jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yongjun Yang | Haitao Chang | Baojian Qian | Yifan Gao | Xinyu Yang
Source: Measurement Sensors (article, 2025-01-11)
Field: Chemistry — Spectroscopy

UV absorption tomography of temperature and OH radical distribution based on neural network algorithm Temperature and OH concentration are crucial parameters that influence the combustion reaction and serve as indicators of the combustion state. The spatial distribution measurement of these parameters has garnered significant attention in the field of combustion diagnostics. In this study, a two-dimensional (2D) planar broadband absorption tomography (BAT) method was developed. We proposed a general BAT scheme, which combines the single-temperature approximation with MAART algorithm, as well as a two-step neural network BAT scheme. Through simulations, we achieved a temperature reconstruction error of approximately 3 % with a calculation time on the order of 10s for the proposed general BAT scheme. Furthermore, we reduced the calculation time to milliseconds for the neural network BAT scheme. In our experimental application, we utilized the established BAT method to measure 2D planar temperature distributions at various flame heights in a Bunsen flame. The results clearly demonstrated the flame structure at different heights and are consistent with CFD simulation results, validating the practical measurement capability of our established BAT method. Tomography ; Distribution (mathematics) ; Artificial neural network ; Absorption (acoustics) ; Algorithm ; Computer science ; Materials science ; Physics ; Artificial intelligence ; Optics ; Mathematics ; Mathematical analysis Spectroscopy and Laser Applications ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Xianfeng Wu | Zhao Jing | Kunlun Xie | Xiaopeng Zhao
Source: Nanomaterials (article, 2025-01-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep Learning Design for Loss Optimization in Metamaterials Inherent material loss is a pivotal challenge that impedes the development of metamaterial properties, particularly in the context of 3D metamaterials operating at visible wavelengths. Traditional approaches, such as the design of periodic model structures and the selection of noble metals, have encountered a plateau. Coupled with the complexities of constructing 3D structures and achieving precise alignment, these factors have made the creation of low-loss metamaterials in the visible spectrum a formidable task. In this work, we harness the concept of deep learning, combined with the principle of weak interactions in metamaterials, to re-examine and optimize previously validated disordered discrete metamaterials. The paper presents an innovative strategy for loss optimization in metamaterials with disordered structural unit distributions, proving their robustness and ability to perform intended functions within a critical distribution ratio. This refined design strategy offers a theoretical framework for the development of single-frequency and broadband metamaterials within disordered discrete systems. It paves the way for the loss optimization of optical metamaterials and the facile fabrication of high-performance photonic devices. Metamaterial ; Robustness (evolution) ; Computer science ; Context (archaeology) ; Photonic metamaterial ; Broadband ; Photonics ; Materials science ; Material Design ; Nanotechnology ; Flexibility (engineering) ; Electronic engineering ; Optoelectronics ; Telecommunications ; Engineering ; Mathematics Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Advanced Antenna and Metasurface Technologies
Authors: R. A. Oshiya (18850706) | Arko Mohari (21239784) | Ayan Datta (1268127)
Source: Figshare (article, 2025-05-03)
Field: Chemistry — Organic Chemistry

Machine LearningPredicts Regioselectivity in Pd-CatalyzedDirecting Group-Assisted C–H Activation Regioselectivity in C–H activation is crucial for applications in pharmaceuticals and advanced materials. Machine learning (ML) techniques enhance the speed and accuracy of regioselectivity prediction in palladium-catalyzed directing group-assisted C–H activation in aryl substrates. Among the models tested, the standard support vector machine (SVM) model demonstrated better generalizability, achieving an F1 score of 0.92 and MCC of 0.93 on the test set. This approach aids the development of efficient catalytic strategies for C–H activation. Regioselectivity ; Support vector machine ; Statistical learning ; Power (physics) ; Feature (linguistics) Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions ; Chemical Reactions and Isotopes
Authors: Hengtong Wang | Haijiang Min | Minghao Gao | Zhong Han | Zhong Chuan | Željko Ban | Qin Zhou | Wang Xiao | Jian Cai | Qing Li | Mei Huang | Dong Jiang | Shan Xu | Yingxin Zhong
Source: Journal of Cereal Science (article, 2025-11-01)
Field: Chemistry — Spectroscopy

