jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ying Cao | Yuchen Zhou | Xiaodan Liu | Dazhou Li
Source: Journal of Chemical Information and Modeling (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

Geometry-Driven Attention Model with 3D Molecular Features for Multi-Property Prediction of OLED Materials As Organic Light-Emitting Diode (OLED) technology advances in applications such as high-end displays, medical devices, and VR/AR systems, the development of high-performance materials that improve energy efficiency and support environmental sustainability has become increasingly important. Traditional methods like Density Functional Theory (DFT) are accurate but computationally expensive, motivating the need for efficient prediction tools. This paper presents 3D-SAT-OLED, a deep learning model based on three-dimensional self-attention to predict key OLED performance parameters. The model integrates 3D molecular structures with a Gaussian kernel-based feature extractor (GPE) and spatial position encoding, while Dynamic Tanh (DyT) and Swish-Gated Linear Unit (SwiGLU) activations enhance stability and nonlinear modeling. Experiments show that 3D-SAT-OLED achieves state-of-the-art performance on QM9 and OLED QM properties data sets, significantly outperforming existing models across <i>R</i><sup>2</sup>, MAE, and other metrics, while demonstrating strong generalization and predictive accuracy. OLED ; Generalization ; Nonlinear system ; Stability (learning theory) ; Feature (linguistics) ; Key (lock) ; Scalability ; Extractor Machine Learning in Materials Science ; Organic Light-Emitting Diodes Research ; Green IT and Sustainability
Authors: Jaeuk Sung | S.B. Heo | Do‐Hyun Kim | Youngmee Kwon | Jinyoung You | Yanggeun Joo | Eunji Hwang | Jungyu Lee | Sang‐Joon Cho | Heejun Yang | Yunseok Kim
Source: Small Structures (article, 2025-09-23)
Field: Materials Science — Surfaces, Coatings and Films

Autonomous AI‐Driven Measurement and Characterization of 2D Materials Using Scanning Probe Microscopy Scanning probe microscopy (SPM) has become a valuable tool for probing physical properties and nanoscale materials and devices. However, conventional SPM imaging requires manual identification of regions of interest and heavily depends on human intuition for image interpretation, which severely limits the ability to collect large datasets and conduct objective analysis. In this work, an AI‐assisted autonomous SPM framework is presented for microstructural and electrical property characterization of 2D materials with high efficiency. By analyzing topographic features through advanced clustering algorithms, the approach employs accurate image segmentation of complex geometries and multilevel thickness variations in overlapping 2D MoWTe 2 flakes. To demonstrate its scalability, this autonomous workflow is applied to over 100 MoWTe 2 flakes. Levering SPM's multimodal imaging capabilities, the framework simultaneously extracts flake thickness and work function, allowing for direct correlation between these properties. This deep‐learning‐driven autonomous approach mitigates the need for manual intervention, significantly accelerating the exploration and characterization of nanomaterials across diverse material systems. Characterization (materials science) ; Workflow ; Nanoscopic scale ; Scanning probe microscopy ; Intuition ; Segmentation ; Microscopy ; Cluster analysis Electron and X-Ray Spectroscopy Techniques ; Force Microscopy Techniques and Applications ; Advanced Electron Microscopy Techniques and Applications
Authors: Liwen He | Guang Chen | Lidan Lu | Jian Zhen Ou | Jieyu Yang | Lianqing Zhu
Source:  (article, 2025-02-20)
Field: Materials Science — Surfaces, Coatings and Films

