jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mi Zhang | Hua Hou | Baoshan Wang
Source:  (article, 2025-06-08)
Field: Materials Science — Materials Chemistry

Valence-Bond Based Machine-Learning Structure-Activity Relationship Models to Assess the Performance of Gaseous Dielectrics in Multidimensional Aspects In view of the growing impact of sulfur hexafluoride (SF<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</inf>) on global environment, the development of eco-friendly dielectric replacement gases has attracted considerable experimental and theoretical interests. A valence-bond based structure-activity relationship method is proposed in this work to assess the performance of a gas in six aspects including dielectric strength, boiling point, global warming potential, arc interruption capability, toxicity, and flammability. The chemical bonds are extracted from the simplified molecular input line entry specification (SMILES). Good correlations between theory and experiment are achieved using the extensive machine-learning training by means of the artificial neural network, shedding new light on the characteristic sought in SF<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</inf> replacement gases. Valence (chemistry) ; Dielectric ; Machine learning ; Computer science ; Materials science ; Artificial intelligence ; Chemistry ; Optoelectronics Machine Learning in Materials Science ; Semiconductor materials and devices ; Ionic liquids properties and applications
Authors: Elena Trukhan | Efim Mazhnik | Artem R. Oganov
Source: ArXiv.org (preprint, 2025-02-12)
Field: Materials Science — Materials Chemistry

Acceleration of crystal structure relaxation with Deep Reinforcement Learning We introduce a Deep Reinforcement Learning (DRL) model for the structure relaxation of crystal materials and compare different types of neural network architectures and reinforcement learning algorithms for this purpose. Experiments are conducted on Al-Fe structures, with potential energy surfaces generated using EAM potentials. We examine the influence of parameter settings on model performance and benchmark the best-performing models against classical optimization algorithms. Additionally, the model's capacity to generalize learned interaction patterns from smaller atomic systems to more complex systems is assessed. The results demonstrate the potential of DRL models to enhance the efficiency of structure relaxation compared to classical optimizers. Reinforcement learning ; Relaxation (psychology) ; Benchmark (surveying) ; Acceleration ; Reinforcement ; Artificial neural network ; Crystal structure Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Min Han (56772) | Jun Liang (251318) | Ziwei Wang (3344528) | Biao Jin (279879)
Source: Figshare (article, 2025-06-03)
Field: Chemistry — Spectroscopy

Accurate Predictionof the Diffusion Coefficientsof Organic Compounds in Water by Multimodal Learning The aqueous diffusion coefficients of organic compounds are among the most important topics of interest for chemical research and engineering, particularly for the dispersal of organic pollutants in water environments. The experimental determination of the diffusion coefficients is time-consuming, and the experimental data are lacking for most newly synthesized chemicals. Our study developed a multimodal deep learning model incorporating molecular images, molecular descriptors, and temperatures to predict aqueous diffusion coefficients at varying temperatures. The deep learning model made accurate predictions on the test set (<i>R</i><sup>2</sup> = 0.986) and had a considerably lower error than previous empirical equations. Besides, the model interpretation indicated that the deep learning model correctly captured the effects of molecular features and temperature on the prediction of diffusion coefficients. Thus, our study provided a valuable tool for the rapid and accurate prediction of organic compounds diffusion coefficients in water at varying temperatures and also facilitated a better understanding of how different molecular features and water temperatures influence diffusion-controlled processes in both the environmental and engineered systems. Diffusion ; Aqueous solution ; Experimental data ; Empirical modelling ; Molecular descriptor ; Deep learning ; Organic molecules Diffusion Coefficients in Liquids ; Membrane Separation and Gas Transport ; Adsorption, diffusion, and thermodynamic properties of materials
Authors: Sumaira Naeem | Tagir Kadyrov | Norah Salem Alsaiari | M.S. Al‐Buriahi
Source: Chemical Physics Letters (article, 2025-04-18)
Field: Materials Science — Materials Chemistry

A faster computational frame work for dye design and screening: A goal to achieve higher ionization energy Frame (networking) ; Work (physics) ; Ionization ; Energy (signal processing) ; Computer science ; Frame work ; Computational science ; Computational physics ; Chemistry ; Physics ; Engineering ; Telecommunications ; Mechanical engineering ; Theoretical physics ; Ion ; Organic chemistry ; Quantum mechanics Photochromic and Fluorescence Chemistry ; Pigment Synthesis and Properties ; Photochemistry and Electron Transfer Studies
Authors: Hancheng Zhao | Jiankun Pu | Mgcini Keith Phuthi | Venkatasubramanian Viswanathan
Source: ECS Meeting Abstracts (article, 2025-07-11)
Field: Chemistry — Inorganic Chemistry

Structure Disorder in Carbon Monofluoride and Its Effect on Electronic, Phononic and Mechanical Properties Electrification of aviation requires energy density far beyond current Li-ion batteries. One perspective candidate is Li-(CF)n, where (CF)n stands for poly(carbon monofluoride), and this chemistry achieves theoretical specific energy of 3600Wh/kg. One bottleneck hindering the wide application of this battery chemistry is lack of understanding of the (CF)n cathode microstructure, which is disordered in nature. In this work, we study the possible modes of structure disorder and its effect on electronic, phononic and mechanical properties. We developed a machine learning interatomic potential (MLIP) under the NequIP framework that reaches density functional theory (DFT) level accuracy to facilitate simulation of disordered structures, and use this model for analyzing structure-property relation of (CF)n. We found that stacking of multiple disordered (CF)n layer quantitatively reproduces X-ray diffraction (XRD) peak broadening observed in experiments. The band gap is observed to increase almost linearly with formation energy of disordered structure, which we found to result from local C-F and C-C bond distortion. A negative correlation between both highest phonon frequency as well as bulk and shear modulus with increase of formation energy is also observed, which leads us to identify a linear relation between (CF)n C-C bonding and formation energy. Our study could be an important step to further the understanding of the structure-property relation of disordered (CF)n and provide insight for future (CF)n synthesis. Materials science ; Carbon fibers ; Electronic structure ; Condensed matter physics ; Composite material ; Physics ; Composite number Inorganic Fluorides and Related Compounds ; Fiber-reinforced polymer composites
Authors: Zahra Poursorkh (11647571) | Hooman Tavakolizadeh (18296572) | Ashton Christy (11647580) | Wendy Tsai (21657686) | Paul Bicho (10953022) | Edward Grant (1889932)
Source: Figshare (article, 2025-07-04)
Field: Materials Science — Biomaterials

Fusing RamanSpectra with Fiber Metrics in MachineLearning Models That Predict the Physical–Mechanical Propertiesof Paper Wood pulp manufacturers regularly monitor specific sets of physical and mechanical properties of production pulps to ensure that they meet paper-maker product-quality targets. Conventional methods based on time-consuming manual procedures serve as cornerstones of pulp quality control. However, low-throughput laboratory analysis presents substantial personnel and material resource demands. As a result, conventional methods cannot support real-time process management. Here, we describe an alternative approach that applies data fusion to combine two instrumental measures of pulp quality and provide instantaneous input for analysis by advanced machine learning models that predict 20 paper properties. This strategy uses Raman spectroscopy to capture molecular vibrational structure indicative of the chemical composition and morphological state of a pulp sample, together with pulp and fiber physical attributes such as bulk freeness and distributions of individual length, curl, and width measured online by a PulpEye integrated analysis system. The present work has collected these instrumental measures to build an <b>X</b> matrix of predictors for more than 500 production pulp samples. An industry-standard battery of conventional physical and mechanical quality control tests provides a complete set of response matrices, <b>Y</b>, that support the training of machine learning models that predict conventional measures of paper quality from instantaneous spectroscopic and physical attributes of pulp. These machine-learning algorithms emphasize features from both Raman spectra and PulpEye metrics. Among multivariate analysis methods, we find that eXtreme Gradient Boosting (XGBoost) algorithms consistently outperform conventional Partial Least-Squares Regression (PLSR). The best-trained models predict many target paper properties with errors comparable to those of wet-lab measurements, promising a method for real-time process control in the pulp and paper industry. Implementation will offer data-driven insights that reduce the reliance on manual testing and facilitate a deeper understanding of the factors that affect the properties of complex cellulosic materials. Boosting (machine learning) ; Pulp (tooth) ; Support vector machine ; Papermaking ; Pattern recognition (psychology) ; Paper machine ; Multivariate statistics ; Raman spectroscopy Advanced Cellulose Research Studies ; Lignin and Wood Chemistry ; Spectroscopy and Chemometric Analyses
Authors: Temesgen Atnafu Yemata | Tessera Alemneh Wubieneh | Yun Zheng | Wee Shong Chin | Messele Kassaw Tadsual | Tadisso Gesessee Beyene
Source: Spectroscopy Journal (review, 2025-10-14)
Field: Materials Science — Materials Chemistry

Spectroscopic and Microscopic Characterization of Inorganic and Polymer Thermoelectric Materials: A Review Thermoelectric (TE) materials represent a critical frontier in sustainable energy conversion technologies, providing direct thermal-to-electrical energy conversion with solid-state reliability. The optimizations of TE performance demand a nuanced comprehension of structure–property relationships across diverse length scales. This review summarizes established and emerging spectroscopic and microscopic techniques used to characterize inorganic and polymer TE materials, specifically poly(3,4-ethylenedioxythiophene): poly(styrenesulfonate) (PEDOT:PSS). For inorganic TE, ultraviolet–visible (UV–Vis) spectroscopy, energy-dispersive X-ray (EDX) spectroscopy, and X-ray photoelectron spectroscopy (XPS) are widely applied for electronic structure characterization. For phase analysis of inorganic TE materials, Raman spectroscopy (RS), electron energy loss spectroscopy (EELS), and nuclear magnetic resonance (NMR) spectroscopy are utilized. For analyzing the surface morphology and crystalline structure, chemical scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray diffraction (XRD) are commonly used. For polymer TE materials, ultraviolet−visible–near-infrared (UV−Vis−NIR) spectroscopy and ultraviolet photoelectron spectroscopy (UPS) are generally employed for determining electronic structure. For functional group analysis of polymer TE, attenuated total reflectance–Fourier-transform infrared (ATR−FTIR) spectroscopy and RS are broadly utilized. XPS is used for elemental composition analysis of polymer TE. For the surface morphology of polymer TE, atomic force microscopic (AFM) and SEM are applied. Grazing incidence wide-angle X-ray scattering (GIWAXS) and XRD are employed for analyzing the crystalline structures of polymer TE materials. These techniques elucidate electronic, structural, morphological, and chemical properties, aiding in optimizing TE properties like conductivity, thermal stability, and mechanical strength. This review also suggests future research directions, including in situ methods and machine learning-assisted multi-dimensional spectroscopy to enhance TE performance for applications in electronic devices, energy storage, and solar cells. X-ray photoelectron spectroscopy ; Spectroscopy ; Raman spectroscopy ; Polymer ; Infrared spectroscopy ; Scanning electron microscope ; Energy-dispersive X-ray spectroscopy ; Electron energy loss spectroscopy ; Thermoelectric effect Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Thermal Radiation and Cooling Technologies
Authors: Mohamed Soliman (806208) | Cédric Marchand (17265120) | Aymen Mahmoudi (14795644) | Neeraj Kumar Rajak (21338248) | Takashi Taniguchi (331592) | Kenji Watanabe (75040) | Arnaud Gloppe (14795641) | Bernard Doudin (1567120) | Damien Deleruyelle (1824721) | Ian O’Connor (2395594) | Abdelkarim Ouerghi (1596277) | Jean-Francois Dayen (1640275)
Source: Figshare (article, 2025-05-12)
Field: Materials Science — Materials Chemistry

Van der Waals Inverted-Floating-GateTransistors forArtificial Intelligence Electronics An inverted floating gate device architecture is introduced, demonstrated with all-van-der-Waals technology, targeting both logic and neuromorphic circuits. Integrating a top polymorphic multilayer graphene floating gate improves the electrostatic coupling to the ReS<sub>2</sub> semiconductor channel by facilitating efficient dynamic conductance tuning and enabling dual-mode reconfigurable logic and memory operations. The non-volatile capability is used to implement compact logic gates for in-memory computing. The device is also shown to emulate synaptic plasticity, with an accuracy of 92% demonstrated in simple artificial neural network simulations. Moreover, spiking neuron circuits for neural networks through a five-transistor design makes it a versatile building block for artificial intelligence electronics. These findings demonstrate the potential of hybrid integration of van der Waals materials to address the limitations of traditional semiconductor technologies and become key to developments of next-generation electronics. Neuromorphic engineering ; Logic gate ; Transistor ; Artificial neural network ; Artificial neuron ; Electronic circuit ; Electronics ; CMOS ; Key (lock) ; Memristor 2D Materials and Applications ; Advanced Memory and Neural Computing ; Graphene research and applications
Authors: Laura-Bianca Paşca | Yuanbin Liu | Andy S. Anker | Ludmilla Steier | Volker L. Deringer
Source: ArXiv.org (preprint, 2025-06-02)
Field: Materials Science — Materials Chemistry

Machine-learning-driven modelling of amorphous and polycrystalline BaZrS$_{3}$ The chalcogenide perovskite material BaZrS$_{3}$ is of growing interest for emerging thin-film photovoltaics. Here we show how machine-learning-driven modelling can be used to describe the material's amorphous precursor as well as polycrystalline structures with complex grain boundaries. Using a bespoke machine-learned interatomic potential (MLIP) model for BaZrS$_{3}$, we study the atomic-scale structure of the amorphous phase, quantify grain-boundary formation energies, and create realistic-scale polycrystalline structural models which can be compared to experimental data. Beyond BaZrS$_{3}$, our work exemplifies the increasingly central role of MLIPs in materials chemistry and marks a step towards realistic device-scale simulations of materials that are gaining momentum in the fields of photovoltaics and photocatalysis. Crystallite ; Amorphous solid ; Chalcogenide ; Photovoltaics ; Work (physics) ; Perovskite (structure) Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Phase-change materials and chalcogenides
Authors: Srinivasa Kranthi Kiran Kolachina | Ruth Agada | Jie Yan | Beni B. Dangi | Oliver Steinbock
Source:  (article, 2025-11-02)
Field: Materials Science — Materials Chemistry

Adaptive Frame Extraction and Deep CNN-Based Classification of Salt Crystallization Videos Crystallization-based classification of salt compounds, specifically Potassium Chloride (KCl), Sodium Chloride (NaCl), and Ammonium Chloride (NH<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf>Cl), is essential in chemical, pharmaceutical, and materials industries, where accurate monitoring of crystal nucleation and growth directly impacts product quality and process optimization. Traditional crystallization monitoring techniques often rely on time-consuming manual analysis or destructive physical measurements, limiting their applicability for real-time, automated applications. In this study, we propose a novel deep learning framework for automated salt crystallization classification using video-derived frame-level datasets. The proposed pipeline integrates adaptive per-class frame extraction to address variability in crystallization dynamics, along with dataset balancing strategies to mitigate class imbalance without synthetic augmentation. Multiple Convolutional Neural Network (CNN) architectures—including RegularizedCNN, ResNet18, EfficientNetB0, MobileNetV2, VGG16, DenseNet121, and VanillaCNN—are trained and evaluated across both manual (video-level) and randomized (frame-level) data splits. Experimental results demonstrate that the proposed method achieves high accuracy across multiple models, reaching up to 98.7% with the VGG16 architecture, highlighting the effectiveness of dynamic frame extraction and balancing in improving generalization. This study contributes a reproducible pipeline for video-based salt crystallization classification and provides comparative insights into model performance under varying dataset split strategies. Frame (networking) ; Extraction (chemistry) ; Pattern recognition (psychology) ; Feature extraction ; Crystallization Crystallization and Solubility Studies ; Image Retrieval and Classification Techniques ; Image Processing Techniques and Applications
Authors: Fajar Hardoyono
Source: Indonesian Journal of Chemistry (article, 2025-01-25)
Field: Chemistry — Spectroscopy

Development of Chemo-Selective Gas Sensors Based on Molecularly Imprinted Polymer-Quartz Crystal Microbalance for Detection of Bioactive Compounds in &lt;i&gt;Curcuma longa&lt;/i&gt; Turmeric contains bioactive compounds that are efficacious for human health. A breakthrough of simpler and lower-cost identification techniques is needed to utilize it. This paper aims to investigate the development of chemo-selective gas sensors for identifying α-curcumene, ar-turmerone, curlone, and β-sesquiphellandrene. Four chemo-selective sensors were created using quartz crystal microbalance (QCM) sensors coated with molecularly imprinted polymers to identify each of those compounds. The polymers were prepared using polyacrylic acid, hydrochloric acid, and pure target compounds (α-curcumene, ar-turmerone, curlone, and β-sesquiphellandrene). The turmeric odor from 10 different samples was exposed to QCM sensors. The changing frequency of QCM sensors due to the mass loading of target compounds on the surface of QCM sensors was recorded to analyze the performance of QCM sensors. The result of the principal component analysis showed that the QCM sensors performed well and could distinguish the turmeric samples at five combinations of the compounds. The turmeric sample classification using backpropagation neural networks reached high accuracies, with 97.04% in training and 98.73% in testing datasets. These findings indicate that the employment of sensory analysis using QCM sensors has the prospect of being a complementary technique for identifying bioactive compounds. Chemistry ; Quartz crystal microbalance ; Molecularly imprinted polymer ; Curcuma ; Molecular imprinting ; Combinatorial chemistry ; Chromatography ; Selectivity ; Organic chemistry ; Adsorption ; Traditional medicine ; Catalysis Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; thermodynamics and calorimetric analyses
Authors: Ibarra-Hoyos, Diego | Connors, Peter | Jang, Ho | Grain, Nathan | Klich, Israel | Chern, Gia-Wei | Liaw, Peter K. | Scully, John R. | Poon, Joseph
Source: ArXiv.org (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

From Quantum Annealing to Alloy Discovery: Towards Accelerated Design of High-Entropy Alloys Data scarcity remains a central challenge in materials discovery, where finding meaningful descriptors and tuning models for generalization is critical but inherently a discrete optimization problem prone to multiple local minima confounding the true optimal state. Classical methods often get trapped in these minima, while quantum annealing can escape them via quantum fluctuations, including tunneling, that overcome narrow energy barriers. We present a quantum-assisted machine-learning (QaML) framework that employs quantum annealing to address these combinatorial optimization challenges through feature selection, support-vector training formulated in QUBO form for classification and regression, and a new QUBO-based neural-network pruning formulation. Recursive batching enables quantum annealing to handle large feature spaces beyond current qubit limits, while quantum-pruned networks exhibit superior generalization over classical methods, suggesting that quantum annealing preferentially samples flatter, more stable regions of the loss landscape. Applied to high-entropy alloys (HEAs), a data-limited but compositionally complex testbed, the framework integrates models for fracture-strain classification and yield-strength regression under physics-based constraints. The framework identified and experimentally validated Al8Cr38Fe50Mn2Ti2 (at.%), a single-phase BCC alloy exhibiting a 0.2 % yield strength of 568 MPa, greater than 40 % compressive strain without fracture, and a critical current density in reducing acid nearly an order of magnitude lower than 304 stainless steel. These results establish QA as a practical route to overcome classical optimization limits and accelerate materials discovery. Quantum annealing ; Maxima and minima ; Quantum ; Annealing (glass) ; Simulated annealing ; Alloy ; Qubit Machine Learning in Materials Science ; High Entropy Alloys Studies ; Additive Manufacturing Materials and Processes