Development of a rapid method for the determination of the spatial distribution of protein in wheat grain using mature resin section and image processing algorithm Endosperm ; Slicing ; Image processing ; Interference (communication) ; Wheat grain ; Pattern recognition (psychology) ; Common wheat ; Pixel Advanced Proteomics Techniques and Applications ; Cell Image Analysis Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Zhaowei Zhu | Shunbo Hu | Yin Wang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Application of Literature Datasets and Knowledge Transfer to Accelerate Material Design Field (mathematics) ; Adaptation (eye) ; Predictive modelling ; Data modeling ; Transfer of learning ; Experimental data ; Domain adaptation ; Value (mathematics) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Ruixuan Qi | H.X. Li | Yifei Zhang | Weiqing He | Wangdong Yang
Source:  (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Development and Validation of a Neural Network Interatomic Potential for Silicon Carbide Silicon carbide (SiC), as a third-generation wide-bandgap semiconductor, holds great promise for applications in high-temperature, high-power, and extreme-environment devices. To address the challenge of balancing accuracy and computational efficiency in traditional numerical simulation methods, this study proposes a modeling framework that integrates first-principles calculations with neural network potentials (NNPs). A high-quality dataset was constructed using a closed-loop active learning approach that iteratively trained, explored, and labeled. The resulting NNP model achieves density functional theory (DFT)-level accuracy in predicting both energies and atomic forces, with errors of less than 1.52 meV/atom and 21.00 meV/Å, respectively. Structural property validation shows that the radial distribution function (RDF) predicted by the NNP matches DFT results in terms of peak positions and shapes, significantly outperforming empirical potentials such as Tersoff and 2NN-MEAM. In terms of dynamical properties, the phonon dispersion curves—including both acoustic and optical modes—also exhibit excellent agreement with DFT. This research not only provides an efficient tool for the study of SiC at the atomic scale but also offers a generalizable modeling workflow applicable to other wide-bandgap semiconductors and heterointerface systems. Silicon carbide ; Workflow ; Artificial neural network ; Dispersion (optics) ; Phonon ; Function (biology) ; Silicon ; Density functional theory ; Interatomic potential Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Silicon Carbide Semiconductor Technologies
Authors: Huitao Yu | Zhiqiang Zhao | Zhuhua Zhang | Yong‐Wei Zhang | Jin-Wu Jiang
Source: ArXiv.org (preprint, 2025-10-08)
Field: Materials Science — Materials Chemistry

Anomalous strain-dependent thermal conductivity in superelastic screw-dislocated graphites The design of strain-stable, or even strain-enhanced thermal transport materials is critical for stable operation of high-performance electronic devices. However, most nanomaterials suffer from strain-induced degradation, with even minor tensile strains markedly reducing thermal conductivity. Here, we demonstrate that screw-dislocated graphites (SDGs), recently identified as topological semimetals, display an unusual increase in cross-plane thermal conductivity under both tensile and compressive strains, revealed by high-accuracy machine-learning-potential-driven non-equilibrium molecular dynamics. Notably, SDGs exhibit over 100% enhancement under tensile strains up to 80% along the dislocation axis, arising from strain-induced increase in dislocation interface tilt angle that elongates the effective heat transfer paths. Their thermal conductivity surpasses multilayer graphene by an order of magnitude. An analytical model is further derived linking thermal conductivity to dislocation number and strain, offering a predictive framework for designing strain-tunable screwdislocated structures. These findings highlight SDGs as a promising platform for high-performance electronic and wearable devices with tunable thermal properties. Thermal conductivity ; Dislocation ; Ultimate tensile strength ; Thermal conduction ; Thermal ; Graphene ; Nanomaterials Graphite, nuclear technology, radiation studies ; Advanced ceramic materials synthesis ; Thermal properties of materials
Authors: Jiaqi Guan | Jiahan Li | Xiangxin Zhou | Xingang Peng | Sheng Wang | Yunan Luo | Jian Peng | Jianzhu Ma
Source: ArXiv.org (preprint, 2025-01-25)
Field: Chemistry — Organic Chemistry

