jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Md. Masudul Islam | Galib Muhammad Shahriar Himel | Md. Golam Moazzam | Mohammad Shorif Uddin
Source: The Journal of Engineering (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Rice Variety Classification Using Next Generation Convolutional Networks ABSTRACT Rice, a staple food for over half of the global population, exhibits remarkable diversity in grain types. However, this diversity poses significant challenges in accurate identification, often resulting in misclassification and even intentional adulteration. These issues compromise product quality and erode trust within the agricultural supply chain. Traditional identification methods, which rely on manual inspection and chemical analysis, are often imprecise, labour‐intensive, and impractical, especially when distinguishing grains based on external features such as size, shape, and texture. To address these limitations, we propose an advanced framework that leverages cutting‐edge machine‐learning techniques for rice variety classification. Our approach employs a convolutional neural network model enhanced with Transformer‐based design principles, achieving exceptional performance. The model attains an accuracy of 99.60% on one dataset and 100% on another under similar experimental conditions. Its robustness and simplicity make it a highly effective solution for practical applications. The proposed methodology holds significant promise for broader applications in agriculture and food safety, paving the way for a more transparent and reliable agricultural ecosystem that fosters trust and innovation. Variety (cybernetics) ; Computer science ; Convolutional neural network ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; GABA and Rice Research
Authors: Yue Liu | Dahui Liu | Zhengwei Yang | Xianyuan Ge | Wenxuan Yao | Jie Wu | Maxim Avdeev | Siqi Shi
Source: Energy storage materials (article, 2025-06-09)
Field: Materials Science — Materials Chemistry

A knowledge acquisition automatizing framework from literature exemplified by Na+ activation energy prediction of NASICON solid-state electrolyte Fast ion conductor ; Materials science ; Electrolyte ; Solid-state ; Energy (signal processing) ; Nanotechnology ; Chemical engineering ; Engineering physics ; Physical chemistry ; Electrode ; Engineering ; Chemistry ; Physics Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Krittapong Deshsorn | Weekit Sirisaksoontorn | Wisit Hirunpinyopas | Pawin Iamprasertkun
Source: FlatChem (article, 2025-01-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Exploring how base model combination affects the results of a “stacking” ensemble machine learning model: An applied study on optimization of heteroatom doped carbon data Heteroatom ; Stacking ; Materials science ; Base (topology) ; Doping ; Carbon fibers ; Optoelectronics ; Organic chemistry ; Composite material Supercapacitor Materials and Fabrication ; Advanced Sensor and Energy Harvesting Materials ; Gas Sensing Nanomaterials and Sensors
Authors: Zhi Cao | Yuan Liu | Zhaoxu Wang | Qingyuan Yang | Baishu Zheng
Source: International Journal of Hydrogen Energy (article, 2025-05-09)
Field: Chemistry — Inorganic Chemistry

Prediction of hydrogen storage in IL/COF composites based on high-throughput computational screening and machine learning Hydrogen storage ; Throughput ; Materials science ; Composite material ; Hydrogen ; Computer science ; High-throughput screening ; Chemistry ; Operating system Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Radioactive element chemistry and processing
Authors: Chipeng Guo | Xiangyu Tang | Yue Chen | Claire Gao | Qinglin Shan | Heyi Wei | Xun Liu | Chuncheng Lu | Meixia Fu | E. H. Wang | Xinhong Liu | Xinmei Hou | Yanglong Hou
Source: International Journal of Minerals Metallurgy and Materials (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

From microstructure to performance optimization: Innovative applications of computer vision in materials science Microstructure ; Reliability (semiconductor) ; Characterization (materials science) ; Key (lock) ; Field (mathematics) ; Crystal (programming language) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Neural Network Applications
Authors: Humayun Zubair Khan | Junaid Zafar | Abdul Jabbar | Jalil ur Rehman Kazim | Masood Ur Rehman | Muhammad Ali Imran | Qammer H. Abbasi
Source: Next research. (article, 2025-05-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advancements in metasurfaces for polarization control: A comprehensive survey Metasurfaces, composed of sub-wavelength-scale engineered structures, have revolutionized the manipulation of light, providing unprecedented control over optical properties such as polarization, phase, and amplitude. This survey paper explores recent advancements in metasurfaces, focusing on their application for polarization control. We examine the design principles underlying these metasurfaces, including phase manipulation, unit cell engineering, material selection, and spatial arrangement. The role of machine learning techniques for the inverse design of metasurfaces is also explored, highlighting their potential to optimize designs for specific polarization functionalities. The paper delves into vector beams with spatially inhomogeneous polarization, discussing their significance in high-resolution microscopy, optical communication, quantum information processing, and other emerging applications. The latest trends in using machine and deep learning for metasurface design are addressed, particularly in the context of overcoming traditional challenges such as tunability, bandwidth limitations, and fabrication constraints. We also explore advanced techniques like geometric and Pancharatnam-Berry phase manipulation, nonlinear metasurfaces, and space-variant polarization control. These innovations enable dynamic, reconfigurable, and multi-functional metasurfaces capable of adapting to a wide range of optical functions. The paper concludes by identifying future research directions, emphasizing the integration of adaptive metasurfaces with real-time control mechanisms and the potential for breakthroughs in quantum optics, imaging, and sensing technologies. Through these advancements, metasurfaces are expected to drive significant progress in the development of compact, efficient, and versatile optical devices. Polarization (electrochemistry) ; Optics ; Computer science ; Physics ; Materials science ; Chemistry Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Xinzhe Zhang | Rentao Wang | Chuan Zhi Zang | Kai Song | Xiaolu Wang | Guoju Li
Source: Composites Part B Engineering (article, 2025-08-25)
Field: Materials Science — Materials Chemistry

Machine learning-based multi-objective optimization of ceramic composite armor plates via the NSGA-II genetic algorithm Armour ; Materials science ; Composite number ; Composite material ; Ceramic ; Genetic algorithm ; Multi-objective optimization ; Ceramic composite ; Algorithm ; Computer science ; Machine learning High-Velocity Impact and Material Behavior ; Mechanical Behavior of Composites ; Additive Manufacturing and 3D Printing Technologies
Authors: S.N. Nazrin | Camellia Doroody | Liyana Adilla Burhanuddin | N. S. Nirmala Jothi | N.B. Ibrahim | Halimah Badioze Zaman | Mudassir Hussain Tahir | Manzoore Elahi M. Soudagar | Sivalingam Ramesh | Sagar Shelare | Shubham Sharma
Source: Ceramics International (article, 2025-04-26)
Field: Chemistry — Inorganic Chemistry

Machine learning-driven prediction of optical and physical properties in lanthanum and gold-doped zinc borotellurite glasses for optoelectronic applications Materials science ; Doping ; Lanthanum ; Zinc ; Optoelectronics ; Nanotechnology ; Engineering physics ; Metallurgy ; Inorganic chemistry Pigment Synthesis and Properties ; Phase-change materials and chalcogenides ; Glass properties and applications
Authors: Nuttapon Yodsin | Pimjai Pimbaotham | Thana Maihom | Rathawat Daengngern | Masanori Tachikawa | Siriporn Jungsuttiwong | Yuwanda Injongkol
Source: Journal of Catalysis (article, 2025-06-09)
Field: Chemistry — Inorganic Chemistry

Unlocking efficient CO2–to–methanol conversion on frustrated Lewis pair-functionalized UiO–67: A synergistic approach using DFT and SISSO Chemistry ; Methanol ; Catalysis ; Frustrated Lewis pair ; Lewis acids and bases ; Combinatorial chemistry ; Computational chemistry ; Nanotechnology ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Organoboron and organosilicon chemistry
Authors: Akash Ball | Gianmarco Terrones | Shuwen Yue | Heather J. Kulik
Source: ACS Applied Materials & Interfaces (article, 2025-06-05)
Field: Chemistry — Inorganic Chemistry

