‐Containing Compounds Using the Main Products and Functional Group Method ABSTRACT Machine learning methods were employed to predict the detonation heat of energetic compounds containing C, H, O, and N elements with high accuracy, based on structural features and estimated detonation product descriptors. Since detonation heat fundamentally reflects the cumulative contributions of chemical bond cleavage and formation, it is hypothesized to possess a certain degree of linear additivity. Initially, the MLR model was developed for predicting the heat of detonation. The leave‐one‐out cross‐validation results of the training set were R 2 = 0.868, MAE = 459 kJ/kg, and RMSE = 673 kJ/kg. For the test set, the results were R 2 = 0.912, MAE = 334 kJ/kg, and RMSE = 524 kJ/kg. This prediction result indicates that the assumption that linear addition is reasonable. Subsequently, an SVR model with a linear kernel was employed to construct the prediction model. This approach yielded improved performance for the training set, with leave‐one‐out cross‐validation R 2 = 0.884, MAE = 421 kJ/kg, and RMSE = 636 kJ/kg. For the test set, the model achieved R 2 = 0.938, MAE = 309 kJ/kg, and RMSE = 439 kJ/kg. These results suggest that the inherent complexity of the explosion process also introduces nonlinear effects, thereby increasing the challenge of predicting the heat of detonation. These results not only verify the interpretability of the model but also provide a crucial basis for understanding the formation mechanism of the detonation heat. Detonation ; Interpretability ; Mean squared error ; Test set ; Basis (linear algebra) ; Standard enthalpy of formation ; Linear regression ; Nonlinear system ; Degree (music) Machine Learning in Materials Science ; Energetic Materials and Combustion ; Computational Drug Discovery Methods
Authors: Leonardo Boledi | Dirk Bosbach | Jenna Poonoosamy
Source: arXiv (Cornell University) (preprint, 2026-03-16)
Field: Materials Science — Materials Chemistry

A Kolmogorov-Arnold Surrogate Model for Chemical Equilibria: Application to Solid Solutions The computational cost of geochemical solvers is a challenging matter. For reactive transport simulations, where chemical calculations are performed up to billions of times, it is crucial to reduce the total computational time. Existing publications have explored various machine-learning approaches to determine the most effective data-driven surrogate model. In particular, multilayer perceptrons are widely employed due to their ability to recognize nonlinear relationships. In this work, we focus on the recent Kolmogorov-Arnold networks, where learnable spline-based functions replace classical fixed activation functions. This architecture has achieved higher accuracy with fewer trainable parameters and has become increasingly popular for solving partial differential equations. First, we train a surrogate model based on an existing cement system benchmark. Then, we move to an application case for the geological disposal of nuclear waste, i.e., the determination of radionuclide-bearing solids solubilities. To the best of our knowledge, this work is the first to investigate co-precipitation with radionuclide incorporation using data-driven surrogate models, considering increasing levels of thermodynamic complexity from simple mechanical mixtures to non-ideal solid solutions of binary (Ba,Ra)SO$_4$ and ternary (Sr,Ba,Ra)SO$_4$ systems. On the cement benchmark, we demonstrate that the Kolmogorov-Arnold architecture outperforms multilayer perceptrons in both absolute and relative error metrics, reducing them by 62% and 59%, respectively. On the binary and ternary radium solid solution models, Kolmogorov-Arnold networks maintain median prediction errors near $1\times10^{-3}$. This is the first step toward employing surrogate models to speed up reactive transport simulations and optimize the safety assessment of deep geological waste repositories. Perceptron ; Surrogate model ; Ternary operation ; Work (physics) ; Surrogate data ; Binary number ; Nonlinear system ; Focus (optics) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Nuclear reactor physics and engineering
Authors: Hanim Zuhrotul Amanah | Reza Adhitama Putra Hernanda | Khoirunnisaa Khoirunnisaa | Mohamad Soleh Hidayat | Hoonsoo Lee | Geonwoo Kim | Ajeng Siti Rohmat | Sri Rahayoe | Eni Harmayani
Source: Food Analytical Methods (article, 2026-03-29)
Field: Chemistry — Analytical Chemistry

Least absolute shrinkage and selection operator-selected NIR reflectance discrete features for robust glucomannan content estimation in Amorphophallus muelleri Blume flour Glucomannan ; Principal component analysis ; Univariate ; Principal component regression ; Linear regression ; Calibration ; Multivariate statistics ; Reflectivity ; Regression analysis Spectroscopy and Chemometric Analyses ; Polysaccharides Composition and Applications ; Food composition and properties
Authors: T. Sumiyoshi | Nahoko Kuroki | Hirotoshi Mori
Source: Inorganic Chemistry (article, 2026-03-28)
Field: Materials Science — Materials Chemistry

Electronic-Structure Informatics Guided Identification of Actinide/Lanthanide Selectivity Factors We report an interpretable electronic-structure informatics strategy for identifying ligand features that govern actinide/lanthanide (An/Ln) selectivity at the metal-ligand interaction level, a domain where limited experimental data and open-shell electronic complexity have hindered broad adoption of machine learning (ML) approaches. A systematically benchmarked DFT database of terpyridine-type ligands coordinated to Am<sup>3+</sup>, Cm<sup>3+</sup>, Eu<sup>3+</sup>, and Gd<sup>3+</sup> enabled the construction of compact electronic descriptors that predict complex-formation free energies and An/Ln selectivity differences via Gaussian process regression. The resulting model achieves quantitative accuracy (mean absolute error; MAE = 17.5 kJ mol<sup>-1</sup> for Δ<i>G</i><sub>bind</sub>; 2.6 kJ mol<sup>-1</sup> for ΔΔ<i>G</i><sub>An-Ln</sub>) while preserving chemical interpretability. Shapley Additive exPlanations (SHAP) analysis reveals that metal ionization potentials and inner-site charge patterns cooperatively govern selectivity, consistent with established coordination principles and hard-soft matching trends. These insights yield actionable design rules: electron-deficient central azines paired with moderately π-accepting terminal donors preferentially enhance An binding relative to Ln. By explicitly positioning the model as a controlled aqueous-phase thermodynamic screening layer, this work provides a reproducible and mechanistically grounded ML framework for predicting intrinsic An/Ln selectivity factors. The resulting electronic design variables are consistent with reported experimental trends and provide a practical basis for future extraction-oriented ligand design. Selectivity ; Matching (statistics) ; Identification (biology) ; Gaussian ; Materials informatics ; Basis (linear algebra) ; Informatics ; Mutual information ; Ligand (biochemistry) ; Gaussian process Machine Learning in Materials Science ; Radioactive element chemistry and processing ; Computational Drug Discovery Methods
Authors: Mustafa Emre ALTAY | Fatih Kahrıman
Source: Open MIND (article, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Enhancing near-infrared spectroscopy calibration for accurate protein and gluten determination in wheat flour and intact grains using chemometric techniques Rapid and reliable determination of protein and gluten in wheat is crucial for quality assessment and process control. Near-infrared (NIR) spectroscopy provides a nondestructive alternative to conventional chemical analysis; however, its predictive performance depends strongly on preprocessing and modeling strategy. This study evaluated how different combinations of scatter correction, derivative, and wavelength-selection methods influence NIR calibration performance for predicting protein and gluten contents in both wheat flour and intact grain samples, using Partial Least Squares (PLS) and Support Vector Machine (SVM) regression models under identical conditions. The results demonstrated that SVM achieved superior prediction accuracy for both protein and gluten contents, particularly when combined with Standard Normal Variate (SNV) preprocessing and mild smoothing. Among the best-performing models, those developed from flour-based spectra generally achieved higher coefficients of determination (R<sup>2</sup>, Coefficient of Determination, up to 0.96) than those based on grain spectra (R<sup>2</sup> ≈ 0.88–0.90), reflecting reduced scattering and greater compositional uniformity in flour samples. The most successful combinations were SNV + SVM for protein prediction (R<sup>2</sup> = 0.99) and smoothing + SNV + Genetic Algorithm–Partial Least Squares (GA-PLS) + SVM for gluten prediction (R<sup>2</sup> = 0.93). Overall results revealed that combining NIR spectroscopy with optimized preprocessing and machine-learning algorithms enables rapid and precise quantification of wheat quality traits, supporting its broader application in industrial quality control and breeding programs. Partial least squares regression ; Calibration ; Gluten ; Support vector machine ; Wheat flour ; Preprocessor ; Data pre-processing ; Correlation coefficient ; Smoothing Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Food composition and properties
Authors: Amirsalar Bagheri | Shirin Sheikhizadeh | Sajid Alavi | Davood Babaei Pourkargar
Source: Artificial Intelligence in Agriculture (article, 2026-04-01)
Field: Chemistry — Analytical Chemistry