Group Ligands Docking to Protein Pockets Molecular docking is a key task in computational biology that has attracted increasing interest from the machine learning community. While existing methods have achieved success, they generally treat each protein-ligand pair in isolation. Inspired by the biochemical observation that ligands binding to the same target protein tend to adopt similar poses, we propose \textsc{GroupBind}, a novel molecular docking framework that simultaneously considers multiple ligands docking to a protein. This is achieved by introducing an interaction layer for the group of ligands and a triangle attention module for embedding protein-ligand and group-ligand pairs. By integrating our approach with diffusion-based docking model, we set a new S performance on the PDBBind blind docking benchmark, demonstrating the effectiveness of our proposed molecular docking paradigm. Docking (animal) ; Group (periodic table) ; Protein–ligand docking ; Computer science ; Chemistry ; Computational biology ; Stereochemistry ; Biology ; Medicine ; Virtual screening ; Organic chemistry Click Chemistry and Applications ; Computational Drug Discovery Methods ; Monoclonal and Polyclonal Antibodies Research
Authors: Peng Ding | Lin Liu | Binbin Xie | Lin Shen | Xuebo Chen | Wei‐Hai Fang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Stacking Machine Learning Models for Predicting Photophysical Properties of Iridium Complexes Iridium ; Stacking ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry Machine Learning in Materials Science
Authors: Jolla Kullgren | Peter Broqvist | Ageo Meier de Andrade | Yunqi Shao | S. H. Kim | Kwang-Ryeol Lee
Source: ArXiv.org (preprint, 2025-06-24)
Field: Materials Science — Materials Chemistry

AI-assisted prediction of catalytically reactive hotspots in nanoalloys Nanoalloys offer a unique opportunity to tailor chemical properties through changes in composition, shape, and size. However, this flexibility introduces complexity that challenges both experimental and conventional theoretical methods. In this work, we present an AI-assisted framework for predicting reactive hotspots in nanoalloys. First, we use a Metropolis Monte Carlo method with a lattice-based machine learning potential, trained on 2NN-MEAM data, to rapidly identify thermodynamically stable nanoparticle structures, demonstrated for Pt-Ni homotops. This approach yields core-shell geometries with Ni-rich cores and Pt-enriched surfaces. To predict catalytic activity, we exploit the correlation between reactivity and d-band centers. Rather than relying on costly DFT calculations, we employ a multiscale method using SCC-DFTB and machine learning to efficiently and accurately map d-band centers across a wide range of nanoalloy sizes and compositions. The framework is validated on Pt-Ni nanoparticles of experimental relevance and is readily extendable to other nanoalloy systems. Flexibility (engineering) ; Exploit ; Monte Carlo method ; Range (aeronautics) ; Nanoparticle ; Relevance (law) Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Omar A. M. Abdelraouf
Source: ArXiv.org (preprint, 2025-06-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Broadband Tunable Deep-UV Emission from AI-Optimized Nonlinear Metasurface Architectures Metasurfaces represent a pivotal advancement in nonlinear optics, leveraging high-Q resonant cavities to enhance harmonic generation. Multi-layer metasurfaces (MLMs) further amplify this potential by intensifying light-matter interactions within individual meta-atoms at the nanoscale. However, maximizing nonlinear efficiency demands extreme field confinement through optimized designs of large geometric and material parameters, which exceed traditional simulation's computational ability. To overcome this, we introduce NanoPhotoNet-NL, an AI-driven design tool employing a hybrid deep neural network (DNN) that synergizes convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM) models. This framework accelerates nonlinear MLM design speed by four orders of magnitude while maintaining over 98.3% prediction accuracy relative to physical simulators. The optimized MLMs achieve quality factors exceeding 50, enabling broadband third-harmonic generation (THG) in the deep ultraviolet (DUV) from wavelengths 200 nm to 260 nm via parametric sweeps. Furthermore, NanoPhotoNet-NL facilitates dynamically tunable DUV nanolight sources with 20 nm spectral coverage in the UVC band using low-loss nonlinear phase change materials. This work marks a transformative leap in nonlinear metasurface engineering, unlocking high-performance, reconfigurable platforms for nonlinear and quantum optical nanodevices. Nonlinear system ; Broadband ; Parametric statistics ; Artificial neural network ; Nonlinear optics ; Field (mathematics) ; Convolutional neural network ; Harmonic Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Acoustic Wave Phenomena Research
Authors: Xue Liang | Liang Zhang | Zhuoyue Yang | Youhua Jiang | Chao Qiang Jiang | Haoyang Cui
Source: Engineering Applications of Artificial Intelligence (article, 2025-06-25)
Field: Chemistry — Analytical Chemistry