Data-Driven Discovery of Water-Stable Metal–Organic Frameworks with High Water Uptake Capacity Metal-organic frameworks (MOFs) are promising candidate materials for applications that would benefit from precise chemical patterning, such as desalination, but many MOFs suffer from poor stability in water. In addition to water stability, high water uptake capacity in ambient conditions is expected to be necessary for water-related practical applications of MOFs, motivating large-scale search that can only be achieved computationally. Here, we take a combined machine learning and high-throughput screening approach to identify water-stable MOFs with high water uptake capacities. Starting from a subset of previously curated MOFs with experimentally known exceptionally high stability in water, we explore the effect of linker functionalization with 12 diverse hydrophilic functional groups expected to further tune water uptake. For these 736 MOFs, we use grand canonical Monte Carlo (GCMC) simulations to compute their water uptake capacity. We observe strong positive correlations between MOF pore features (e.g., the largest cavity diameter and volumetric pore volume) and water uptake capacity, although we notice breakdowns of such correlations in MOFs with extremely hydrophobic linkers that repel water molecules despite having large pores. Finally, we develop machine learning models to screen new MOFs simultaneously for water stability and water uptake capacity. From a pool of hypothetical and experimental MOFs, we identify 74 promising materials within the domain of applicability of the machine learning models that are predicted to both be water-stable and have high water uptake. Materials science ; Metal-organic framework ; Nanotechnology ; Adsorption ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Enhanced Oil Recovery Techniques ; Machine Learning in Materials Science
Authors: Peiying Wu | Rui Zhang | Céline Porte | Fabian Kießling | Twan Lammers | Sima Rezvantalab | Sara Mihandoost | Roger M. Pallares
Source: Nanoscale Advances (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning to predict gold nanostar optical properties Gold nanostars (AuNS) are nanoparticles with spiky structures and morphology-dependent optical features. These include strong extinction coefficients in the visible and near-infrared regions of the spectrum, which are commonly exploited for biomedical imaging and therapy. AuNS can be obtained <i>via</i> seedless protocols with Good's buffers, which are beneficial because of their simplicity and the use of biocompatible reagents. However, AuNS growth and optical properties are affected by various experimental factors during their seedless synthesis, which affects their performance in diagnosis and therapy. In this study, we develop a workflow based on machine learning models to predict AuNS optical properties. This approach includes data collection, feature selection, data generation, and model selection, resulting in predictions of the first and second localized surface plasmon resonance positions within 9 and 15% of their true values (root-mean-squared percentage error), respectively. Our results highlight the benefits of using machine learning models to infer the optical properties of AuNS from their synthesis conditions, potentially improving nanoparticle design and production for better disease diagnosis and therapy. Computer science ; Artificial intelligence ; Nanotechnology ; Materials science ; Machine learning Gold and Silver Nanoparticles Synthesis and Applications ; Nanocluster Synthesis and Applications ; Machine Learning in Materials Science
Authors: Zhipeng Huo | Qin Wu | Hansheng Li | Yaoyuan Zhang | Kangcheng Chen | Daxin Shi
Source: Journal of Alloys and Compounds (article, 2025-07-20)
Field: Materials Science — Materials Chemistry

Recent advances in modification methods for LiBH4-based hydrogen storage materials Hydrogen storage ; Materials science ; Chemical engineering ; Nanotechnology ; Metallurgy ; Engineering ; Alloy Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Spacecraft and Cryogenic Technologies
Authors: Katarzyna Peta
Source: Materials (article, 2025-01-05)
Field: Materials Science — Surfaces, Coatings and Films