An integrated time series modeling and computer vision framework for predictive structure characterization of extruded plant-based meat products Plant-based meat extrusion is a complex multi-stage process involving dynamic interactions among raw materials, operational parameters, and resulting product structure. Maintaining consistent product quality requires precise control over extrusion conditions, which remains challenging given the process's nonlinear behavior. Conventional physics-based models often struggle to accurately capture these complexities, while existing structure evaluation methods are labor-intensive and costly. Moreover, the lack of real-time feedback mechanisms hinders adaptive process control, limits the implementation of automated quality assurance, and contributes to product variability. To address these limitations, this study presents unsupervised and semi-supervised computer vision (CV) algorithms for automated texture assessment, integrated with machine learning-based extrusion models. These models capture the relationships between operational parameters and structural attributes, such as fibrousness and porosity. The proposed semi-supervised CV approach demonstrated significantly higher accuracy and a lower structure-scoring error than the unsupervised method. In integrated time series modeling, both XGBoost and LightGBM effectively captured the dynamics of the structure score, die temperature, and specific mechanical energy, with LightGBM performing slightly better across most metrics. By establishing dynamic links between process conditions and product structure, the proposed framework enables real-time monitoring and optimization of plant-based meat extrusion, enhancing process control and product consistency. • LSTM and GRU models capture high-frequency extrusion dynamics for optimization. • Unsupervised and semi-supervised computer vision improve structure quantification. • Integrated structure scores with process data for dynamic structure evolution prediction. • XGBoost and LightGBM facilitate structure prediction despite small dataset constraints. Process (computing) ; Product (mathematics) ; Quality (philosophy) ; Time series ; Raw data ; Soft sensor ; Pattern recognition (psychology) ; Process analytical technology Spectroscopy and Chemometric Analyses ; Food Drying and Modeling ; Food Supply Chain Traceability
Authors: Guangyan Li | Changchang Li | Jie Cheng | Linfeng Chen
Source: Talanta (article, 2026-04-03)
Field: Materials Science — Materials Chemistry

Laccase- and peroxidase-like synergy-driven Cu-MOF nanozyme sensor array for high-throughput screening of phenolic pollutants Pollutant ; Air pollutants ; Water pollutants ; Ozone ; Degradation (telecommunications) Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Yuan Tao | Chenhuan Cai | A. L. Chen | Tao Xia | Haiyan Yu | Y C Liu | Lina Zhou | Jingchuan Zhu
Source: Materials Chemistry and Physics (article, 2026-01-14)
Field: Materials Science — Materials Chemistry

Reinforcement of the Fe(110)/TiN(100) interface by elemental doping: machine learning accelerated first principles calculations Dopant ; Interface (matter) ; Stacking ; Doping ; Reinforcement learning ; Generalization ; Transition metal Machine Learning in Materials Science ; Advanced materials and composites ; Advanced ceramic materials synthesis
Authors: Mingyu Ye | Yang Pan | Rui Xu | Zhiyu Xu | Jiya Zhou | Xuan Luo | Yuxin Huang
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2026-03-24)
Field: Chemistry — Analytical Chemistry