Hyperspectral imaging for rapid impurity detection in power system liquids Hyperspectral imaging ; Computer science ; Artificial intelligence ; Remote sensing ; Computer vision ; Geology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: K. Balaji Nanda Kumar Reddy | Damodhar Reddy R | K Karthik | Nagamuni Venkata Sai K | Sarveshwar Prasad
Source:  (article, 2025-05-16)
Field: Materials Science — Polymers and Plastics

Advanced Molecular Solar Thermal Energy Storage Systems with Convolutional Block Attention Modules Various sustainable energy options, including CSP and STE, are emerging. Effective and cost-effective integration of TES devices is crucial for the viability and economic competitiveness of CSP technology. Renewable energy sources must be used more to reduce our dependence on fossil fuels and the greenhouse gas effect. sun energy is promising among renewable sources since Earth receives a lot of sun irradiation. The recommended model includes preprocessing, battery state estimation, and model training. To measure performance, the dataset is separated into training and testing. We compare the model to different methods to determine its accuracy and reliability. The experimental results reveal that the suggested DL approach predicts better than the competition. System optimisation of battery state estimate ensures optimal energy management. The model's severity grading accuracy of 97.43% shows its durability and precision. Modern deep learning algorithms can increase CSP system reliability and efficiency, according to the results. By addressing solar energy's intermittency, the proposed method advances sustainable energy. Future work will focus on improving the model's prediction and real-time suitability. Thermal energy storage ; Computer science ; Block (permutation group theory) ; Thermal ; Energy storage ; Solar energy ; Electrical engineering ; Physics ; Engineering ; Meteorology ; Thermodynamics Transition Metal Oxide Nanomaterials ; Electrocatalysts for Energy Conversion
Authors: Kazuyoshi Yoshimi | Yuichi Motoyama | Tatsumi Aoyama | Mitsuaki Kawamura | Naoki Kawashima
Source: ArXiv.org (preprint, 2025-05-23)
Field: Materials Science — Materials Chemistry

Project For Advancement of Software Usability in Materials Science The Institute for Solid State Physics (ISSP) at The University of Tokyo has been carrying out a software development project named ``the Project for Advancement of Software Usability in Materials Science (PASUMS)". Since the launch of PASUMS, various open-source software programs have been developed/advanced, including ab initio calculations, effective model solvers, and software for machine learning. We also focus on activities that make the software easier to use, such as developing comprehensive computing tools that enable efficient use of supercomputers and interoperability between different software programs. We hope to contribute broadly to developing the computational materials science community through these activities. Usability ; Software development ; Interoperability ; Software ; Software construction ; Social software engineering ; Package development process ; Software walkthrough Machine Learning in Materials Science ; Semiconductor materials and devices ; Advanced Materials Characterization Techniques
Authors: Luis Itza Vazquez-Salazar (11167677) | Markus Meuwly (1433455)
Source: Figshare (article, 2025-08-04)
Field: Materials Science — Materials Chemistry

Augmenting ChemicalDatabases for Atomistic MachineLearning by Sampling Conformational Space Machine learning (ML) has become a standard tool for the exploration of the chemical space. Much of the performance of such models depends on the chosen database for a given task. Here, this aspect is investigated for “chemical tasks” including the prediction of hybridization, oxidation, substituent effects, and aromaticity, starting from an initial “restricted” database (iRD). Choosing molecules for augmenting this iRD, including increasing numbers of conformations generated at different temperatures, and retraining the models can improve predictions of the models on the selected “tasks”. Addition of a small percentage of conformations (1%) obtained at 300 K improves the performance in almost all cases. On the other hand, and in line with previous studies, redundancy and highly deformed structures in the augmentation set compromise prediction quality. Energy and bond distributions were evaluated by means of Kullback–Leibler (<i>D</i><sub>KL</sub>) and Jensen–Shannon (<i>D</i><sub>JS</sub>) divergence and Wasserstein distance (<i>W</i><sub>1</sub>). The findings of this work provide a baseline for the rational augmentation of chemical databases or the creation of synthetic databases. Chemical space ; Divergence (linguistics) ; Set (abstract data type) ; Redundancy (engineering) ; Training set ; Chemical database ; Sampling (signal processing) ; Context (archaeology) ; Retraining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Siyi Wang | Min Nian | Yu Ma | Xiaojia Chen | Xiaotong Ji | Changzhi Shi | Xing Chen | Mingliang Fang
Source: Environmental Science & Technology (article, 2025-07-16)
Field: Chemistry — Analytical Chemistry

Machine Learning-Based Screening of Cosmetic Ingredients Identifies Vat Blue 6 as a Thyroid Hormone Receptor β Disruptor Thyroid disorders are among the most prevalent endocrine conditions worldwide, exhibiting a rising incidence and disproportionately affecting women. In this study, we hypothesized that cosmetics may contain previously unidentified thyroid-disrupting chemicals. To evaluate this possibility, we compiled a comprehensive data set of cosmetic ingredients and developed a random forest regression-based machine learning model to predict their potential to disrupt thyroid hormone receptor β (TRβ), a critical regulator of thyroid function. From the top 40 compounds ranked by the model, 12 frequently used cosmetic ingredients were selected for experimental validation. Of these, six demonstrated measurable binding affinity toward TRβ. Notably, Vat Blue 6 (VB6), a colorant utilized in cosmetic formulations, exhibited structural characteristics potentially mimicking thyroid hormones and displayed potent TRβ binding with an affinity (<i>K</i><sub>d</sub>) as low as 0.7 μM. Subsequent <i>in vitro</i> assays and <i>in vivo</i> experiments in mice confirmed VB6's thyroid-disrupting effects, evidenced by dose-dependent reductions in serum thyroid hormone concentrations and morphological alterations of thyroid tissue. This study highlights the efficacy of machine learning approaches in rapidly screening large chemical inventories to identify potential thyroid disruptors and underscores the critical need for further toxicological assessment of cosmetic ingredients, particularly considering their frequent and prolonged exposure among female populations. Endocrine disruptor ; Thyroid ; Hormone receptor ; Hormone ; Medicine ; Chemistry ; Internal medicine ; Endocrine system Dye analysis and toxicity ; Consumer Attitudes and Food Labeling ; Biochemical Analysis and Sensing Techniques
Authors: Snehal S. Datwase | Charansing Kayte | Ratnadeep R. Deshmukh | Pravin V. Dhole | Akshay Kshirsagar
Source:  (article, 2025-03-14)
Field: Chemistry — Analytical Chemistry

Banana Quality analysis and classification using Spectral Signature Analysis and Machine Learning Ensuring the quality of bananas is crucial for both growers and end users along the whole supply chain. This study investigates the use of machine learning and spectral signature analysis to distinguish between bananas that have been artificially and naturally ripened. After processing, the spectral data from ASD FieldSpec4 was saved in CSV format. To categories the ripeness of bananas, three machine learning methods were used: Random Forest, Support Vector Machine (SVM), and Partial Least Squares Regression (PLSR). 20% was set aside for testing and the remaining 80% for training. The Random Forest method performed better than PLSR and SVM, with accuracies of 88.89% and 86%, respectively. The Random Forest approach attained 100% accuracy. The results show that the ripening process of calcium carbide has an impact on the amounts of moisture and nutrients, and that naturally and chemically ripened bananas have different spectral signatures. This method provides a non-invasive way to determine ripeness, increasing trust in the safety and quality of bananas. Random forest ; Ripeness ; Partial least squares regression ; Support vector machine ; Spectral signature ; Signature (topology) ; Pattern recognition (psychology) ; Quality (philosophy) Spectroscopy and Chemometric Analyses ; Banana Cultivation and Research ; Smart Agriculture and AI