Approach to tuning phase for periodical subwavelength grating based on Sb₂Se₃-incorporated silicon metalines Relying on periodic alignment, dense integration and sensitive optical properties, silicon waveguide with subwavelength grating (SWG) plays an important role in area of micro-nano integrated optics. Currently, diffractive deep neural networks for passive grating component are not reconfigurable, which means their function has been fixed once fabricated. As Sb<sub>2</sub>Se<sub>3</sub> material has the advantage of post-design programmability, we propose a scheme that fills Sb<sub>2</sub>Se<sub>3</sub> material into the slot of the periodic SWG structure to obtain the programmable phase-change. By changing the refractive index of phase-change material (PCM) and slot structural parameters, the impact of phase-change on diffractive function are obtained, which makes the characterization of components performance reconfigurable. By using the finite-difference time-domain (FDTD) method, phase-changing are more sensitive to slot length than slot width. With the change of the refractive index, the phase can be tuned from 0 to 360&deg; with the slot length 2.5&mu;m, width 220nm of Sb<sub>2</sub>Se<sub>3</sub>. Thanks to the programmable property of silicon metalines, our proposed design has broad potentials for applications in sophisticated phase management, photonic convolution neural networks, high-speed optical communications, distributed feedback grating filters, waveguide antennas, lens modal confinement, etc. Silicon ; Grating ; Materials science ; Phase (matter) ; Optoelectronics ; Diffraction grating ; Optics ; Physics Optical Coatings and Gratings ; Photonic and Optical Devices ; Photonic Crystals and Applications
Authors: Akos-Etele Csibi | Hans Sandén | Pavel Baykalov | Ruth Pereira | Anabela Cachada | Boris Rewald | David Perry
Source:  (preprint, 2025-03-15)
Field: Chemistry — Analytical Chemistry

Developing machine learning based models for soil parameters prediction and mapping using Vis-NIR spectroscopic data The application of Vis-NIR spectroscopy for physico-chemical soil properties estimations, like soil organic carbon, and digital soil mapping for the scopes of enhancing precision agriculture, promote soil carbon sequestration and improve soil health is fastly developing thanks to the use of machine learning algorithms and big data handling.With our Subterra Green device, developed by S4 Mobile laboratories, a mobile field unit equipped with a visible and near infrared (VNIR) spectrometer and a load cell for measuring probe insertion force, we are able to collect spectroscopic data until 90 cm underground, down to a 1 cm resolution.As part of the EU founded PHENET project, among many others, one specific scope is to conduct soil surveys among various soil types, including highly fertile chernozems, to less productive gleyic or cambisols. Samples collection for training and testing of machine learning based models, takes place from the humid continental zones of Austria to the temperate oceanic climate of Portugal. Ground-truthing data is verified with laboratory biochemical analysis of the selected soil samples.&amp;#160;The ultimate goal would be to estimate important soil parameters in-situ and provide digital soil maps on larger scales (several hectares), providing this with the highest accuracy possible by using pre-processing techniques such as external parameter orthogonalization or direct standardization to correct detrimental effects caused by varying water content, bulk density, soil texture etc.Developing precise machine learning based models using Vis-NIR spectroscopy and subsequently generating high-resolution digital soil maps leads us to fast, non-destructive and cost-effective monitoring of soil physico-chemical properties over space and time. Computer science ; Artificial intelligence ; Machine learning ; Remote sensing ; Geography Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Harpriya Minhas (13220737) | Rahul Kumar Sharma (3861805) | Biswarup Pathak (1491820)
Source: Figshare (article, 2025-05-13)
Field: Materials Science — Materials Chemistry

Exploring CombinedStereochemically Active Lone-Pairand Rattling Effects in Thermoelectrics with Machine Learning Potentials Stereochemically active lone pairs (SCALPs) are recognized for their ability to break local symmetry, induce lattice anharmonicity, and influence thermoelectric properties. Similarly, rattling atoms influence the thermal conductivity by introducing additional vibrational modes that disrupt phonon transport. SCALPs containing pnictogen chalcogenides alongside rattling atoms pose challenges for calculating thermal transport properties using <i>ab initio</i> methods because of their noncentrosymmetric structures. Here, we employ machine learning interatomic potentials (MLIPs) to explore the combined effects of SCALPs and rattling atoms in AAsSe<sub>2</sub> (A = Li or Na). The strong anharmonicity in the γ-NaAsSe<sub>2</sub> system arises from rattling modes and active As 4s<sup>2</sup> SCALP-induced electrostatic interactions, leading to reduced lone-pair angles. The unique chemical bonding behind the high anharmonicity is attributed to antibonding states near the valence band and the flat vibrational behavior of rattler atoms, induced phonon softening, structural distortions, and enhanced phonon scattering. This work highlights the combined effects of SCALPs and rattling atoms, leveraging MLIPs to accelerate thermoelectric material design. Anharmonicity ; Antibonding molecular orbital ; Pnictogen ; Phonon ; Lone pair ; Thermoelectric effect ; Thermoelectric materials ; Thermal conductivity Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Topological Materials and Phenomena
Authors: Chu Wang | Lin Liu | Wei Sun | Juanjuan Wang | Q LI | Kai Feng | Bin Liao | Wei‐Hai Fang | Xuebo Chen
Source: Advanced Optical Materials (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

