jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Barış Demirbay | Duygu Bayram Kara
Source: Physica Scripta (article, 2026-04-08)
Field: Materials Science — Materials Chemistry

On the performance of machine learning models proposed for electrical conductivity classification of carbon nanotube-doped polymer nanocomposite films Abstract This study presents a comparative performance evaluation of four machine learning classifiers, which are artificial neural networks, support vector machines, bagged decision trees and logistic regression, aiming to predict the conductivity regime (insulator or semiconductor) of carbon nanotube (CNT)-doped polystyrene (PS) latex films using a highly constrained dataset. To ensure scientifically valid and reproducible estimates, a leak-free repeated nested 5-fold cross-validation framework was employed together with variance inflation factor analysis to rule out multicollinearity. Initial standard metrics suggested that the high-capacity bagged tree ensemble possessed near-perfect predictive power. However, subsequent threshold-independent global precision-recall pooling and rigorous Y-scrambling permutation tests definitively exposed this as an artifact of fold-specific memorization, with the ensemble demonstrating a critical failure by perfectly memorizing randomized noise. Ultimately, the logistic regression model was found to be the superior and most robust framework. By enforcing a strict linear decision boundary, the logistic model successfully resisted overfitting, demonstrating that its constrained mathematical structure naturally aligns with the abrupt, macroscopic phase transition characteristic of electrical percolation. Research findings reveal that for modest measurement datasets governed by threshold-like physical behavior, simpler linear models offer a superior bias-variance tradeoff, providing a reliable computational tool to significantly reduce trial and error experiments in nanocomposite design. Decision tree ; Random forest ; Support vector machine ; Carbon nanotube ; Ensemble learning ; Logistic regression ; Pooling ; Variance (accounting) Carbon Nanotubes in Composites ; Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials
Authors: Yuqing Sun | Xiaorui Chen | Jianzhi Gao | Wenliang Zhu | Minghu Pan
Source: Cell Reports Physical Science (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Searching for high-performance thermoelectric materials via an advanced machine learning framework <h2>Summary</h2> Data quality, feature engineering, and model generalization are key challenges in applying machine learning to high-performance materials design. Here, we report a framework addressing these challenges using thermoelectric materials as a case study. Data are collected and curated from the Starrydata2 database, followed by multi-step feature engineering, including construction, selection, and optimization, to obtain a physically meaningful subset. By benchmarking against mainstream regression models, with independent external dataset testing and model interpretability analysis, a modified tabular prior-data fitted network (TabPFN) model (model I) demonstrates superior accuracy and generalization in predicting the thermoelectric figure of merit (<i>ZT</i>). Model I is applied to halide double perovskites from the Materials Project database, identifying candidates including Rb<sub>2</sub>CuSbCl<sub>6</sub>, Cs<sub>2</sub>AgAuCl<sub>6</sub>, and Rb<sub>2</sub>CuBiCl<sub>6</sub>. First-principles calculations validate their thermoelectric properties, with n-type Cs<sub>2</sub>AgAuCl<sub>6</sub> achieving <i>ZT</i><sub>max</sub> = 1.64 at 800 K. These results highlight the potential of a data-driven and computationally synergistic approach for discovering high-performance thermoelectric materials. Interpretability ; Benchmarking ; Generalization ; Thermoelectric effect ; Feature (linguistics) ; Thermoelectric materials ; Materials informatics ; Key (lock) Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties
Authors: John Paolo L. Lazarte | Jordan F. Madrid | John Andrew A. Luna | Patrick Jay E. Cabalar
Source: Radiation Physics and Chemistry (article, 2026-02-10)
Field: Materials Science — Polymers and Plastics

Artificial neural network optimization of radiation-induced graft polymerization for heavy metal adsorbents Grafting ; Glycidyl methacrylate ; Monomer ; Adsorption ; Polypropylene ; Artificial neural network ; Polymerization ; Mean squared error Polymer crystallization and properties ; Advanced Polymer Synthesis and Characterization ; Polymer Science and PVC
Authors: Yu. D. Fomin | E. N. Tsiok | V. N. Ryzhov
Source: ArXiv.org (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

Melting of thin silicon films: a molecular dynamics study with two machine learning potentials Thermal stability of silicene and thin silicon films is studied by molecular dynamics using two machine-learning potentials, SNAP and GAP. For SNAP potential, systems ranging from a single silicene layer to films of 36 layers are considered. Silicene is found to lose its structure at 500 K. The decomposition temperature increases with film thikness and reaches saturation at about 28 layers, corresponding to the bulk melting point of the SNAP model (1380 K). Thin films up to 8 layers exibit two-phase coexistence upon decomposition, while thicker films undergo surface melting followed by complete collapse into the liquid state. The GAP potential, although more accurate for bulk silicon, fails to describe the gas phase: silicene modelled with GAP decomposes into a set of small clusters. The results are compared with earlier data for the Stillinger-Weber potential. Silicene ; Silicon ; Molecular dynamics ; Thin film ; Thermal stability ; Melting point ; Layer (electronics) ; Thermal Thermal properties of materials ; Graphene research and applications ; Material Dynamics and Properties
Authors: Abrar Faiyad | Ashlie Martini
Source: Journal of Chemical Theory and Computation (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

Machine Learning Interatomic Potentials Enable Molecular Dynamics Simulations of Doped MoS <sub>2</sub> Dopants can tune the performance of MoS<sub>2</sub> in various applications, but the use of molecular dynamics simulations for doped MoS<sub>2</sub> materials discovery is limited by the lack of multidopant interatomic potentials. Universal machine learning interatomic potentials (MLIPs) could be a solution, but the accuracy of these potentials must first be evaluated. Here, we evaluate the accuracy of a recently developed MLIP, META's Universal Model for Atoms, for 25 different MoS<sub>2</sub> dopants spanning metals, nonmetals, and transition metals in Mo-substitution, S-substitution, and intercalated positions by benchmarking the MLIP-predicted formation energy and the dopant-induced structural change against density functional theory (DFT) calculations. The computational framework for MLIP validation and simulations is described in detail, and the source code is made available online. The MLIP is then demonstrated by performing heating-cooling simulations of MoS<sub>2</sub> supercells with all 25 dopants. These simulations capture complex phenomena including dopant clustering, MoS<sub>2</sub> layer fracturing, interlayer diffusion, and chemical compound formation at orders-of-magnitude reduced computational cost compared to DFT. This work provides a computational workflow for the application-oriented design of doped-MoS<sub>2</sub>, enabling high-throughput screening of dopant candidates and optimization of compositions for targeted tribological, electronic, and optoelectronic performance. Dopant ; Molecular dynamics ; Interatomic potential ; Density functional theory ; Workflow ; Doping ; Work (physics) ; Benchmarking Machine Learning in Materials Science ; 2D Materials and Applications ; Advanced Physical and Chemical Molecular Interactions
Authors: Changshuai Yin | Xuhao Wan | Wei Yu | Yao Lu | Jiaren Feng | Taiqiao Liu | Songpeng Zhao | Kang Liang | Qiangmin Wei | Yuzheng Guo | Sheng Liu | Zhaofu Zhang
Source: Crystal Growth & Design (article, 2026-01-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Explainable Analysis and Optimization of the Thermal Field in Gallium Oxide Single Crystal Growth Based on Machine Learning The edge-defined film-fed growth (EFG) method, as the core technology for growing high-quality gallium oxide (β-Ga2O3) single crystals, constitutes a complex system engineering. The precise control of thermal field distribution during crystal growth has emerged as one of the critical factors for enhancing crystal quality. Machine learning (ML) techniques have demonstrated tremendous potential in optimizing the thermal field design, particularly in predicting the influence of equipment dimensions on thermal field distribution. Centered on the application of ML in the thermal field design of β-Ga2O3, this paper proposes a prediction model based on equipment dimensions. A dataset containing 5000 sets of geometric parameters of equipment was constructed. A predictive model was established based on the machine learning Categorical Boosting (CatBoost) model, and explainable ML models were employed to analyze the relationship between equipment dimensions and thermal field distribution. Shapley additive explanation values were utilized to quantify the impact of inputs on outputs, thereby identifying the key indicators influencing the thermal field design in the EFG method. The optimal geometric configuration was obtained through multiobjective optimization, facilitating the growth of higher-quality β-Ga2O3 single crystals. Finally, the key indicator factors affecting the temperature gradient at the solid–liquid interface were verified through experiments. Field (mathematics) ; Thermal ; Categorical variable ; Key (lock) ; Gallium ; Single crystal Ga2O3 and related materials ; Machine Learning in Materials Science ; GaN-based semiconductor devices and materials
Authors: Lee Lambert | Brett J. Borghetti | Abigail A. Bickley
Source: Journal of Radioanalytical and Nuclear Chemistry (article, 2026-02-20)
Field: Chemistry — Inorganic Chemistry

Uranium chemical compound classification using sub-images and statistical machine learning for nuclear forensics Abstract Uranium particle analysis from Scanning Electron Microscope (SEM) imagery is a crucial tool in nuclear forensics. The particle morphology lexicon proposed by Tamasi et al. in J Radioanal Nucl Chem 307, 1611–1619 (2016) follows a standardized, manual identification process to identify particle morphology features. The present work seeks to mirror this methodology using computer feature selection from the scikit-image Python library rather than human classification. Using a random forest classifier, a 56% overall uranium true positive classification accuracy (a 39.6% balanced classification accuracy) was achieved on a test set outperforming a naïve (chance) model by 48%. The methodology introduced splits a particle image into sub-images to predict the chemical compound present in the sub-image. These sub-images use a majority rules voting mechanism to classify the chemical compound and calcination temperature of the original whole particle. Images analyzed in this dataset were from particles of pure compounds but the introduced methodology opens the door for future work on aggregate compound particle analysis. The dataset utilized contained 1906 SEM images: 830 Secondary Electron Imaging (SEI) and Low-Energy Electron Imaging (LEI) images and 1076 Large-Angle Backscattered Electron Imaging (LABE) images. Each image was one of 13 uranium chemical classifications, where image quantities were unbalanced. When examining image types, initial data seems to indicate that LABE images outperformed SEI/LEI images in machine learning applications. Random forest ; Pattern recognition (psychology) ; Particle (ecology) ; Image processing ; Feature selection ; Python (programming language) ; Uranium ; Feature (linguistics) ; Supervised learning Radioactive element chemistry and processing ; Nuclear Materials and Properties ; Radioactive contamination and transfer
Authors: Park, Kwanghee | Ueno, Seiji | Tanimura, Yoshitaka
Source: arXiv (Cornell University) (preprint, 2026-03-18)
Field: Materials Science — Materials Chemistry

sbml4md: A computational platform for System-Bath Modeling via Molecular Dynamics powered by Machine Learning We introduce sbml4md, a newly developed algorithm implemented as a software package to extract parameters of multimode anharmonic Brownian (MAB) models from molecular dynamics (MD) trajectories for simulating nonlinear vibrational spectra of intramolecular modes of molecular liquids. By leveraging machine learning (ML) techniques to capture vibrational anharmonicity, intermolecular couplings, and bath correlation functions for each mode, sbml4md obviates empirical fitting and enables the modeling of environments with spatial and temporal heterogeneity. This work provides a set of parameters specifically tailored for the Hierarchical Equations of Motion (HEOM) framework, enabling numerically "exact" simulations of nonlinear vibrational spectra. Building upon our previous implementation for intramolecular vibrational modes [Park, Jo, and Tanimura, J. Chem. Phys. 163, 214104 (2025)], the present code enhances optimization efficiency by explicitly accounting for intermolecular vibrational contributions. This extension enables sbml4md to broaden the applicability of HEOM-based dynamical modeling by seamlessly integrating classical MD approaches, thereby providing a flexible and scalable framework for simulating both linear and nonlinear spectra under realistic conditions with minimal empirical input. The accompanying ML code, written in Python, is provided as supporting material. Intermolecular force ; Nonlinear system ; Anharmonicity ; Scalability ; Molecular dynamics ; Software ; Intramolecular force ; Noise (video) Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Molecular spectroscopy and chirality
Authors: Yichen Zheng | Kai Kang | Zaiyu Zhang | Hai Liu | Yi Liu | Yuehua Chen
Source: Extreme Mechanics Letters (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Physics-informed neural networks for bulge test modeling of general anisotropic two-dimensional crystalline materials with decoupled elasticity Isotropy ; Nonlinear system ; Anisotropy ; Constitutive equation ; Finite element method ; Characterization (materials science) ; Artificial neural network ; Elasticity (physics) ; Inverse Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Topological Materials and Phenomena
Authors: Kunal Khatri | Vineet Mehta
Source: arXiv (Cornell University) (preprint, 2026-03-25)
Field: Materials Science — Materials Chemistry

Deep Convolutional Neural Networks for predicting highest priority functional group in organic molecules Our work addresses the problem of predicting the highest priority functional group present in an organic molecule. Functional Groups are groups of bound atoms that determine the physical and chemical properties of organic molecules. In the presence of multiple functional groups, the dominant functional group determines the compound's properties. Fourier-transform Infrared spectroscopy (FTIR) is a commonly used spectroscopic method for identifying the presence or absence of functional groups within a compound. We propose the use of a Deep Convolutional Neural Networks (CNN) to predict the highest priority functional group from the Fourier-transform infrared spectrum (FTIR) of the organic molecule. We have compared our model with other previously applied Machine Learning (ML) method Support Vector Machine (SVM) and reasoned why CNN outperforms it. Convolutional neural network ; Functional group ; Group (periodic table) ; Pattern recognition (psychology) ; Functional near-infrared spectroscopy ; Support vector machine ; Organic molecules ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Dipayan Chatterjee | Sudeshna Kundu | Sowradeep Pal | Swarupa Das | Kanishka Majumder | Prativa Agarwalla
Source: Journal of Electronic Materials (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

PerovLearn: An Interpretable Ensemble Machine Learning Framework for Bandgap Prediction in Hybrid Halide Perovskite Solar Cell Materials Photovoltaic system ; Band gap ; Bridging (networking) ; Ensemble learning ; Perovskite (structure) ; Interpretability ; Random forest ; Density functional theory ; Energy conversion efficiency ; Generalization Machine Learning in Materials Science ; Perovskite Materials and Applications ; Thermal Expansion and Ionic Conductivity
Authors: Shin-Pon Ju | An-Zhi Lin | Shan-Han Lo | Hui-Lung Chen | Hsin-Tsung Chen | Hsing-Yin Chen
Source: Physica E Low-dimensional Systems and Nanostructures (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Anisotropic mechanical behavior and failure pathways of bilayer borophene: A deep learning molecular dynamics study Bilayer ; Borophene ; Zigzag ; Molecular dynamics ; Anisotropy ; Deformation (meteorology) ; Lipid bilayer mechanics ; Toughness Boron and Carbon Nanomaterials Research ; Graphene research and applications ; Carbon Nanotubes in Composites
Authors: Kerrin Janssen | Jan M. Wollschläger | Jonny Proppe | Andreas H. Göller
Source: Digital Discovery (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

When machine learning models learn chemistry I: quantifying explainability with matched molecular pairs Explainability methods in machine learning-driven research are increasingly being used, but it remains challenging to assess their reliability without deeply investigating the specific problem at hand. Reliability (semiconductor) ; Interpretability ; Feature (linguistics) ; Training set ; Pattern recognition (psychology) Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Computational Drug Discovery Methods
Authors: Xuetao Yan | Lifei Chen | Yuze Ren | Kaixuan Cui | Tianliang Li | Lin Li | Zeyu Li | Haicheng Song | Y. X. Chen | Zhenzhen Li | Lei Feng | Lingyan Feng
Source: Advanced Optical Materials (article, 2026-03-31)
Field: Chemistry — Organic Chemistry

Explainable Machine Learning for Solvents‐Driven G‐quartet Circularly Polarized Luminescence Material in Multilevel Anti‐counterfeiting ABSTRACT Circularly polarized luminescence (CPL) materials are highly sought after for their unique chiroptical properties, but their rational design remains challenging. To address this, a machine learning (ML) platform that intelligently screens deep eutectic solvents (DES) for crafting G‐quartet‐based gels with tailored CPL is established. An ML classifier trained on experimental data predicts CPL activity with 0.916 accuracy. SHapley Additive exPlanations (SHAP) analysis identifies key DES molecular descriptors, guiding the discovery of novel materials that exhibit a record‐high luminescence dissymmetry factor (g lum ) of 0.222 for nucleotide‐based systems and achieve chirality inversion via DES modulation. The Sure Independence Screening and Sparsifying Operator (SISSO) algorithm further yields interpretable design formulas. Leveraging the distinct polarization of these gels, we demonstrate a fourth‐level anti‐counterfeiting strategy, decrypting concealed information via Morse and ASCII codes to enhance information security. This ML‐driven methodology accelerates the discovery of high‐performance CPL materials and opens new design avenues. Luminescence ; ASCII ; Classifier (UML) ; Polarization (electrochemistry) ; Morse code ; Circular polarization ; Curse of dimensionality ; Optical sensing Synthesis and Properties of Aromatic Compounds ; Luminescence and Fluorescent Materials ; Luminescence Properties of Advanced Materials
Authors: J. Ren | Xiaoli Yue | Dapeng Yang | Liang Chen | Huimin Chen
Source: Research Square (preprint, 2026-02-19)
Field: Materials Science — Materials Chemistry

Study on predictive models for deformation control in continuous processing of flexible materials based on physics-informed neural networks Deformation (meteorology) ; Artificial neural network ; Transmission (telecommunications) ; Interval (graph theory) ; Adaptation (eye) ; Data processing ; Variable (mathematics) ; Mean squared error Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Neural Networks and Applications
Authors: Ema Slejko | Amaury Coste | Tilen Potisk | Julija Zavadlav | Matej Praprotnik
Source: The Journal of Chemical Physics (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

Achieving all-atom molecular dynamics accuracy from the Poisson–Boltzmann method through machine learning All-atom molecular dynamics (MD) simulations are a standard tool for probing the structural and dynamical properties of biomolecular systems, but their accuracy comes at the cost of high computational demands. To overcome spatial-temporal limitations, implicit models or coarse-graining are often employed, but usually at the expense of reduced accuracy. This limitation is also evident in the Poisson-Boltzmann (PB) mean-field theory, which efficiently captures long-range electrostatics but fails to account for crucial short-range interactions. In this work, we bridge this gap by introducing a graph neural network (GNN) Δ-learning approach trained on the difference between all-atom MD and PB, resulting in DIS-PB (deep implicit solvation model using the PB potential as a prior). DIS-PB, which models solutes and salt ions explicitly by MD while water is coarse-grained out, captures both short-range electrostatic correlations as well as long-range electrostatic interaction tails. Applied to a system of the DNA molecule in 1 mol l-1 salt solution, our method reproduces structural properties (NDPs, RDFs, and binding probability patterns) with high fidelity, showing that the GNN-corrected PB can reach the accuracy of all-atom MD at a lower computational cost. Molecular dynamics ; Electrostatics ; Solvation ; Artificial neural network ; Bridge (graph theory) ; Graph ; Complex system ; Salt bridge ; Computational model Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics