jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dmytro Tymoshchuk | Oleh Yasniy | Iryna Didych | Pavlo Orestovych Maruschak | Nadiia Lutsyk
Source: Sensors (article, 2025-12-24)
Field: Materials Science — Materials Chemistry

Recurrent Neural Networks with Integrated Gradients Explanation for Predicting the Hysteresis Behavior of Shape Memory Alloys The study presents an approach to predicting the hysteresis behavior of shape memory alloys (SMAs) using recurrent neural networks, including SimpleRNN, LSTM, and GRU architectures. The experimental dataset was constructed from 100 to 250 loading-unloading cycles collected at seven loading frequencies (0.1, 0.3, 0.5, 1, 3, 5, and 10 Hz). The input features included the applied stress σ (MPa), the cycle number <i>N</i> (the Cycle parameter), and the indicator of the loading-unloading stage (UpDown). The output variable was the material strain ε (%). Data for training, validation, and testing were split according to the group-based principle using the Cycle parameter. Eighty percent of cycles were used for model training, while the remaining 20% were reserved for independent assessment of generalization performance. Additionally, 10% of the training portion was reserved for internal validation during training. Model accuracy was evaluated using MAE, MSE, MAPE, and the coefficient of determination R<sup>2</sup>. All architectures achieved R<sup>2</sup> > 0.999 on the test sets. Generalization capability was further assessed on fully independent cycles 251, 260, 300, 350, 400, 450, and 500. Among all architectures, the LSTM network showed the highest accuracy and the most stable extrapolation, consistently reproducing hysteresis loops across frequencies 0.1-3 Hz and 10 Hz, whereas the GRU network showed the best performance at 5 Hz. Model interpretability using the Integrated Gradient (IG) method revealed that Stress is the dominant factor influencing the predicted strain, contributing the largest proportion to the overall feature importance. The UpDown parameter has a stable but secondary role, reflecting transitions between loading and unloading phases. The influence of the Cycle feature gradually increases with the cycle number, indicating the model's ability to account for the accumulation of material fatigue effects. The obtained interpretability results confirm the physical plausibility of the model and enhance confidence in its predictions. Interpretability ; Hysteresis ; Generalization ; Shape-memory alloy ; Artificial neural network ; Feature (linguistics) ; Variable (mathematics) ; Stress (linguistics) Shape Memory Alloy Transformations ; Machine Learning in Materials Science ; Aeroelasticity and Vibration Control
Authors: Younes, Eliane | Hachem, Elie | Bernacki, Marc
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2025-11-07)
Field: Materials Science — Materials Chemistry

Predicting Grain Growth in Polycrystalline Materials Using Deep Learning Time Series Models Grain Growth strongly influences the mechanical behavior of materials, making its prediction a key objective in microstructural engineering. In this study, several deep learning approaches were evaluated, including recurrent neural networks (RNN), long short-term memory (LSTM), temporal convolutional networks (TCN), and transformers, to forecast grain size distributions during grain growth. Unlike full-field simulations, which are computationally demanding, the present work relies on mean-field statistical descriptors extracted from high-fidelity simulations. A dataset of 120 grain growth sequences was processed into normalized grain size distributions as a function of time. The models were trained to predict future distributions from a short temporal history using a recursive forecasting strategy. Among the tested models, the LSTM network achieved the highest accuracy (above 90\%) and the most stable performance, maintaining physically consistent predictions over extended horizons while reducing computation time from about 20 minutes per sequence to only a few seconds, whereas the other architectures tended to diverge when forecasting further in time. These results highlight the potential of low-dimensional descriptors and LSTM-based forecasting for efficient and accurate microstructure prediction, with direct implications for digital twin development and process optimization. Deep learning ; Series (stratigraphy) ; Time series ; Artificial neural network ; Convolutional neural network ; Computation ; Process (computing) ; Pattern recognition (psychology) Machine Learning in Materials Science ; Metallurgy and Material Forming ; Microstructure and Mechanical Properties of Steels
Authors: Mustafa Aksoy | Yeşim Aygül | Onur Uğurlu | Ümit H. Kaynar | Gülnur Önsal
Source: Bulletin of Materials Science (article, 2025-10-11)
Field: Materials Science — Materials Chemistry

