jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ziauddin Ursani | Dmytro Antypov | Katie Atkinson | Matthew S. Dyer | Matthew J. Rosseinsky | Sven Schewe | Andrij Vasylenko
Source:  (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Protocol for generating new patterns using Monte Carlo ensemble of hierarchical models Hierarchical learning and Monte Carlo ensemble are recently introduced concepts in artificial intelligence. When joined together, they have shown great potential not only in just predicting outcomes but also in their extendibility to various AI related tasks in relation to physical sciences. In this paper, we develop a protocol for the generation of new patterns using hierarchical Monte Carlo ensemble learning with its application on a material science dataset related to the conductivity of materials. This activity led to the generation of new potential conductor compositions, furthering the process of discovery of new materials, which is an ultimate goal in material science. To facilitate the process, the dataset and the corresponding training model were redesigned. This modification widened the scope of the model, which can now cover the data-deficient benchmarks. To test its robustness, we also evaluated our method on 36 general machine learning datasets, including datasets with missing values, and compared the results with the well-known methods in the literature. Monte Carlo method ; Ensemble learning ; Protocol (science) ; Relation (database) ; Process (computing) ; Scope (computer science) Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Advanced Electron Microscopy Techniques and Applications
Authors: Beno Klopčič | Maja Lindič | Gregor Černivec
Source:  (article, 2025-05-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Modelling of Initial Magnetic Curves of Non-Oriented Laminated Steels Using Artificial Neural Networks The article discusses modelling of the initial static magnetic curve of non-oriented laminated steels using artificial neural networks (ANN) for usage in electrical machine calculations. ANN enables modelling of nonlinear characteristics, which is crucial for developing electrical machines that meet application requirements. The initial magnetic curve must satisfy certain boundary conditions to be suitable for utilization in electromagnetic calculations. It is important to consider the measurement uncertainty in calculations that can be in case of ANN achieved by introducing noise injection. In conclusion, the significance and effectiveness of ANN in improving calculations and simulations in the field of electrical engineering are emphasized. This opens new possibilities for developing and understanding the magnetic properties of materials, emphasizing the significant impact and effectiveness of ANN in improving calculations and simulations. Artificial neural network ; Materials science ; Computer science ; Mechanical engineering ; Artificial intelligence ; Engineering Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Magnetic Field Sensors Techniques
Authors: J.S. Lee | Yu Jia | Xiangzi Li | Dae-Jong Kim | Sungkwon Park
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Comprehensive Non-Destructive Assessment of Beef Quality: Focus on Textural Feature Analysis and Tenderness Using Ai and Hyperspectral Imaging Hyperspectral imaging ; Tenderness ; Feature (linguistics) ; Focus (optics) ; Quality (philosophy) ; Pattern recognition (psychology) ; Artificial intelligence ; Remote sensing ; Computer science ; Geology ; Chemistry ; Food science ; Optics ; Physics Spectroscopy and Chemometric Analyses
Authors: Sreeraj Rajan Warrier | Jayasri Dontabhaktuni
Source: ArXiv.org (preprint, 2025-07-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hybrid Quantum Generative Adversarial Networks To Inverse Design Metasurfaces For Incident Angle-Independent Unidirectional Transmission Optimization of metasurface designs for specific functionality is a challenging problem due to the intricate relation between structural features and electromagnetic responses. Recently, many researchers resolved to inverse design of metasurfaces for efficient design parameters based on methods such as parameter optimization, evolutionary optimization and machine learning. In this paper a hybrid quantum machine learning method which uses quantum encoders to enhance the performance of a classical GAN is applied to implement inverse design of a metasurface. Aiming towards angle-independent unidirectional transmission, this approach combines a Quantum Generative Adversarial Network (QGAN) with a Variational Autoencoder (VAE) to optimize metasurface designs. Incident-angle independent uni-directional transmission has potential applications in efficient solar cells, thermal cooling, non-reciprocal devices, etc. However, it is very challenging to achieve such a metasurface design via forward methods and hence very few studies exist till now in this direction. The methodology employed in this work reduces the data requirement for inverse design by 30% compared to conventional GAN-based methods. More importantly, the developed metasurface designs show high fidelity of 95% with regard to the targeted far-field radiation patterns. We also provide a material look-up table for feasible substitutes of the obtained material design with real materials and yet maintaining performance accuracy. Further, we embed the inverse-designed metasurfaces into Perovskite solar cell layers to demonstrate the improvement in its performance. We observe that the conversion efficiency of the example perovskite solar cell improves by 95% and remains independent of incident angle in the range -60$^\circ$ to 60$^\circ$ within the desired frequency range. Transmission (telecommunications) ; Inverse ; Quantum ; Encoder ; Inverse problem ; Range (aeronautics) ; Optimization problem ; Optimal design Metamaterials and Metasurfaces Applications ; Advanced Optical Imaging Technologies ; Neural Networks and Reservoir Computing
Authors: Yanlong Wen | Hongjian Zhang | Haiyan Xiao | Qing Zhu | Liguo Zhang | Hong Li
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Comparative Analysis of Machine Learning Methods for Correcting Uranium Loading Measurement Errors in Pebble-Bed HTGR Spherical Fuel Elements Support vector machine ; Nuclear fuel ; Uranium ; Multilayer perceptron ; Nuclear reactor ; Nuclear material ; Enriched uranium ; Kernel (algebra) ; Artificial neural network Nuclear Materials and Properties ; Nuclear reactor physics and engineering
Authors: Pablo Rodríguez-Belenguer | Alexander Amberg | Frank Bringezu | Markus Frericks | Jennifer Hemmerich | Peter Monecke | Nils Oberhauser | Panuwat Trairatphisan | Manuel Pastor
Source: Journal of Chemical Information and Modeling (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

A Simple Framework for Collaborative Development of Predictive Models Trained on Proprietary Data We present a simple methodology that allows the building and sharing of predictive models without compromising the confidentiality of the structures of the training series. Multiple shared models can be used to obtain ensemble models, providing better coverage of the chemical space and better predictions than the original one. This approach is demonstrated in a collaborative exercise where four pharmaceutical and chemical companies developed predictive models for the AMES mutagenicity end point and shared them to build ensemble models using logical and machine learning algorithms. The results were systematically analyzed and compared, obtaining clear benefits in predictive quality. The method has the advantage of being very simple to execute, using only open-source software, and the possibility to audit the whole process and interchange files to guarantee that no confidential information is exported from the company facilities. Simple (philosophy) ; Confidentiality ; Process (computing) ; Audit ; Point (geometry) ; Ensemble forecasting ; Training set ; Ensemble learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Naoki Matsumura (11860397) | Yuta Yoshimoto (9967682) | Tamio Yamazaki (21014263) | Tomohito Amano (21014266) | Tomoyuki Noda (1808167) | Naoki Ebata (21014269) | Takatoshi Kasano (21014272) | Yasufumi Sakai (21014275)
Source: Figshare (article, 2025-04-08)
Field: Materials Science — Materials Chemistry

Generator of NeuralNetwork Potential for MolecularDynamics: Constructing Robust and Accurate Potentials with ActiveLearning for Nanosecond-Scale Simulations Neural network potentials (NNPs) enable large-scale molecular dynamics (MD) simulations of systems containing >10,000 atoms with the accuracy comparable to <i>ab initio</i> methods and play a crucial role in material studies. Although NNPs are valuable for short-duration MD simulations, maintaining the stability of long-duration MD simulations remains challenging due to the uncharted regions of the potential energy surface (PES). Currently, there is no effective methodology to address this issue. To overcome this challenge, we developed an automatic generator of robust and accurate NNPs based on an active learning (AL) framework. This generator provides a fully integrated solution encompassing initial data set creation, NNP training, evaluation, sampling of additional structures, screening, and labeling. Crucially, our approach uses a sampling strategy that focuses on generating unstable structures with short interatomic distances, combined with a screening strategy that efficiently samples these configurations based on interatomic distances and structural features. This approach greatly enhances the MD simulation stability, enabling nanosecond-scale simulations. We evaluated the performance of our NNP generator in terms of its MD simulation stability and physical properties by applying it to liquid propylene glycol (PG) and polyethylene glycol (PEG). The generated NNPs enable stable MD simulations of systems with >10,000 atoms for 20 ns. The predicted physical properties, such as the density and self-diffusion coefficient, show excellent agreement with the experimental values. This work represents a remarkable advance in the generation of robust and accurate NNPs for organic materials, paving the way for long-duration MD simulations of complex systems. Generator (circuit theory) ; Molecular dynamics ; Interatomic potential ; Artificial neural network ; Stability (learning theory) ; Sampling (signal processing) ; Work (physics) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Crystallography and molecular interactions
Authors: Wei Li | Zhiliang Cai | Li Song | Weixiong Wu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Multitask Transfer Learning Framework for Predicting Hydrogen Storage Performance of Metal/Covalent-Organic Frameworks Across Diverse Operating Conditions Metal-organic framework ; Transfer of learning ; Covalent bond ; Hydrogen storage ; Computer science ; Hydrogen ; Chemistry ; Artificial intelligence ; Organic chemistry Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science
Authors: Adam Wold | You W. | Rech J.J. | Yunxin Wang | Ou J. | Huan Yang
Source: UNC Libraries (article, 2025-12-03)
Field: Materials Science — Materials Chemistry

OSC-Net: a multi-fidelity machine learning model for organic solar cells Organic solar cells (OSCs) have emerged as a promising renewable energy technology, offering advantages such as lightweight design, semitransparency, flexibility, and cost-effectiveness. Power conversion efficiency (PCE) is a key device performance parameter for OSCs, defined as the ratio of the electrical power output generated by the device to the incident solar power input. Despite significant advances, the development of high-performance OSCs remains a labor-intensive process, heavily dependent on expert experience, involving extensive synthesis, characterization, and iterative optimization. Data-driven methods offer a promising alternative for accelerating material discovery, but their effectiveness is often limited by the scarcity of high-quality experimental data. To overcome this challenge, we propose OSC-Net, a multi-fidelity machine learning framework that integrates a large volume of computational data with a smaller set of high-accuracy experimental measurements. This approach enables accurate prediction of key device performance parameters, including PCE, while simultaneously tackling the challenges associated with experimental data scarcity and uncertainty quantification, enabling efficient screening of OSC materials. Importantly, the predictive capability of OSC-Net was verified against published experimental data, confirming its accuracy and reliability. By leveraging both data sources, OSC-Net achieves superior predictive performance compared to conventional single-fidelity models. Furthermore, the uncertainty quantification captures variability in the model, enhancing the reliability of predictions. Finally, OSC-Net was employed for large-scale high-throughput screening, successfully identifying promising candidates with high predicted PCEs that were validated against literature-reported experimental data. Thus, OSC-Net presents a feasible approach for rapid and accurate inference of device performance parameters with limited experimental datasets, enabling efficient OSC material discovery. Reliability (semiconductor) ; Key (lock) ; Renewable energy ; Scarcity ; Inference ; Set (abstract data type) ; Photovoltaic system ; Experimental data ; Power (physics) Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Advanced Memory and Neural Computing
Authors: Gaju S. Chavan
Source: International Journal for Research in Applied Science and Engineering Technology (article, 2025-06-07)
Field: Chemistry — Analytical Chemistry

Optimized the Spectral Reflectance Database of Herbal Plant Sample through ASD FieldSpc4 Spectroscopy Unique spectral signatures are important for reflecting the inherent characteristics of materials. But different factors can alter the spectral signature, thus affecting measurement quality. Environmental and experimental conditions, atmosphericproperties, timing, orientation, height of measurement, FOV, calibration of spectral data and spectral averaging are some of the main factors that affect measurements. This paper presents a database collection method of performing spectral reflectance. The procedure involves calibration protocols, data acquisition routines, preprocessing methods, and storage in a structured database for retrieval and analysis efficiency. The spectral data are classified into plant species, environmental conditions, and measurement parameters for increased usability in machine learning and remote sensing studies. The derived database is a rich source of information for researchers in vegetation classification, stress identification, and hyperspectral modeling. Measuring the spectral signature using ASD fieldspec4 spectroradiometer hyperspectral non-imaging device. The research focus on the importance of optimized spectral data collection and management to aid new researcher’s decision making in environmental monitoring. Sample (material) ; Near infrared reflectance spectroscopy ; Reflectivity ; Spectroscopy ; Diffuse reflectance infrared fourier transform ; Remote sensing ; Materials science ; Near-infrared spectroscopy ; Environmental science ; Computer science ; Optics ; Chemistry ; Geography ; Chromatography ; Physics ; Organic chemistry ; Astronomy Spectroscopy and Chemometric Analyses
Authors: Matteo Zorzetto | Riccardo Torchio | Francesco Lucchini | Paolo Di Barba | Maria Evelina Mognaschi | Michele Forzan | Fabrizio Dughiero
Source: IEEE Transactions on Magnetics (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning-based Reduced Order Modeling of Nonlinear and Multiphysics Magnetic Devices Induction heating processes involve complex multiphysical interactions between electromagnetic and thermal models, often characterized by nonlinear materials. The finite element method is a common approach to tackling such problems, but its computational complexity may become a burden for optimization loops or real-time monitoring. This work proposes a surrogate modeling approach that combines proper orthogonal decomposition and Gaussian process regression, suited for nonlinear and temperature-dependent magnetic materials. The approach is applied to reduce the complexity of the electromagnetic model of the testing electromagnetic analysis method (TEAM) problem 36, consisting of a copper coil heating through induction a steel billet. Results show how the non-intrusive machine learning approach can accurately reconstruct the field distribution, offering a viable first step towards fast multiphysical simulations involving nonlinear magnetic materials. Multiphysics ; Nonlinear system ; Electromagnetic coil ; Finite element method ; Model order reduction ; Magnetic field ; Electromagnetic field ; Computational electromagnetics ; Electromagnetics Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Neural Networks and Applications
Authors: Daisuke Kimura | Naoko Tajima | Toshiya Okazaki | Shun Muroga
Source: ArXiv.org (preprint, 2025-02-11)
Field: Materials Science — Materials Chemistry

Explainable Multimodal Machine Learning for Revealing Structure-Property Relationships in Carbon Nanotube Fibers In this study, we propose Explainable Multimodal Machine Learning (EMML), which integrates the analysis of diverse data types (multimodal data) using factor analysis for feature extraction with Explainable AI (XAI), for carbon nanotube (CNT) fibers prepared from aqueous dispersions. This method is a powerful approach to elucidate the mechanisms governing material properties, where multi-stage fabrication conditions and multiscale structures have complex influences. Thus, in our case, this approach helps us understand how different processing steps and structures at various scales impact the final properties of CNT fibers. The analysis targeted structures ranging from the nanoscale to the macroscale, including aggregation size distributions of CNT dispersions and the effective length of CNTs. Furthermore, because some types of data were difficult to interpret using standard methods, challenging-to-interpret distribution data were analyzed using Negative Matrix Factorization (NMF) for extracting key features that determine the outcome. Contribution analysis with SHapley Additive exPlanations (SHAP) demonstrated that small, uniformly distributed aggregates are crucial for improving fracture strength, while CNTs with long effective lengths are significant factors for enhancing electrical conductivity. The analysis also identified thresholds and trends for these key factors to assist in defining the conditions needed to optimize CNT fiber properties. EMML is not limited to CNT fibers but can be applied to the design of other materials derived from nanomaterials, making it a useful tool for developing a wide range of advanced materials. This approach provides a foundation for advancing data-driven materials research. Carbon nanotube ; Property (philosophy) ; Materials science ; Nanotechnology ; Composite material ; Philosophy ; Epistemology Machine Learning in Materials Science
Authors: Hairui Zhou | Hongyu Liu | Zuyun Chen | Zhengxin Chen | Jun Li | Bowen Liu | Ling Xiao | Xiaolin Liu | Ling Yang | Minliang Lai | Zefeng Chen | Jia Lin
Source: Materials Today Physics (article, 2025-10-18)
Field: Materials Science — Materials Chemistry

Few-shot learning breaks the data bottlenecks in perovskite materials design Perovskite (structure) ; Band gap ; Valence (chemistry) ; Bottleneck ; Quantum ; Quantum dot ; Data-driven ; Scarcity Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Perovskite Materials and Applications
Authors: Zhenhong Qi | Meiqi Dai | Yu‐Juan Ma | Dongpeng Yan
Source: Angewandte Chemie (article, 2025-11-02)
Field: Materials Science — Materials Chemistry

Artificial Decision Tree Guided Screening of Ultralong Room‐Temperature Phosphorescent Cocrystals with Locally Excited States Abstract High‐throughput screening of ultralong room‐temperature phosphorescence (RTP) systems is essential for advancing photofunctional materials. Among molecular solids, charge‐transfer (CT) cocrystals offer great potential due to the wide range of electron donors and acceptors with tunable energy levels. However, realizing RTP in CT systems remains difficult, as intricate orbital interactions in donor–acceptor pairs often lead to spin‐forbidden singlet‐to‐triplet transitions. To overcome this challenge, we developed an artificial‐decision‐tree method guided by theoretical calculations and introduced an energy‐based descriptor ( E ) to streamline the design of RTP‐active cocrystals. This descriptor enables CT‐dominated singlet emission alongside locally excited triplet emission, effectively promoting intersystem crossing. Applying this strategy, we predicted and experimentally validated new cocrystals (TP&amp;1,2‐TFP and TP&amp;1,4‐TFP), which achieved RTP lifetimes up to 1.223 s, the longest reported among current CT cocrystals. We further demonstrated the versatility of this strategy by identifying additional RTP‐active systems (TP&amp;1,2‐TCP and TP&amp;1,4‐TCP). Owing to their tunable emission and time‐resolved RTP, these materials were successfully employed in digital and visual information encryption and time‐gated optical logic gate applications. Therefore, this work presents a robust framework that combines machine learning‐driven prediction with experimental validation, offering a systematic and efficient alternative to traditional trial‐and‐error methods in the discovery of luminescent materials. Phosphorescence ; Intersystem crossing ; Excited state ; Encryption ; Cocrystal ; Singlet fission ; Luminescence ; Singlet state Luminescence and Fluorescent Materials ; Luminescence Properties of Advanced Materials ; Crystallography and molecular interactions
Authors: T.S. Sachit | R. Suresh | T. S. Nagaraj
Source: Journal of The Institution of Engineers (India) Series D (article, 2025-02-10)
Field: Materials Science — Materials Chemistry

Systematic Wear Rate Analysis of Ti–6Al–4 V Based Hybrid Composites Using Taguchi Optimizer and Machine Learning Classifiers Taguchi methods ; Composite material ; Materials science ; Composite number Titanium Alloys Microstructure and Properties ; Aluminum Alloys Composites Properties ; Advanced materials and composites
Authors: Lili Fu | Zhiqiang Zhang | Huimin Xu | Bo Song | C. H. Zhang | Qi Zhang | Ruilin Zheng
Source: Ceramics International (article, 2025-11-13)
Field: Materials Science — Ceramics and Composites

Machine learning-assisted composition design of fluorine-containing glasses with tailored properties Interpretability ; Component (thermodynamics) ; Random forest ; Extrapolation ; Regression ; Linear regression ; Predictive modelling ; Decision tree ; Regression analysis Glass properties and applications ; Phase-change materials and chalcogenides ; Material Dynamics and Properties