Discovery of Novel Iridium(III) Complex‐Based Photofunctional Materials Using Excited‐State Descriptors (Advanced Optical Materials 7/2025) Novel Iridium(III) Complex-Based Photofunctional Materials Discovery Designing Ir(III) complex-based photofunctional materials for OLEDs and photocatalysis presents significant challenges. In article 2402317, Bin Liao, Xuebo Chen, and co-workers propose a three-step, data-driven workflow that leverages machine learning, time-dependent density functional theory (TDDFT), and complete active space perturbation theory (CASPT2) coupled with complete active space self-consistent field (CASSCF) methods. By incorporating critical excited-state descriptors, a promising uracil-based Ir(III) complex is identified from the self-constructed database. Iridium ; Materials science ; Excited state ; Nanotechnology ; Engineering physics ; Optoelectronics ; Organic chemistry ; Atomic physics ; Physics ; Catalysis Lanthanide and Transition Metal Complexes ; Perovskite Materials and Applications ; Inorganic Chemistry and Materials
Authors: Gisela Ibáñez Redín | Daniel Cesar Braz | Débora Gonçalves | Osvaldo N. Oliveira
Source: Journal of the Brazilian Chemical Society (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning to Treat Data for the Design and Improvement of Electrochemical Sensors: Application for a Cancer Biomarker Label-free immunosensors based on screen-printed carbon electrodes offer a promising platform for the detection of cancer biomarkers. Herein, we explore the use of machine learning techniques to improve the performance of these immunosensors. We evaluate the influence of various redox probes on the analytical response in detecting the cancer biomarker protein p53. Ascorbic acid (AA) was found as the optimal redox probe, exhibiting a sensitivity of 0.26 ng mL-1, attributed to its strong affinity to proteins through hydrogen bonds and electrostatic interactions. We also extracted analytical information from the voltammograms, such as shifts in peak potential and changes in peak width, to construct datasets for supervised machine learning. Using different algorithms including logistic regression, linear discriminant analysis, K-nearest neighbor, Gaussian Naive-Bayes, decision trees, and support vector machine, we identified positive samples spiked with p53 in artificial urine and saliva samples. Through a comparison of immunosensors with distinct molecular architectures, we determined the critical role of redox probe selection, which proves to be more significant than modifying the working electrodes in determining performance. Furthermore, immunosensors with inferior inherent detection ability can achieve comparable performance to those with superior analytical characteristics when feature selection and machine learning algorithms are applied to the voltammograms. These findings illustrate the significance of extracting additional information from differential pulse voltammograms beyond peak current intensity. Furthermore, using machine learning techniques allows one to design biosensors capable of distinguishing biomarkers even in complex samples. Biomarker ; Computer science ; Nanotechnology ; Medicine ; Machine learning ; Materials science ; Chemistry ; Biochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Yadong Zhang | Jinjia Cao | Xiaochang Zheng | Yanjun Wang | Yulong Zhang | Wei Chen | Huajun Yuan
Source: Applied Radiation and Isotopes (article, 2025-12-04)
Field: Materials Science — Materials Chemistry

An efficient method for determining the mass attenuation coefficient of contrast agent–water mixtures and analysis of influencing factors in medical imaging systems Monte Carlo method ; Contrast (vision) ; Hyperparameter ; Attenuation ; Random forest ; Hyperparameter optimization ; Medical imaging ; Energy (signal processing) Radiation Shielding Materials Analysis ; Advanced X-ray and CT Imaging ; Radiation Dose and Imaging
Authors: Kerushan Thomas | K. Joby Thomas | A. S. Sabu | Chris Varghese | Reeja Jhonson
Source: Journal of Bio- and Tribo-Corrosion (article, 2025-09-13)
Field: Materials Science — Materials Chemistry

Modelling the Corrosion Inhibition Efficiency of Garcinia Cambogia Extract on Mild Steel in Acid Media Using ANN and RSM Approaches Physisorption ; Corrosion ; Response surface methodology ; Corrosion inhibitor ; Electrochemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Anodic Oxide Films and Nanostructures
Authors: Jian Yang | Ruiliang Zhou | Hailong Liu | Xu Chen | Yu Pu | Ivan S. Babichuk
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

A Lightweight Model and Agent Framework for Fast and Accurate Surface Defect Detection in Mos2 Films Surface (topology) ; Materials science ; Computer science ; Mathematics ; Geometry Machine Learning in Materials Science
Authors: Senk, Valentin
Source: reposiTUm (TU Wien) (article, 2025-12-01)
Field: Materials Science — Polymers and Plastics

Mechanics of plant fiber-reinforced composite materials: from micro–meso modeling to interactive frameworks for material-informed design in early planning phases Plant fiber-reinforced polymer composites (biocomposites) offer renewable, lightweight alternativesfor the built environment and can help reduce embodied carbon. Their structural adoption,however, remains limited by a combination of factors, including variability in constituent properties,fiber-matrix adhesion challenges, the breadth of possible material architectures, and ascarcity of harmonized test methods and structural design guidelines. In this context, this thesisaddresses two complementary needs: (i) predictive, numerical modeling of stiffness, strength,and failure, and (ii) integration of such knowledge into early design tools that lower knowledgebarriers for architects and engineers.Nonlinear finite element models at various scales are developed to capture the coupled failuremechanisms of biocomposites. A two-fiber unit cell incorporates matrix softening, interfacefailure, and fiber rupture, reproducing nonlinear responses reported in the literature and resolvinginteractions among damage mechanisms. Sensitivity studies highlight the decisive role ofinterface behavior, motivating a second study in which microdroplet tests are simulated acrosswide ranges of geometric/mechanical parameters. A neural-network surrogate reproduces fullload-displacement curves and enables efficient parameter studies, yielding traction-separationparameters for the interface that transfer from micro- to mesoscale (demonstrated for a flaxpolypropylenecomposite).A sketch-to-analysis framework connects stylus-based 3D input to analysis-ready modelsand couples it with a micromechanics module that supplies elastic stiffness and limits forvaried biocomposite materials and microstructures (e.g., fiber orientation and volume fraction),providing immediate feedback in early iterations. The workflow is expanded to extended reality,and complemented by haptic feedback: a collaborative robot with a touchable plate prop provideslocal stiffness responses so users can physically sense differences among material/structuralchoices. By coupling haptic and visual feedback, the system enhances communication of structuralperformance and lowers the knowledge barriers for engaging with novel materials.Within the broader materials-to-structures process chain, these contributions address a subsetof issues—predictive mechanics and support for early, material-informed design decisions—to aidcredible consideration of biocomposites in structural applications. Stiffness ; Micromechanics ; Workflow ; Finite element method ; Stiffness matrix ; Haptic technology ; Biocomposite ; Actuator ; Interface (matter) ; Coupling (piping) Natural Fiber Reinforced Composites ; Tree Root and Stability Studies ; Composite Material Mechanics
Authors: Ganesh Sahadeo Meshram | Suman Chakraborty | P. P. Chakrabarti
Source: Procedia Computer Science (article, 2025-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Demarcating Wetting States Classification of the Textured Surfaces Using Machine Learning The accurate categorization and anticipation of wetting conditions on surfaces with texture play a crucial role in the advancement of specialty materials that possess customized characteristics suitable for various applications, such as self- cleaning surfaces, anti-fouling coatings, and fluid manipulation technologies. The conventional approaches for assessing wetting states, predominantly relying on contact angle measurements, encounter constraints in terms of operational efficiency, scalability, and responsiveness, especially when confronted with intricate surface characteristics. The present study investigates the use of machine learning (ML) models to address these obstacles, presenting an innovative strategy for the automated categorization and predictive modeling of wetting phenomena on engineered surfaces. The experimental findings provide evidence of the effectiveness of machine learning models in properly classifying hydrophilic, hydrophobic, and superhydrophilic states, resulting in a substantial reduction in the time and effort required compared to conventional estimation techniques. From this present study, it has been found that Random Forest and XGBoost models are found to be more accurate in classifying the surfaces based on hydrophobicity. Computer science ; Wetting ; Artificial intelligence ; Machine learning ; Composite material ; Materials science Surface Modification and Superhydrophobicity ; Surface Roughness and Optical Measurements ; Textile materials and evaluations
Authors: Jiawen Li | Jiang Guo | Yuanzhe Li | Zetian Mao | Jiaxing Shen | Ting Xu | Diptesh Das | J. He | Run Hu | Yaerim Lee | Koji Tsuda | Junichiro Shiomi
Source: ArXiv.org (preprint, 2025-06-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse Design of Metamaterials with Manufacturing-Guiding Spectrum-to-Structure Conditional Diffusion Model Metamaterials are artificially engineered structures that manipulate electromagnetic waves, having optical properties absent in natural materials. Recently, machine learning for the inverse design of metamaterials has drawn attention. However, the highly nonlinear relationship between the metamaterial structures and optical behaviour, coupled with fabrication difficulties, poses challenges for using machine learning to design and manufacture complex metamaterials. Herein, we propose a general framework that implements customised spectrum-to-shape and size parameters to address one-to-many metamaterial inverse design problems using conditional diffusion models. Our method exhibits superior spectral prediction accuracy, generates a diverse range of patterns compared to other typical generative models, and offers valuable prior knowledge for manufacturing through the subsequent analysis of the diverse generated results, thereby facilitating the experimental fabrication of metamaterial designs. We demonstrate the efficacy of the proposed method by successfully designing and fabricating a free-form metamaterial with a tailored selective emission spectrum for thermal camouflage applications. Metamaterial ; Camouflage ; Inverse ; Fabrication ; Range (aeronautics) ; Metamaterial cloaking ; Inverse problem ; Generative Design ; Nonlinear system Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Thermal Radiation and Cooling Technologies
Authors: R. Gowri Shankar Rao | Jawahar Natarajan | Samanwita Khanra | Jey Kumar Pachiyappan | Imrankhan Nizam | Akshay Muralidhara | Jubie Selvaraj | S Selvamuthukumar
Source: AAPS PharmSciTech (article, 2025-10-03)
Field: Chemistry — Physical and Theoretical Chemistry

Advances in Pharmaceutical Cocrystals and Nano-Cocrystals: Strategies for Enhancing Solubility and Translating to Clinical Use Biopharmaceutical ; Cocrystal ; Solubility ; Dissolution ; Pharmaceutical technology ; Process (computing) Crystallography and molecular interactions ; Crystallization and Solubility Studies ; Chemical Thermodynamics and Molecular Structure
Authors: Shen Fang (14651458) | Hongkun Dou (21396769) | Zeyu Li (4371955) | Wang Han (11415590) | Qingfei Fu (21396772) | Lijun Yang (356935)
Source: Figshare (article, 2025-05-21)
Field: Materials Science — Materials Chemistry

Expectation–Maximization-BasedOptimizationof Neural Quantum States for Ab Initio Quantum Chemistry Accurate solutions for ab initio quantum chemistry are critical for understanding chemistry on the atomic scale. Recently, optimization methods for neural quantum states (NQSs) based on deep neural networks have shown great potential for improving computational accuracy over traditional methods, but they inevitably introduce the challenge of substantial computational costs. To address this challenge, this study proposed expectation–maximization-based optimization of NQS (EMO-NQS), a novel method developed to accelerate optimizations through cost amortization. Specifically, the EMO-NQS method reformulates time-intensive operations as the E-step, amortizing their costs through replays of efficient network-updating operations in the M-step to reduce the overall computational load. Furthermore, an active learning (AL)-based sampling method was introduced to enhance the robustness of EMO-NQS. The performance of EMO-NQS was validated by optimizing the NQS of the equilibrium structures for various molecules. The results demonstrated that EMO-NQS achieved optimization speeds at least twice as fast as those of the vanilla NQS method without compromising accuracy. Furthermore, the consistent acceleration of optimization and the robustness of accuracy facilitated by the AL-based EMO-NQS method were demonstrated by an additional optimization of the construction of the potential energy surface of the N<sub>2</sub> molecule. Robustness (evolution) ; Quantum chemistry ; Ab initio ; Quantum ; Potential energy surface ; Optimization problem ; Ab initio quantum chemistry methods ; Artificial neural network Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum many-body systems