Machine learning predictions of electro-optical properties in ZnO-doped nematic liquid crystals Liquid crystal ; Doping ; Transmittance ; Composite number ; Decision tree ; Nanomaterials Phase-change materials and chalcogenides ; Photonic and Optical Devices ; Quantum Dots Synthesis And Properties
Authors: Shiqiang Hao | Michael C. Gao
Source: International Journal of Hydrogen Energy (article, 2025-12-19)
Field: Materials Science — Metals and Alloys

Impact of hydrogen and vacancy on the properties of Fe–Cr–Ni austenitic alloys using machine learning potential Hydrogen ; Stacking-fault energy ; Vacancy defect ; Hydrogen embrittlement ; Austenite ; Alloy ; Stacking fault ; Molecular dynamics ; Kinetic Monte Carlo Hydrogen embrittlement and corrosion behaviors in metals ; Hydrogen Storage and Materials ; Nuclear Materials and Properties
Authors: Nidaa Ali Hadi | Mohammed Q Al Badiri | Faten Jassim Mohammed | Afrah Hadib Dahi
Source: International Journal of Frontline Research in Chemistry and Pharmacy (article, 2025-12-12)
Field: Chemistry — Organic Chemistry

Click Chemistry: Innovative strategies and bio-orthogonal applications Click chemistry has become one of the most innovative methods in modern chemical research, largely due to its unparalleled efficiency, modularity, and ease of use. It enables the rapid synthesis of complex molecules and functional materials under mild conditions, is environmentally friendly, and offers high yields and excellent functional group tolerance. Over the past two decades, click chemistry has evolved from classic copper(I)-catalyzed azido-yne cycloaddition reactions to a range of bioorthogonal, ultrafast, metal-free reactions, as well as strain-induced cycloaddition reactions, tetrazine-based reverse Diels-Alder linkages, and sulfur-fluorine exchange reactions. These advances have enabled precise molecular manipulation in biological systems and supported advanced applications in areas such as real-time imaging, targeted drug delivery, metabolic labeling, and pre-targeted radiochemistry. …Beyond the fields of chemistry and biology, click chemistry is now also fundamental to the work of materials scientists, polymer engineers, and especially nanoparticle experts. Its modular and predictable nature allows for easy transformation into gels, smart materials capable of detecting stimulus-induced shape or functional changes, surface coatings with on-demand integrated electronics, and controllable nanocarriers with diverse structures. Advances in catalyst design optimization, ligand acceleration, and computer technology have not only improved reaction rates but also enhanced stability and biocompatibility. This has enabled the widespread application of this powerful new chemical synthesis tool in industrial and clinical fields. For example, flow chemistry and fluidized bed catalysis have shortened reaction times, significantly increased product yields, and reduced the amount of toxic waste flowing into rivers. Despite the numerous successes of click chemistry, it still faces challenges such as reagent stability, side reactions, cost, and the development of more environmentally friendly and sustainable processes. Machine learning, computer modeling, and reaction design promise to help address these issues in the future. Therefore, researchers can expect the emergence of next-generation click reagents with customizable reactivity, selectivity for planar structures and spatial orientation, and compatibility in complex biological or material environments. This will make click chemistry a versatile, reliable, and powerful method for molecular construction, opening up unprecedented possibilities for the future of synthetic chemistry, biomedical research, and materials design—a truly new era. Click chemistry ; Cycloaddition ; Modular design ; Flow chemistry ; Catalysis ; Material chemistry ; Continuous flow ; Chemical process Click Chemistry and Applications ; Advanced Polymer Synthesis and Characterization ; Biochemical and Structural Characterization
Authors: Younes, Eliane | Hachem, Elie | Bernacki, Marc
Source: ArXiv.org (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

Predicting Grain Growth in Polycrystalline Materials Using Deep Learning Time Series Models Grain Growth strongly influences the mechanical behavior of materials, making its prediction a key objective in microstructural engineering. In this study, several deep learning approaches were evaluated, including recurrent neural networks (RNN), long short-term memory (LSTM), temporal convolutional networks (TCN), and transformers, to forecast grain size distributions during grain growth. Unlike full-field simulations, which are computationally demanding, the present work relies on mean-field statistical descriptors extracted from high-fidelity simulations. A dataset of 120 grain growth sequences was processed into normalized grain size distributions as a function of time. The models were trained to predict future distributions from a short temporal history using a recursive forecasting strategy. Among the tested models, the LSTM network achieved the highest accuracy (above 90\%) and the most stable performance, maintaining physically consistent predictions over extended horizons while reducing computation time from about 20 minutes per sequence to only a few seconds, whereas the other architectures tended to diverge when forecasting further in time. These results highlight the potential of low-dimensional descriptors and LSTM-based forecasting for efficient and accurate microstructure prediction, with direct implications for digital twin development and process optimization. Deep learning ; Series (stratigraphy) ; Time series ; Artificial neural network ; Convolutional neural network ; Computation ; Process (computing) ; Pattern recognition (psychology) Machine Learning in Materials Science ; Metallurgy and Material Forming ; Microstructure and Mechanical Properties of Steels
Authors: Xu Zhang | Ben Ge | Lei Che | Zezhou Chen
Source: Journal of environmental chemical engineering (article, 2025-07-15)
Field: Chemistry — Inorganic Chemistry

Decoupling the catalytic features of an Fe2O3/USY catalyst in polyethylene pyrolysis using machine learning Catalysis ; Decoupling (probability) ; Polyethylene ; Pyrolysis ; Chemical engineering ; Materials science ; Chemistry ; Organic chemistry ; Engineering ; Control engineering Zeolite Catalysis and Synthesis ; Catalysis and Hydrodesulfurization Studies ; Machine Learning in Materials Science
Authors: Fu-Sheng Yang | Liang-Chia Chen | Chao-Ching Ho
Source: Research Square (preprint, 2025-09-04)
Field: Materials Science — Surfaces, Coatings and Films

Artificial Neural Network-driven Diffraction Imaging for Nanoscale Optical Critical Dimension Metrology in Semiconductor Manufacturing Metrology ; Nanoscopic scale ; Dimension (graph theory) ; Artificial neural network ; Semiconductor ; Critical dimension ; Diffraction ; Semiconductor industry ; Materials science ; Nanotechnology ; Semiconductor device fabrication ; Optoelectronics ; Optics ; Computer science ; Engineering ; Artificial intelligence ; Physics ; Manufacturing engineering ; Mathematics Optical Coatings and Gratings ; Surface Roughness and Optical Measurements ; Near-Field Optical Microscopy
Authors: Xiaohe Wang | Yong Kou | Jiahao Yu | Ying Zhao | Guoliang Shao | Guangpu Zhang | Gazi Hao | Yubing Hu | Xiaojun Feng | Junqing Yang | Wei Jiang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

A Universal Machine Learning Potential Suitable for Ionic and Organic Crystals to Reveal the Atomic Origins of TKX-50/RDX Cocrystal Decomposition Cocrystal ; Crystal (programming language) ; Ionic bonding ; Thermal decomposition ; Decomposition ; Hydrogen bond ; Ionic crystal ; Crystal structure Machine Learning in Materials Science ; Crystallization and Solubility Studies ; Chemical Thermodynamics and Molecular Structure
Authors: Zhongcan Xiao | Guannan Zhang | Zachary Morgan | Viktor Reshniak | Xiaoping Wang
Source: Structural Dynamics (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