Surface Wettability Modeling and Predicting via Artificial Neural Networks Surface wettability, defined by the contact angle, describes the ability of a liquid to spread over, absorb or adhere to a solid surface. Surface wetting analysis is important in many applications, such as lubrication, heat transfer, painting and wherever liquids interact with solid surfaces. The behavior of liquids on surfaces depends mainly on the texture and chemical properties of the surface. Therefore, these studies show the possibility of modeling surface wettability by adjusting the parameters of the surface texturing process. The prediction of the contact angle describing the wettability of the surface was performed using artificial neural networks. In order to select the most effective prediction model, the activation functions of neurons, the number of hidden layers and the network training algorithms were changed. The neural network model presented in these studies is capable of predicting the contact angle with an efficiency defined by the coefficient of determination R<sup>2</sup> between real and predicted contact angles of over 0.9. Wetting ; Contact angle ; Materials science ; Artificial neural network ; Surface (topology) ; Lubrication ; Biological system ; Process (computing) ; Texture (cosmology) ; Artificial intelligence ; Computer science ; Composite material ; Image (mathematics) ; Geometry ; Mathematics Surface Modification and Superhydrophobicity ; Adhesion, Friction, and Surface Interactions ; Surface Roughness and Optical Measurements
Authors: Filip Musiałek | J. Wojtas
Source: Engineering Applications of Artificial Intelligence (article, 2025-05-09)
Field: Chemistry — Spectroscopy

Calibration-free, machine learning based wavelength modulation spectroscopy sensor for fast gas concentration measurements Computer science ; Calibration ; Wavelength ; Modulation (music) ; Spectroscopy ; Optics ; Acoustics ; Physics Spectroscopy and Laser Applications ; Advanced Chemical Sensor Technologies ; Atmospheric Ozone and Climate
Authors: Anesu Nyabadza | Lola Azoulay-Younes | Mercedes Vázquez | Dermot Brabazon
Source: Measurement (article, 2025-03-05)
Field: Chemistry — Electrochemistry

Machine learning for quality control of Tin-Copper electrodes • Machine learning ensemble model classifies 100 electrodes in under 100 ms. • Random forest and support vector machines reach 92% accuracy in manufacturing quality control. • Overfitting reduced with Synthetic Minority Over-sampling Technique. Screen printing is the most common technique for manufacturing printed electrodes, yet machine learning applications in its quality control are underexplored. Herein, SnO 2 -CuO microparticle-nanoparticle-based electrodes were printed on paper substrates and categorised as bad, medium, or good. The optimal chemical composition of the formulated inks and the maximum operating temperatures of the electrodes (up to 150 °C) were investigated. The inks were formulated using various ratios of SnO 2 powder (particle size 325 µm), glycerine and Polyvinyl alcohol (PVA) glue. The highest conductivity recorded was 80 S.m −1 achieved for an ink composed of 1 g SnO 2 powder, 3 g glycerine, 0.03 PVA glue, and heat treated at 150 ⁰C for 30 min. Random forest (RF), support vector machine (SVM) and Feedforward neural networks (FNN) models achieved approximately 80 % accuracy and recall. Synthetic Minority Over-sampling Technique with Edited Nearest Neighbors and hyperparameter tunning methods were used to eliminate model overfitting by balancing the dataset. An ensemble model that can classify about 100 electrodes in under 100 ms and can correctly classify bad electrodes with an average accuracy of 92 % was developed. This ensemble model was demonstrated as a feasible solution for real world fast-paced manufacturing environments. Tin ; Copper ; Electrode ; Quality (philosophy) ; Control (management) ; Materials science ; Computer science ; Metallurgy ; Artificial intelligence ; Chemistry ; Physics Electrochemical Analysis and Applications ; Advanced Memory and Neural Computing ; Gas Sensing Nanomaterials and Sensors
Authors: Chaoqiang Feng | Yaolong Zhang | Bin Jiang
Source: Journal of Chemical Theory and Computation (article, 2025-01-03)
Field: Materials Science — Materials Chemistry

Efficient Sampling for Machine Learning Electron Density and Its Response in Real Space Electron density is a fundamental quantity that can in principle determine all ground state electronic properties of a given system. Although machine learning (ML) models for electron density based on either an atom-centered basis or a real-space grid have been proposed, the demand for a number of high-order basis functions or grid points is enormous. In this work, we propose an efficient grid-point sampling strategy that combines targeted sampling favoring a large density and a screening of grid points associated with linearly independent atomic features. This new sampling strategy is integrated with a field-induced recursively embedded atom neural network model to develop a real-space grid-based ML model for the electron density and its response to an electric field. This approach is applied to a QM9 molecular data set, a H<sub>2</sub>O/Pt(111) interfacial system, an Au(100) electrode, and an Au nanoparticle under an electric field. The number of training points is found to be much smaller than previous models, while yielding comparably accurate predictions for the electron density of the entire grid. The resultant machine-learned electron density model enables us to properly partition partial charge onto each atom and analyze the charge variation upon proton transfer in the H<sub>2</sub>O/Pt(111) system. The machine-learning electronic response model allows us to predict charge transfer and the electrostatic potential change induced by an electric field applied to an Au(100) electrode or an Au nanoparticle. Grid ; Atom (system on chip) ; Electron density ; Computer science ; Electric field ; Electron transfer ; Electron ; Statistical physics ; Computational physics ; Physics ; Chemistry ; Mathematics ; Quantum mechanics ; Physical chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Kongping Wu | Guoqing Chang | Jiandong Ye | Gang Zhang
Source: Journal of Applied Physics (article, 2025-03-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Increased thermal conductivity of β-Ga2O3 using Al substitution: Full spectrum phonon engineering Improving the thermal conductivity of β-Ga2O3 is critical for optimizing its performance in high-power electronic devices, as effective thermal management significantly influences their output power and reliability. In this work, the thermal conductivities of β-Ga2O3 and (AlxGa1−x)2O3 alloys along the (2¯ 0 1) direction were first computed using a non-equilibrium molecular dynamics method based on the deep learning potential. Our results indicate that the calculated thermal conductivity of β-Ga2O3 is 16.6 W m−1K−1 along the (2¯ 0 1) direction, which is in excellent agreement with experimental measurements. In our findings, an Al to Ga ratio of 1:1 leads to the thermal conductivity of the (AlxGa1−x)2O3 alloy being more than twice that of β-Ga2O3, regardless of the Al substitution sites. The (Al0.5Ga0.5)2O3 alloy exhibits enhanced thermal conductivity due to the improved transport properties of optical phonon modes, including the increased group velocities, the enhanced participation, and the induced new vibrational modes at higher frequencies. This research provides theoretical predictions regarding the optimal Al to Ga ratio to enhance the thermal conductivity of (AlxGa1−x)2O3 alloys, offering crucial insights for the design and thermal management of β-Ga2O3 power devices. Thermal conductivity ; Phonon ; Substitution (logic) ; Materials science ; Condensed matter physics ; Physics ; Composite material ; Computer science Ga2O3 and related materials ; Advanced Photocatalysis Techniques ; Thermal Expansion and Ionic Conductivity
Authors: Valerio Briganti | Alessandro Lunghi
Source: npj Computational Materials (article, 2025-03-06)
Field: Materials Science — Materials Chemistry

A machine-learning framework for accelerating spin-lattice relaxation simulations Molecular and lattice vibrations are able to couple to the spin of electrons and lead to their relaxation and decoherence. Ab initio simulations have played a fundamental role in shaping our understanding of this process but further progress is hindered by their high computational cost. Here we present an accelerated computational framework based on machine-learning models for the prediction of molecular vibrations and spin-phonon coupling coefficients. We apply this method to three open-shell coordination compounds exhibiting long relaxation times and show that this approach achieves semi-to-full quantitative agreement with ab initio methods reducing the computational cost by about 80%. Moreover, we show that this framework naturally extends to molecular dynamics simulations, paving the way to the study of spin relaxation in condensed matter beyond simple equilibrium harmonic thermal baths. Lattice (music) ; Condensed matter physics ; Relaxation (psychology) ; Computer science ; Statistical physics ; Physics ; Materials science ; Psychology ; Neuroscience Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Advanced Electron Microscopy Techniques and Applications