Geographical origin discrimination and quality testing of Angelica dahurica slices based on mid-infrared spectroscopy and machine learning Quality (philosophy) ; Spectroscopy ; Pattern recognition (psychology) ; Quality assessment ; Nuclear magnetic resonance spectroscopy Spectroscopy and Chemometric Analyses ; Plant chemical constituents analysis ; Traditional Chinese Medicine Analysis
Authors: Yulin Hu | Jianwei Qiao | Dehong Li | Chengyan Gao | Zhiguo Xia
Source: Journal of Rare Earths (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Recent developments in near-infrared phosphors for broad-band light-emitting diodes light sources Phosphor ; Light-emitting diode ; Broadband ; Diode ; Luminescence ; Absorption (acoustics) ; Common emitter Luminescence Properties of Advanced Materials ; Luminescence and Fluorescent Materials ; Carbon and Quantum Dots Applications
Authors: Edoardo Bertania | Angelo Modena | Alessandro Caimi | Marco Bellotti | Luca Romizi | Maurizio Rinaldi | Ivana Miletto | Elia Bari | Lorena Segale | Giada Diana | Alessandro Candiani | Maria Luisa Torre | Ferdinando Auricchio
Source: International Journal of Pharmaceutics (article, 2026-02-28)
Field: Materials Science — Biomaterials

Microfluidic production of silk fibroin nanoparticles: Process optimization and modeling Silk fibroin is an attractive material for drug delivery due to its biocompatibility, stability, and ability to form nanoparticles through solvent-induced self-assembly. Although microfluidic nanoprecipitation offers superior control and reproducibility compared to batch methods, its rational optimization remains challenging because the tight coupling between flow dynamics, solvent exchange, and fibroin self-assembly hinders nanoparticle formation. Here, we present an integrated experimental-computational framework that combines Design of Experiments, machine-learning prediction, and computational fluid dynamics (CFD) to investigate how operating conditions influence the formation of silk fibroin nanoparticles (SFNs) within a specific microfluidic geometry. The combined effects of fibroin:acetone ratio (1:3-1:5) and total flow rate (8-140 mL/min) were systematically investigated, with particle size and distribution quantified by Nanoparticle Tracking Analysis. Linear, power-law, and Random Forest models captured non-linear interactions between operating parameters, identifying intermediate to high flow rates and higher acetone fractions as optimal for producing small, homogeneous nanoparticles. CFD simulations mechanistically rationalize these trends by quantifying mixing efficiency, shear rate, residence time, and solvent-rich regions that trigger fibroin nanoprecipitation. CFD-derived descriptors, including Volume of Change and process efficiency, delineated parameter regions associated with controlled desolvation and uniform nanoparticle formation. By integrating experimental data, data-driven modeling, and CFD-based mechanistic analysis, this study demonstrates how these complementary approaches can elucidate structure-process relationships in microfluidic SFN production. While the insights are system-specific, the proposed workflow provides a transparent and mechanistically grounded approach for interrogating complex nanoprecipitation processes and informing hypothesis-driven process refinement in related microfluidic systems. Fibroin ; Microfluidics ; Nanoparticle ; Mixing (physics) ; Process optimization ; Volumetric flow rate ; Flow focusing ; Residence time distribution ; Residence time (fluid dynamics) Silk-based biomaterials and applications ; Electrospun Nanofibers in Biomedical Applications ; 3D Printing in Biomedical Research
Authors: Ziqi Jiang | Bin Jiang
Source: The Journal of Physical Chemistry Letters (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

Quasi-Classical Trajectory with Adsorbate Gaussian Binning: Quantum-State-Resolved Prediction of Dissociative Sticking Probability Made Simple Dissociative chemisorption (DC) of molecules on solid surfaces is of both fundamental and practical importance in many interfacial applications. However, accurately predicting dissociative sticking probabilities (<i>S</i><sub>0</sub>) of polyatomic molecules on metal surfaces remains challenging. Fully coupled quantum dynamical methods are demanding, and conventional quasi-classical trajectory (QCT) methods are plagued by the zero-point energy leakage issue. Herein, we apply a newly developed QCT approach with adsorbate Gaussian binning (QCT-AGB) to the DC of methane on Ni(111), a key benchmark system. Utilizing a first-principles neural network potential, the QCT-AGB simulations achieve unprecedented agreement with experimental data across a wide range of collision energies and initial vibrational states, including branching ratios of different channels in isotopologues. This work validates QCT-AGB as an efficient and reliable approach for modeling quantum-state-resolved DC of polyatomic molecules on surfaces. Polyatomic ion ; Gaussian ; Trajectory ; Sticking probability ; Quantum ; Potential energy surface ; Molecule ; Range (aeronautics) ; Chemisorption Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Surface Chemistry and Catalysis
Authors: Khasim Ali Abid | Sajid Ahmed Syed | Majid Khan
Source: Structural Concrete (article, 2026-01-04)
Field: Materials Science — Materials Chemistry

Interpretable machine learning models for predicting penetration depth in ultra‐high‐performance concrete under ballistic impact Abstract The process of rigid projectiles penetrating concrete is governed by a range of factors, such as the material strength of the concrete and the properties of the projectile. This study focuses on developing precise models to predict penetration depth (PD) in ultra‐high‐performance concrete (UHPC) targets subjected to projectile impact, utilizing three machine learning (ML) techniques: multi‐expression programming (MEP), decision tree (DT), and gene expression programming (GEP). The research employed a dataset comprising 103 data points and 10 input features, and various statistical metrics were applied to evaluate model performance. The proposed models were also compared with existing empirical formulas to assess their accuracy and reliability. Additionally, SHapley Additive exPlanations (SHAP) were employed for model interpretability. While all models demonstrated strong predictive capabilities, the DT model achieved the highest accuracy, with a correlation coefficient ( R ) of 0.999 and a mean absolute error (MAE) of 0.588. The MEP and GEP models also performed well, with R values of 0.995 and 0.987 and MAE values of 3.118 and 5.416, respectively. Furthermore, empirical equations based on GEP and MEP were derived, providing a straightforward and practical method for estimating PD. The evaluation of the proposed ML models against existing empirical formulas revealed that the ML models significantly outperformed empirical approaches in predictive accuracy. The SHAP analysis identified impact energy, velocity, and diameter as the most influential factors in determining PD, while compressive strength and fiber aspect ratio also played a notable role. Overall, this study demonstrates that ML significantly enhances the accuracy of PD predictions, outperforming traditional empirical methods and offering a robust approach for estimating PD in UHPC targets. Gene expression programming ; Projectile ; Empirical modelling ; Predictive modelling ; Range (aeronautics) ; Random forest ; Decision tree ; Correlation coefficient ; Coefficient of determination High-Velocity Impact and Material Behavior ; Innovative concrete reinforcement materials ; Rock Mechanics and Modeling
Authors: Luca Pennati | Andong Hu | Ivy Peng | Lukas Müllender | Stefano Markidis
Source: arXiv (Cornell University) (preprint, 2026-04-08)
Field: Materials Science — Materials Chemistry

Making Room for AI: Multi-GPU Molecular Dynamics with Deep Potentials in GROMACS GROMACS is a de-facto standard for classical Molecular Dynamics (MD). The rise of AI-driven interatomic potentials that pursue near-quantum accuracy at MD throughput now poses a significant challenge: embedding neural-network inference into multi-GPU simulations retaining high-performance. In this work, we integrate the MLIP framework DeePMD-kit into GROMACS, enabling domain-decomposed, GPU-accelerated inference across multi-node systems. We extend the GROMACS NNPot interface with a DeePMD backend, and we introduce a domain decomposition layer decoupled from the main simulation. The inference is executed concurrently on all processes, with two MPI collectives used each step to broadcast coordinates and to aggregate and redistribute forces. We train an in-house DPA-1 model (1.6 M parameters) on a dataset of solvated protein fragments. We validate the implementation on a small protein system, then we benchmark the GROMACS-DeePMD integration with a 15,668 atom protein on NVIDIA A100 and AMD MI250x GPUs up to 32 devices. Strong-scaling efficiency reaches 66% at 16 devices and 40% at 32; weak-scaling efficiency is 80% to 16 devices and reaches 48% (MI250x) and 40% (A100) at 32 devices. Profiling with the ROCm System profiler shows that &gt;90% of the wall time is spent in DeePMD inference, while MPI collectives contribute &lt;10%, primarily since they act as a global synchronization point. The principal bottlenecks are the irreducible ghost-atom cost set by the cutoff radius, confirmed by a simple throughput model, and load imbalance across ranks. These results demonstrate that production MD with near ab initio fidelity is feasible at scale in GROMACS. Molecular dynamics ; Inference ; Domain decomposition methods ; Benchmark (surveying) ; Embedding ; Throughput ; Set (abstract data type) ; Profiling (computer programming) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Nanopore and Nanochannel Transport Studies