jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jorge L. Mejía-Méndez | Edwin E. Reza-Zaldívar | Diego E. Navarro-López | A. Sánchez-Martínez | O. Ceballos-Sánchez | Miguel Badillo | Angélica Lizeth Sánchez López | Miguel José Yacamán | Daniel A. Jacobo‐Velázquez | Edgar R. López-Mena | Gildardo Sanchez‐Ante
Source: Ultrasonics Sonochemistry (article, 2025-12-04)
Field: Materials Science — Materials Chemistry

From sonochemical synthesis to predictive modeling: Unraveling the antioxidant properties of La-doped CeO2 nanoparticles This study introduces a novel combination of sonochemical synthesis and machine learning (ML) modeling to analyze the effect of Lanthanum (La) doping on the biological properties of cerium oxide (CeO<sub>2</sub>) nanoparticles with various La concentrations (0, 1, 5, and 10 at.%). Ultrasonic-assisted synthesis enabled La incorporation into the CeO<sub>2</sub> lattice, resulting changes in crystallinity, lattice parameters, and surface features. Detailed characterization confirmed successful doping and indicated stable nanoparticle suspensions with controlled size ranges. Sonochemical synthesis promoted the oxidation of Ce<sup>3+</sup> to Ce<sup>4+</sup>. Biological testing revealed low cytotoxicity across various cell lines (HepG-2, 3 T3-L1, Caco-2, and U87) and increased antioxidant activity, especially in samples with 5 and 10 at.% La, which demonstrated improved free radical scavenging of DPPH and H<sub>2</sub>O<sub>2</sub> radicals. Notably, advanced ML models-including Extremely Randomized Trees, random forest, Gradient Boosting, and LightGBM-enabled the prediction of antioxidant activity based on nanoparticle features, identifying antioxidant method, concentration, and chemical composition as key factors influencing biological effects. This combined experimental and data-driven approach not only clarifies the structure-activity relationships of La-doped CeO<sub>2</sub> nanoparticles but also emphasizes the significant potential of ML in designing and optimizing nanomaterials for biomedical applications. The combination of sonochemical synthesis and ML modeling provides a robust framework for accelerating nanomaterial development by minimizing trial-and-error experiments and providing mechanistic insights into their biological functions. Nanomaterials ; Nanoparticle ; Antioxidant ; Cerium oxide ; Cerium ; Lanthanum ; Sonochemistry ; Biomolecule Advanced Nanomaterials in Catalysis ; Nanoparticles: synthesis and applications ; Catalytic Processes in Materials Science
Authors: Nack J. Kim | Jeong‐Moo Lee | Jung-Wook Wee
Source: Polymer Degradation and Stability (article, 2025-11-27)
Field: Materials Science — Polymers and Plastics

Prediction of mechanical properties of weathered injection-molded polycarbonate/acrylonitrile-butadiene-styrene blends using Fourier-transform infrared spectroscopy-based 1D convolutional neural network Degradation (telecommunications) ; Weathering ; Convolutional neural network ; Fourier transform infrared spectroscopy ; Infrared ; Artificial neural network ; Spectral properties Polymer crystallization and properties ; Fiber-reinforced polymer composites ; Injection Molding Process and Properties
Authors: Tiffany J. Callahan | Jie Shi | Kevin J. Cheng | Michael A. Sauer | Taras V. Pogorelov | Sara Capponi
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-08-22)
Field: Materials Science — Materials Chemistry

Interpretable Machine Learning Uncovers Structural Determinants of Wnt-Wls Binding Specificity from Extended Atomistic Simulations Abstract The Wnt protein family plays a critical role in cell development, with each Wnt protein interacting differently with the Wls membrane protein through distinct binding residues. A direct comparison and elucidation of the molecular mechanisms underlying Wnt–Wls binding across the diverse Wnt family remain challenging, owing to variations in sequence length and amino acid composition among Wnt proteins, which can affect their binding affinity and trafficking efficiency via Wls. Here we combine extended atomistic molecular dynamics simulations with supervised machine learning to elucidate binding specificity among four Wnt proteins, selected based on experimental structure availability and scientific relevance. We implement a local structure alignment algorithm to enable cross-system matching and comparison of residue interactions, and we apply a two-stage clustering strategy to reduce feature redundancy and facilitate robust feature selection. After training a Random Forest classifier that achieved high predicting accuracy, our feature importance analysis reveals both previously known and novel key residue pairs responsible for distinguishing among the Wnt systems. Our findings highlight that the binding specificity across different systems arises from the distributed nature of interactions across the protein binding surface and demonstrate how interpretable machine learning can effectively uncover crucial biophysical interactions. Importantly, our integrated strategy is generalizable to other systems and provides a data-driven approach for analyzing protein– protein interactions and guiding experimental validation or therapeutic targeting. Computer science ; Artificial intelligence ; Computational biology ; Biology Machine Learning in Materials Science
Authors: Mengxin Yang (10684155) | Guanlin Song (21798244) | Longhui Cheng (16511486) | Haisheng Ren (1609681)
Source: Figshare (article, 2025-07-28)
Field: Materials Science — Materials Chemistry

SMILES Token AdditivityModel with Interpretabilityand Generalizability for Fuel Property Predictions Deep learning models for the quantitative structure–property relationship (QSPR) have traditionally encountered challenges related to limited interpretability and generalizability. In this study, we present the simplified molecular input line entry system (SMILES) token additivity (STA) model for accurately predicting fuel properties, which takes SMILES as input and employs stacked multihead self-attention encoders to extract molecular structural information. This model provides insights into the structure–property relationships by quantifying the contributions of individual tokens to target properties. Furthermore, since the STA model operates without handcrafted molecular fingerprints, it is capable of generalizing to a broad spectrum of structure-related properties. To validate the model’s efficacy, seven critical fuel properties of standard enthalpy of formation (Δ<sub><i>f</i></sub><i>H</i>°), entropy (<i>S</i>), isobaric heat capacity (<i>C</i><sub>p</sub>), cetane number (CN), boiling point (BP), melting point (MP), and flash point (FP) were tested. The 10-fold cross-validation demonstrated outstanding predictive accuracy, with mean absolute errors of 1.86 kcal/mol (Δ<sub><i>f</i></sub><i>H</i>°), 0.62 kcal/mol/K (<i>S</i>), and 1.82 kcal/mol/K (<i>C</i><sub>p</sub>), alongside root-mean-square errors (RMSE) of 4.90 (CN), 11.27 °C (BP), 14.09 °C (MP), and 9.47 °C (FP). All properties achieved <i>R</i><sup>2</sup> values exceeding 0.95. The results demonstrate that it achieves predictive accuracy comparable to conventional machine learning models relying on sophisticated feature engineering while also identifying the effect of key tokens on Δ<sub><i>f</i></sub><i>H</i>° and CN. Interpretability ; Property (philosophy) ; Security token ; Feature (linguistics) ; Entropy (arrow of time) ; Flash point ; Spurious relationship ; Point (geometry) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Phase Equilibria and Thermodynamics
Authors: Parisa Mohammadi | Hamid Reza Shahdoosti | Shoeib Babaee Touski | Alireza Kokabi
Source: Physica B Condensed Matter (article, 2025-12-11)
Field: Materials Science — Materials Chemistry

Ferroelectric MInP <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si372.svg" display="inline" id="d1e8388"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:math> X <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si373.svg" display="inline" id="d1e8396"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>6</mml:mn> </mml:mrow> </mml:msub> </mml:math> (M= Cu, Ag, Au; X= S, Se and Te) monolayers for selective gas capture: A combined DFT and machine learning approach Adsorption ; Ferroelectricity ; Artificial neural network ; Polarization (electrochemistry) ; Feed forward ; Feedforward neural network ; Density functional theory 2D Materials and Applications ; Gas Sensing Nanomaterials and Sensors ; Advanced Thermoelectric Materials and Devices
Authors: Matteo T. Degiacomi | J A McKeown | Arron Bale | Cameron L. Brown | Hayden Fisher | Robert P. Rambo | Jonathan W. Essex | Christopher Prior
Source: Research Square (preprint, 2025-05-02)
Field: Materials Science — Materials Chemistry

Carbonara: A Rapid Method for SAXS-Based Refinement of Protein Structures Small-angle X-ray scattering ; Computer science ; Computational biology ; Biology ; Scattering ; Physics ; Optics Enzyme Structure and Function
Authors: Denis Andzheevich Sapegin | F. L. Bakharev | Dmitry Krupenya | Azamat Gafurov | Konstantin Pildish | Joseph C. Bear
Source: Digital Discovery (article, 2025-01-01)
Field: Chemistry — Spectroscopy

Moment of inertia as a simple shape descriptor for diffusion-based shape-constrained molecular generation We introduce MLConformerGenerator – an efficient machine-learning framework, guided by a concise shape descriptor for reliable molecule generation conditioned on reference conformers or arbitrary shapes. Moment of inertia ; Simple (philosophy) ; Moment (physics) ; Inertia ; Diffusion ; Statistical physics ; Mathematics ; Physics ; Classical mechanics ; Thermodynamics Analytical Chemistry and Chromatography ; Chemical Reactions and Isotopes ; Chromatography in Natural Products
Authors: Jae Won Choi (9094574) | Cheol Woo Ha (3982754)
Source: Figshare (article, 2025-09-23)
Field: Chemistry — Organic Chemistry

Development of ReversibleColor-Changing Three-DimensionalStructures Using a Thermochromic Resin in Digital Light Processing-BasedVat Photopolymerization The digital light processing-based vat photopolymerization (DLP-VPP) technology has demonstrated remarkable versatility and precision in producing complex structures across various fields. In this study, a thermochromic DLP-VPP system that achieves reversible color changes based on the temperature by combining thermochromic microcapsules (TM) with an acrylic photopolymer resin is proposed. Experiments are conducted to determine the optimal ratio of TM to achieve an ideal layer thickness without structural failure. The thermochromic properties of the TM resin composites are confirmed using differential scanning calorimetry and thermogravimetric analysis. Furthermore, experiments are performed to analyze the thermochromic reaction times at different heights, areas, and temperatures. Subsequently, a regression model is developed using machine learning techniques to predict the thermochromic reaction times. This study demonstrates the use of three-dimensional (3D) passive thermochromic sensors for visual temperature monitoring. Multimaterial DLP-VPP printing allows for the creation of 3D thermochromic QR codes, offering enhanced information security through temperature-dependent visibility. Overall, the proposed thermochromic DLP-VPP system has significant potential for applications in smart temperature sensors, anticounterfeiting, and cryptography. This study offers ideas for further research and development of functional materials for additive manufacturing. Thermochromism ; Photopolymer ; Thermogravimetric analysis ; Differential scanning calorimetry ; Digital Light Processing ; 3d printer ; Display device Photopolymerization techniques and applications ; Transition Metal Oxide Nanomaterials ; Polymer composites and self-healing
Authors: Emily Lın | Evelyn N. Wang | Sili Deng
Source: npj Computational Materials (article, 2025-11-20)
Field: Chemistry — Inorganic Chemistry

Neural ordinary differential equations (ODEs) for smooth, high-accuracy isotherm reconstruction, interpolation, and extrapolation Machine learning (ML) surrogate models offer a promising route to accelerate material property prediction, bypassing costly atomistic simulations. Here, we introduce IsothermODE, a neural ordinary differential equation (NODE) framework for reconstructing full uptake and heat of adsorption $$\left(\Delta {H}_{{\rm{ads}}}\right)$$ isotherms for CO2 adsorption in metal-organic frameworks (MOFs) using only sparse pressure data. Unlike traditional ML models, IsothermODE leverages the intrinsic structure of differential equations to produce smooth, physically-consistent predictions that generalize across wide pressure ranges. We demonstrate high-fidelity interpolation and extrapolation, even with only five pressure points. To address the stochasticity inherent in Grand Canonical Monte Carlo (GCMC) simulations, we integrate uncertainty quantification, yielding tight bounds on predicted enthalpy curves. We further interpret the learned latent dynamics in terms of adsorption thermodynamics and textural properties, offering insight into structure-property relationships. Finally, we demonstrate IsothermODE’s long-range interpolation and extrapolation capabilities with sparse isotherm data (5 pressure points) and large incomplete intervals featuring missing data between 4–40 (case 1) and 25–50 (case 2) bars. IsothermODE provides a fast, robust alternative to simulation-heavy workflows, enabling scalable screening and design of next-generation carbon capture materials. Extrapolation ; Interpolation (computer graphics) ; Ordinary differential equation ; Monte Carlo method ; Artificial neural network ; Differential equation ; Partial differential equation ; Missing data Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics
Authors: Marc U. Engelhardt (16642483) | Markus O. Zimmermann (1284975) | Finn Mier (21514038) | Frank M. Boeckler (509961)
Source: Figshare (article, 2025-06-10)
Field: Chemistry — Physical and Theoretical Chemistry

Comparison of QMMethods for the Evaluation of Halogen−πInteractions for Large-Scale Data Generation Halogen−π interactions play a pivotal role in molecular recognition processes, drug design, and therapeutic strategies, providing unique opportunities for enhancing and fine-tuning the binding affinity and specificity of pharmaceutical agents. The present study systematically benchmarks various combinations of quantum mechanical (QM) methods and basis sets to characterize halogen−π interactions in model systems. We evaluate both density functional theory (DFT) methods and wave function-based post-HF methods in terms of accuracy to reference calculations at the CCSD­(T)/CBS level of theory and runtime efficiency. By balancing these crucial aspects, we aim to identify an optimal configuration suitable for high-throughput applications. Our results indicate that MP2 using the reasonably large TZVPP basis set is in excellent agreement with reference calculations, striking a balance between accuracy and computational efficiency. This allows us to generate large, reliable data sets, which will serve as a basis to develop and train machine-learning models capable of accurately capturing the strength of halogen−π interactions, thereby providing a robust data-driven foundation for medicinal chemistry analysis. Basis (linear algebra) ; Set (abstract data type) ; Experimental data ; Density functional theory ; Data set ; Training set ; Reference data Crystallography and molecular interactions ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Xin Wen | Chengyan Han | Xiaoguang Liu | Kechen Song | Yu Han | Xingjie Li
Source: Coatings (article, 2025-11-26)
Field: Materials Science — Surfaces, Coatings and Films

A Method for Suppressing the Reflection of Coating Images on Aero-Engine Blades Surface inspection of aero-engine blades is critical for aero-engine production and maintenance. However, composite materials like titanium alloys and superalloys, as well as thermal barrier coatings on blades, exhibit distinct optical reflection properties, while their complex curved surfaces cause severe image reflections leading to overexposure, underexposure, edge blurring and reduced measurement accuracy. To solve this, we propose ELANet, a deep-learning-based multi-exposure image fusion method with DenseNet as the backbone. Its key innovations include two parts: first, an Efficient Channel Attention mechanism to capture reflection feature differences between substrate and coating, prioritizing resource allocation to anti-reflection channels; second, an Ultra-Lightweight Subspace Attention Mechanism with only one-fifth the parameters of traditional spatial attention that adaptively assigns weights to local features based on curved surface reflection laws, enhancing edge and detail extraction while reducing computational cost. The Efficient Channel Attention and Ultra-Lightweight Subspace Attention Mechanism synergistically address exposure and blurring issues. Validated against 12 mainstream methods via 9 quantitative metrics, ELANet achieves state-of-the-art performance: MEF-SSIM reaches 0.9472, which is 1.3% higher than the best comparative method, PSNR reaches 21.48 dB, which is 2.2 percent higher than the second-best method, and the average processing time is 0.48 s. Ablation experiments confirm the necessity of the Efficient Channel Attention and Ultra-Lightweight Subspace Attention Mechanism. This method effectively supports high-precision blade inspection. Reflection (computer programming) ; Channel (broadcasting) ; Subspace topology ; Enhanced Data Rates for GSM Evolution ; Feature (linguistics) ; Key (lock) ; Coating ; Feature extraction Optical Coatings and Gratings ; Surface Roughness and Optical Measurements ; Advanced Image Processing Techniques
Authors: Ting-Ju Wei
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-12-21)
Field: Materials Science — Materials Chemistry

Dataset for Foundation Model for Polycrystalline Material Informatics This repository hosts the datasets generated for the publication “Foundation Model for Polycrystalline Material Informatics.” The datasets were produced to support reproducibility, benchmarking, and methodological extension of the proposed foundation-model framework for polycrystalline materials. All datasets are fully compatible with the open-source implementation provided in the corresponding GitHub repository: https://github.com/BerryWei/Foundation-Model-for-Polycrystalline-Material-Informatics The repository includes voxel-based polycrystalline microstructures and associated material-response data used in both the pretraining and downstream evaluation stages of the study. Together with the released codebase, these datasets provide a complete and transparent research artifact for advancing machine-learning-driven materials informatics. Foundation (evidence) ; Context (archaeology) ; Materials informatics ; Crystallite ; Informatics Machine Learning in Materials Science ; Material Selection and Properties ; Composite Material Mechanics
Authors: Wei Feng | Siyuan Liu | Hongyi Wang | Zhenliang Mu | Zhichen Pu | Xu Han | Tianze Zheng | Zhenze Yang | Zhi Wang | Weihao Gao | Yidan Cao | Yu Kuang | Sheng Gong | Wen Yan
Source: Materials Today Energy (article, 2025-12-26)
Field: Materials Science — Materials Chemistry

Accelerated machine learning force field for predicting thermal conductivity of organic liquids Thermal conductivity ; Workflow ; Molecular dynamics ; Force field (fiction) ; Field (mathematics) ; Conductivity ; Thermal ; Ab initio Machine Learning in Materials Science ; Thermal properties of materials ; Phase Equilibria and Thermodynamics
Authors: Moritz Bensberg (8862209) | Marco Eckhoff (6751847) | F. Emil Thomasen (8846753) | William Bro-Jørgensen (5641484) | Matthew S. Teynor (8921255) | Valentina Sora (8955257) | Thomas Weymuth (1767655) | Raphael T. Husistein (21987694) | Frederik E. Knudsen (21987697) | Anders Krogh (17846) | Kresten Lindorff-Larsen (317515) | Markus Reiher (1282191) | Gemma C. Solomon (1301760)
Source: Figshare (article, 2025-08-05)
Field: Materials Science — Materials Chemistry

Machine Learning-EnhancedCalculation of Quantum-ClassicalBinding Free Energies Binding free energies are key elements in understanding and predicting the strength of protein–drug interactions. While classical free energy simulations yield good results for many purely organic ligands, drugs, including transition metal atoms, often require quantum chemical methods for an accurate description. We propose a general and automated workflow that samples the potential energy surface with hybrid quantum mechanics/molecular mechanics (QM/MM) calculations and trains a machine learning (ML) potential on the QM/MM energies and forces to enable efficient alchemical free energy simulations. To represent systems including many different chemical elements efficiently and to account for the different descriptions of QM and MM atoms, we propose an extension of element-embracing atom-centered symmetry functions for QM/MM data as an ML descriptor. The ML potential approach takes electrostatic embedding and long-range electrostatics into account. We demonstrate the applicability of the workflow on the well-studied protein–ligand complex of myeloid cell leukemia 1 and the inhibitor 19G and on the anticancer drug NKP1339 acting on the glucose-regulated protein 78. Workflow ; Electrostatics ; Quantum ; Embedding ; Energy (signal processing) ; Quantum chemical ; Surface (topology) ; Yield (engineering) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jae Won Choi | Cheol Woo Ha
Source: ACS Applied Materials & Interfaces (article, 2025-09-18)
Field: Chemistry — Organic Chemistry

Development of Reversible Color-Changing Three-Dimensional Structures Using a Thermochromic Resin in Digital Light Processing-Based Vat Photopolymerization The digital light processing-based vat photopolymerization (DLP-VPP) technology has demonstrated remarkable versatility and precision in producing complex structures across various fields. In this study, a thermochromic DLP-VPP system that achieves reversible color changes based on the temperature by combining thermochromic microcapsules (TM) with an acrylic photopolymer resin is proposed. Experiments are conducted to determine the optimal ratio of TM to achieve an ideal layer thickness without structural failure. The thermochromic properties of the TM resin composites are confirmed using differential scanning calorimetry and thermogravimetric analysis. Furthermore, experiments are performed to analyze the thermochromic reaction times at different heights, areas, and temperatures. Subsequently, a regression model is developed using machine learning techniques to predict the thermochromic reaction times. This study demonstrates the use of three-dimensional (3D) passive thermochromic sensors for visual temperature monitoring. Multimaterial DLP-VPP printing allows for the creation of 3D thermochromic QR codes, offering enhanced information security through temperature-dependent visibility. Overall, the proposed thermochromic DLP-VPP system has significant potential for applications in smart temperature sensors, anticounterfeiting, and cryptography. This study offers ideas for further research and development of functional materials for additive manufacturing. Thermochromism ; Photopolymer ; Thermogravimetric analysis ; Differential scanning calorimetry ; Digital Light Processing ; 3d printer ; Display device Photopolymerization techniques and applications ; Photochromic and Fluorescence Chemistry ; Interactive and Immersive Displays