CrystalPilot: A machine-learning based software platform for single crystal neutron diffraction experiments We present an in-house software platform CrystalPilot, designed to enable smart, adaptive single crystal neutron diffraction experiments at the TOPAZ beamline at Oak Ridge National Laboratory. It leverages machine learning methods to provide intelligent experiment steering and advanced algorithms for background noise treatment. Neutron scattering experiments are inherently high-cost; therefore, automatic steering is essential for optimizing efficiency and maximizing scientific output. CrystalPilot employs real-time regression-based prediction models to predict the data quality and combine with automatic decision-making to steer the experiments in single crystal neutron diffraction. This approach minimizes the neutron beam time, ensuring experiments remain efficient. Background noise significantly hampers accurate analysis of material properties. We present two machine learning-based methods for background treatment: KD-Tree Estimation and Ring Feature Extraction. The former use a KD-Tree based data structure to estimate the statistical information of background in different regions, while the latter uses a non-uniform Gaussian fitting model to extract the most prominent aluminum ring artifacts in neutron scattering data. Both methods have been tested and demonstrated to effectively remove background noise, enhancing data quality and making them well-suited for different neutron scattering applications. By combining these algorithms within a cohesive software platform, CrystalPilot ensures that neutron diffraction experiments remain adaptive to real-time conditions, thereby accelerating experimental workflows and improving data fidelity. This integration of machine learning and statistical modeling establishes a robust framework for neutron experiment steering, significantly enhancing throughput, data quality, and scientific discovery. Neutron scattering ; Neutron ; Software ; Neutron diffraction ; Feature (linguistics) ; Spallation Neutron Source ; Beamline ; Oak Ridge National Laboratory ; Neutron detection Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Zichang Lin | Wenjie Chen | Yitao Lin | Xinxin Zhang | Yuegang Zhang
Source: ArXiv.org (article, 2025-12-29)
Field: Materials Science — Materials Chemistry

Universal and Experiment-calibrated Prediction of XANES through Crystal Graph Neural Network and Transfer Learning Strategy Theoretical simulation is helpful for accurate interpretation of experimental X-ray absorption near-edge structure (XANES) spectra that contain rich atomic and electronic structure information of materials. However, current simulation methods are usually too complex to give the needed accuracy and timeliness when a large amount of data need to be analyzed, such as for in-situ characterization of battery materials. To address these problems, artificial intelligence (AI) models have been developed for XANES prediction. However, instead of using experimental XANES data, the existing models are trained using simulated data, resulting in significant discrepancies between the predicted and experimental spectra. Also, the universality across different elements has not been well studied for such models. In this work, we firstly establish a crystal graph neural network, pre-trained on simulated XANES data covering 48 elements, to achieve universal XANES prediction with a low average relative square error of 0.020223; and then utilize transfer learning to calibrate the model using a small experimental XANES dataset. After calibration, the edge energy misalignment error of the predicted S, Ti and Fe K edge XANES is significantly reduced by about 80%. The method demonstrated in this work opens up a new way to achieve fast, universal, and experiment-calibrated XANES prediction. XANES ; Artificial neural network ; Graph ; Spectral line ; Absorption (acoustics) ; Enhanced Data Rates for GSM Evolution Machine Learning in Materials Science ; X-ray Spectroscopy and Fluorescence Analysis ; X-ray Diffraction in Crystallography
Authors: Yifu Li
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Screening of g-C3N4 Supported Single-Atom-Catalysts for CO2 Conversion and Potential Syngas Production Syngas ; Catalysis ; Atom (system on chip) ; Production (economics) ; Process engineering ; Chemistry ; Computer science ; Engineering ; Organic chemistry ; Economics ; Embedded system Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Cherednichenko, Anton | Afonin, Sergii | Babii, Oleg | Voitsitskyi, Taras | Stratiichuk, Roman | Koleiev, Ihor | Vozniak, Volodymyr | Shevchuk, Nazar | Ostrovsky, Zakhar | Yesylevskyy, Semen | Nafiiev, Alan | Starosyla, Serhii | Ulrich, Anne S. | Jirgensons, Aigars | Komarov, Igor V.
Source: KITopen (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Neural Network Models for Prediction of Biological Activity using Molecular Dynamics Data: A Case of Photoswitchable Peptides Prediction of biological activities of chemical compounds by the machine learning techniques in general and the neural networks (NNs) in particular, is usually based on the analysis of their binding to the target of interest. If such affinity data is not available, the ligand-based approaches can be used where the NN models are trained to assess similarity of compounds to those with known biological activity. Obviously, this approach only works well if the similarity between the training set and the evaluated molecules is sufficiently high. In the case of large and conformationally flexible organic compounds, the activity becomes dependent not only on chemical identity but also on the dynamics of molecular motions, which imposes significant challenges to existing approaches based on static structural 2D and 3D molecular descriptors. A prominent example of compounds, which are especially challenging for existing NN activity prediction techniques, are photoswitchable macrocyclic peptides containing a diarylethene “photoswitch” (DAE). These molecules exist in two isomeric forms with remarkably different biological activities, which are interconvertible by light of different wavelengths. Activity prediction models have to distinguish in this case not only between the different peptides but also between the photoisomers of the same peptide. In this work, we demonstrate that the features extracted from classical molecular dynamics (MD) trajectories are superior to conventional 2D or 3D descriptor-based features when used in activity prediction NN models of DAE-containing photoswitchable peptides. Using MD-derived features, we successfully created two NN models that predict activities of photoswitchable peptidomimetics, analogs of the natural peptidic antibiotic gramicidin S. The first model precisely predicts the cytotoxic activity of similar peptide analogs. The second model reliably predicts the differences in the biological activities of DAE photoisomers of the same peptide, even if the type of its activity differs from one in the training dataset. Our results demonstrate that accounting for MD-derived dynamic features allows generalizing the ligand-based activity prediction NN models to the cases of large and conformationally flexible molecules, which were previously considered intractable by this class of models. Artificial neural network ; Quantitative structure–activity relationship ; Similarity (geometry) ; Molecular model ; Biological activity ; Molecular dynamics ; Structural similarity ; Molecular machine Photochromic and Fluorescence Chemistry ; Click Chemistry and Applications ; Chemical Synthesis and Analysis
Authors: Ding Peng | Zhaoxi Yu | Sangen Zhao | Junhua Luo | Lin Shen | Wei‐Hai Fang
Source: The Journal of Physical Chemistry Letters (article, 2025-06-09)
Field: Chemistry — Physical and Theoretical Chemistry

Machine Learning Prediction on Birefringence of Nonlinear Optical Crystals and Polymorphs with Different Birefringence Activities Nonlinear optical (NLO) crystal materials have been widely used in the scientific and industrial fields. Birefringence is an important property of the NLO crystals. Tuning appropriate birefringence through element substitution or polymorphic transformation may promote phase-matching performance facing various demands of laser wavelength. A growing number of studies based on machine learning (ML), such as the multilevel descriptors developed in our group (Zhang et al. <i>J. Phys. Chem. C</i> 2021, 125, 25175-25188), can successfully predict birefringence of NLO materials. However, how to identify polymorphs with different birefringence activities is still a nascent research topic. In this work, we proposed hp-wACSFs, a new descriptor based on the widely used atom-centered symmetric function, to predict the birefringence of inorganic crystals. A series of ML classifiers were built using hp-wACSFs. Two learning tasks, which aim at birefringence-active NLO crystals or polymorphs with different birefringence activities, were implemented. The performance on the former task was as good as our previously reported work, while the best accuracy on the latter task, which cannot be processed in the absence of three-dimensional descriptors, achieved 0.8 in this work. We finally implemented virtual screening using constructed ML models to search polymorphs with different birefringence activities. Birefringence ; Materials science ; Crystal (programming language) ; Nonlinear optical ; Work (physics) ; Nonlinear system ; Machine learning ; Optics ; Artificial intelligence ; Computer science ; Physics ; Thermodynamics ; Quantum mechanics Crystallography and molecular interactions ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography