jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Elisabetta Nocerino
Source: Journal of Physics D Applied Physics (review, 2025-09-04)
Field: Materials Science — Materials Chemistry

Emergent properties and the multiscale characterization challenge in condensed matter, from crystals to complex materials: a review Abstract The complexity of condensed matter arises from emergent behaviors that cannot be understood by investigating individual constituents in isolation. While traditional condensed-matter approaches, developed primarily for ideal crystalline solids, have provided important insights into symmetry, order, and electronic structure, they fall short in describing the rich, multiscale organization of hierarchical and soft materials. These systems exhibit structural correlations across multiple length and time scales, often governed by nonlinear interactions that span from molecular to macroscopic domains. This review explores how the convergence of emerging experimental and computational strategies is redefining our ability to characterize and model such systems. We outline how multimodal techniques, combining scattering, imaging, and spectroscopy, can map structural order and dynamics across scales, with methods such as small-angle scattering tensor tomography, dark-field imaging, and ultrafast spectroscopies, providing unprecedented spatiotemporal resolution. On the computational front, machine learning approaches such as graph neural networks, neural operators, and physics-informed models, offer powerful tools to connect disparate scales and uncover hidden correlations in high-dimensional data. These advancements have the potential to close the gap between structure and function in complex materials, thereby addressing one of the Grand Challenges of contemporary material science: understanding and engineering multiscale architectures whose emergent properties underpin the behavior of next-generation functional materials, biological systems, and adaptive technologies. Characterization (materials science) ; Materials science ; Nanotechnology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; nanoparticles nucleation surface interactions
Authors: Chiheb Ben Mahmoud | Louise A. M. Rosset | Jonathan R. Yates | Volker L. Deringer
Source: The Journal of Chemical Physics (article, 2025-07-09)
Field: Chemistry — Spectroscopy

Graph-neural-network predictions of solid-state NMR parameters in silica from spherical tensor decomposition Nuclear magnetic resonance (NMR) is a powerful spectroscopic technique that is sensitive to the local atomic structure of matter. Computational predictions of NMR parameters can help interpret experimental data and validate structural models, and machine learning (ML) has emerged as an efficient route to making such predictions. Here, we systematically study graph-neural-network approaches to representing and learning tensor quantities for solid-state NMR-in particular, the anisotropic magnetic shielding and the electric field gradient. We assess how the numerical accuracy of different ML models translates into prediction quality for experimentally relevant NMR properties: chemical shifts, quadrupolar coupling constants, tensor orientations, and even 2D spectra. We apply these ML models to a structurally diverse dataset of amorphous SiO2 configurations, spanning a wide range of density and local order, to larger configurations beyond the reach of traditional first-principles methods, and to the dynamics of the α-β inversion in cristobalite. Our work marks a step toward streamlining ML-driven NMR predictions for both static and dynamic behavior of complex materials and toward bridging the gap between first-principles modeling and real-world experimental data. Tensor (intrinsic definition) ; Solid-state nuclear magnetic resonance ; Chemical shift ; Artificial neural network ; Amorphous solid ; Anisotropy ; Computer science ; Materials science ; J-coupling ; Statistical physics ; Nuclear magnetic resonance spectroscopy ; Biological system ; Nuclear magnetic resonance ; Artificial intelligence ; Chemistry ; Physics ; Mathematics ; Optics Advanced NMR Techniques and Applications ; Advanced Condensed Matter Physics ; X-ray Diffraction in Crystallography
Authors: Mingrou Xie | Daniel Schwalbe‐Koda | Yolanda M. Semanate-Esquivel | Estefanía Bello‐Jurado | Alexander J. Hoffman | Omar A. Santiago-Reyes | Cecilia Paris | Manuel Moliner | Rafael Gómez‐Bombarelli
Source: Nature Computational Science (article, 2025-08-07)
Field: Chemistry — Inorganic Chemistry

A comprehensive mapping of zeolite–template chemical space Zeolite ; Space (punctuation) ; Chemical space ; Chemical engineering ; Computer science ; Materials science ; Chemistry ; Engineering ; Organic chemistry ; Biochemistry ; Catalysis Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Moucun Yuan | Zihan Zou | Yi Luo | Jun Jiang | Wei Hu
Source: The Journal of Physical Chemistry Letters (article, 2025-04-14)
Field: Materials Science — Materials Chemistry

QMe14S: A Comprehensive and Efficient Spectral Data Set for Small Organic Molecules Developing machine learning protocols for molecular simulations requires comprehensive and efficient data sets. Here we introduce the QMe14S data set, comprising 186,102 small organic molecules featuring 14 elements (H, B, C, N, O, F, Al, Si, P, S, Cl, As, Se, and Br) and 47 functional groups. Using density functional theory at the B3LYP/TZVP level, we optimized the geometries and calculated properties, including energy, atomic charge, atomic force, dipole moment, quadrupole moment, polarizability, octupole moment, first hyperpolarizability, and Hessian. At the same level, we obtained the harmonic IR, Raman, and NMR spectra. Furthermore, we conducted ab initio molecular dynamics simulations to generate dynamic configurations and extract nonequilibrium properties, including energy, forces, and Hessians. By leveraging our E(3)-equivariant message-passing neural network (DetaNet), we demonstrated that models trained on QMe14S outperform those trained on the previously developed QM9S data set in simulating molecular spectra. The QMe14S data set thus serves as a comprehensive benchmark for molecular simulations, offering valuable insights into structure-property relationships. Set (abstract data type) ; Data set ; Organic molecules ; Molecule ; Information retrieval ; Computer science ; Chemistry ; Artificial intelligence ; Organic chemistry Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Jun Peng | Daniel Hensel | Rakshith Venugopal | Lucas Rave | Colin Schormann | Wolfram Koch | Robert H. Blick | Robert Zierold
Source: Advanced Engineering Materials (article, 2025-02-15)
Field: Materials Science — Polymers and Plastics

ALD‐Assisted VO<sub>2</sub> for Memristor Application Vanadium dioxide (VO 2 ) is a well‐known candidate for memristor applications due to its insulator‐to‐metal transition (IMT) characteristics. The fabrication of memristor devices requires highly controlled synthesis processes concerning the material chemistry and geometry. Atomic layer deposition (ALD) offers unique advantages for the fabrication of hardware neural networks, such as miniaturization, conformality, and sub‐nm thickness control. Herein, an ALD process for non‐stoichiometric vanadium oxide (VO x ) using tetrakis(dimethylamino)vanadium (TDMAV) and water as precursors is presented. Subsequently, a tailor‐made annealing process converts VO x into VO 2 , which exhibits an IMT of about three orders of magnitude at around 70 °C, rendering it a promising memristor material. VO 2 thin film and Si–Al 2 O 3 /VO 2 core/shell memristors are fabricated and analyzed, both of which exhibited I–V hysteresis loops, indicating their suitability for memristor applications in both 2D and 3D morphologies. Additionally, these memristors are sensitive to the operation temperature, with the hysteresis loops narrowing and shifting toward lower voltages as temperature increases, eventually disappearing beyond VO 2 's intrinsic phase transition temperature. This study highlights the viability of ALD‐assisted VO 2 for memristor applications and demonstrates its potential for advancing the three‐dimensionalization of neuromorphic chips. Memristor ; Materials science ; Atomic layer deposition ; Fabrication ; Vanadium ; Annealing (glass) ; Nanotechnology ; Thin film ; Neuromorphic engineering ; Hysteresis ; Resistive random-access memory ; Vanadium oxide ; Optoelectronics ; Voltage ; Electronic engineering ; Electrical engineering ; Computer science ; Composite material ; Artificial neural network ; Condensed matter physics ; Metallurgy ; Physics Transition Metal Oxide Nanomaterials ; Advanced Memory and Neural Computing ; Gas Sensing Nanomaterials and Sensors
Authors: Murugan Madhubalan | S. Sathish | Minda Mahivardhan | T. Prabhakaran | Kannan Karthika Abimanyu | Ramesh Kumar | Jong‐Hyun Seo | Binxi Gu | Subbaiah Muthu Prabhu | Byong‐Hun Jeon
Source: Small Structures (article, 2025-12-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advances in Selenium‐Based Materials for Supercapacitors: Chemistry, Interaction Mechanisms, and Practical Applications High‐performance supercapacitors require electrode materials that can simultaneously deliver elevated energy storage capacity, rapid power output, and long‐term cycling stability. Compared to sulfur analogs, selenium (Se)‐based materials have shown great promise because of their favorable redox activity, enhanced polarizability, and superior electrical conductivity. Herein, recent developments in Co–Se, Ni–Se, sulfoselenides (S–O–Se), and other Se‐based electrode materials are critically reviewed, with emphasis on their synthesis techniques, structural advantages, and electrochemical performance in relation to charge storage mechanisms. Comparative analysis reveals that their capacitance and durability are significantly enhanced by morphological control, heterostructure engineering, and synergistic effects with conductive substrates. The incorporation of Se into materials and their charge storage mechanisms is discussed using density functional theory analysis and artificial intelligence/machine learning approaches. This review highlights the growing contribution of Se‐based electrodes to supercapacitor technology by critically examining their development from basic binary selenides to intricate hybrid systems. Furthermore, the influence of Se metal centers and carbon frameworks in enhancing mechanical stability, active site accessibility, and ion transport is meticulously investigated. To close the gap toward practical next‐generation energy storage devices, the advantages and limitations of Se and its composite systems are evaluated, and prospects for advancing Se‐based electrodes are carefully outlined. Supercapacitor ; Energy storage ; Electrochemical energy storage ; Capacitance ; Electrode ; Durability ; Battery (electricity) ; Electrochemistry Supercapacitor Materials and Fabrication ; MXene and MAX Phase Materials ; Advancements in Battery Materials
Authors: Dines Chandra Santra | Hajime Kawanami
Source: Chemical Communications (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Recent development in catalysts for the production, storage and release of hydrogen The rising demand for energy and growing environmental concerns have intensified the search for sustainable alternatives to fossil fuels, establishing hydrogen as a promising clean energy carrier. However, its widespread adoption is currently limited by its reliance on a fossil-based production method, as well as by persistent challenges in storage and transport. Recent advancements in CO<sub>2</sub> hydrogenation, catalytic design, and machine learning-driven optimisation offer ways to enhance both the sustainability and efficiency of hydrogen production. Among these advancements, formic acid (FA) is emerging as a sustainable energy source due to its ease of handling compared to solid or gaseous materials. This review explores the development of state-of-the-art catalysts for CO<sub>2</sub> hydrogenation and FA dehydrogenation, contributing to the advancement of a hydrogen-based economy. It also delves into the methanol economy as an alternative strategy. Particular emphasis has been placed on transition metal-based complexes, which are recognised for their high catalytic activity associated with a well-defined mechanism, including Pd-based heterogeneous and non-noble metal homogeneous catalysts. Furthermore, this review demonstrates how machine learning can accelerate catalyst innovation. By addressing the key challenges of hydrogen storage, efficiency, and scalability, this review contributes to the development of practical, cost-effective, and environmentally friendly hydrogen energy solutions. Hydrogen storage ; Catalysis ; Production (economics) ; Hydrogen production ; Chemistry ; Chemical engineering ; Hydrogen ; Combinatorial chemistry ; Organic chemistry ; Engineering Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Ammonia Synthesis and Nitrogen Reduction
Authors: Aidan J. Canning | Joy Q. Li | Jia Ning Chen | Khang Hoang | Taylor Thorsen | A. Vaziri | Tuan Vo‐Dinh
Source: Journal of Materials Science (article, 2025-02-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Tunable and scalable production of nanostar particle platforms for diverse applications using an AI-integrated automated synthesis system Materials science ; Scalability ; Particle (ecology) ; Production (economics) ; Solid mechanics ; Nanotechnology ; Systems engineering ; Process engineering ; Computer science ; Engineering ; Composite material ; Operating system Gold and Silver Nanoparticles Synthesis and Applications ; Machine Learning in Materials Science ; Electrohydrodynamics and Fluid Dynamics
Authors: Qiyu Yang | Congzi Xia | Yuanwu Cui | Xiaoqiuyan Zhang | Tianyu Zhang | Yong Huang | Diwei Liu | Yanyu Wei | Yubin Gong | Guangyou Fang | Zhenyou Wang | Xuequan Chen | Min Hu
Source: Heliyon (article, 2025-03-01)
Field: Chemistry — Analytical Chemistry

Fusion of terahertz spectroscopy and Raman spectroscopy combined with support vector machine to distinguish different pericarpium citri reticulatae species Pericarpium citri reticulatae (PCR), or Chenpi, is a valuable medicinal and food resource in China, but its classification can be challenging due to variations in medicinal value and market price. Traditional methods for identifying PCR involve subjective sensory analysis or cumbersome chemical techniques. This paper explores a new approach by combining terahertz and Raman spectral data with machine learning to classify seven PCR species of different ages, origins, and artificial skins. We used K-nearest neighbor (KNN), convolutional neural networks (CNN), and support vector machines (SVM) for model selection. The best model achieved 96.8 % accuracy and high AUC metrics. An innovative Manhattan data preprocessing method improved model performance by addressing errors due to instrumental and environmental factors. The T-distributed stochastic neighbor embedding (t-SNE) algorithm visualized data connections, and recursive feature elimination identified key frequencies. The THz band achieved 94.1 % accuracy using just 5.4 % of the original data, and the Raman band reached 77.8 % accuracy with 10 feature frequencies. This study demonstrates the effectiveness of spectral fusion and machine learning for precise PCR classification, offering valuable applications for traditional Chinese medicine and the food industry. Raman spectroscopy ; Spectroscopy ; Terahertz spectroscopy and technology ; Terahertz radiation ; Support vector machine ; Analytical Chemistry (journal) ; Nuclear magnetic resonance ; Materials science ; Artificial intelligence ; Chemistry ; Physics ; Computer science ; Optics ; Optoelectronics ; Chromatography ; Quantum mechanics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Harini Loganathan | D. Durgalakshmi | R. Ajay Rakkesh
Source: TrAC Trends in Analytical Chemistry (article, 2025-04-21)
Field: Materials Science — Materials Chemistry

Recent advances in MXene-based electrochemical sensors for healthcare applications Nanotechnology ; Materials science ; Electrochemistry ; Health care ; Computer science ; Chemistry ; Political science ; Electrode MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Jianing Niu | Xujiang Chao | Hongcheng Lian | Jun Luo | Lehua Qi
Source: Chemical Engineering Journal (article, 2025-06-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning and droplet-based printing accelerating the achievement of flexible terahertz metamaterial absorber Terahertz radiation ; Metamaterial ; Materials science ; 3D printing ; Computer science ; Nanotechnology ; Optoelectronics ; Composite material Metamaterials and Metasurfaces Applications ; Energy Harvesting in Wireless Networks ; Advanced Antenna and Metasurface Technologies
Authors: Heng Li | Runnan Chen | Yun Lü | Juli Jiang | Chen Lin | Leyong Wang
Source: Coordination Chemistry Reviews (article, 2025-04-18)
Field: Chemistry — Physical and Theoretical Chemistry

Advances in crystallization chaperones based on a host-guest system for structural determination of difficult-to-crystallize molecules Chemistry ; Crystallization ; Molecule ; Host (biology) ; Crystallography ; Nanotechnology ; Organic chemistry Crystallography and molecular interactions ; Enzyme Structure and Function ; Protein Structure and Dynamics
Authors: Noor Hadi Aysa | Sajjad Karimi
Source: Polymer Composites (article, 2025-04-15)
Field: Materials Science — Polymers and Plastics

Integrating machine learning and digital twin for strength prediction of <scp>CFRP</scp>/aluminum adhesive joints under hygrothermal conditions Abstract This study investigates the application of machine learning models integrated with a digital twin (DT) framework to predict and correlate the performance of carbon fibre‐reinforced polymer‐to‐aluminum adhesive joints subjected to hygrothermal aging. By combining experimental methods with machine learning techniques, the research aims to bridge the gap between the effects of natural and accelerated aging on adhesive joints. The joints were manufactured and then left to age naturally for a period of one to 3 years. For accelerated aging, the joints were subjected to hygrothermal conditions for a period of four to 50 days. Three‐point bending tests were utilized to evaluate the performance of the joints. To evaluate natural aging periods using accelerated aging data, five machine learning algorithms were used: support vector regression (SVR), artificial neural network (ANN), linear regression, random forest regression (RF) and XGBoost. scanning electron microscopy (SEM) analyses showed that moisture absorption caused a substantial change in the surface morphology of aluminum adherends, including increased roughness and crystalline formations. The results indicated that XGBoost has provided almost perfect predictions since MSE values equal to 0 were observed at all iterations, highlighting its accuracy and reliability. In contrast, the SVR and linear regression models demonstrated much lower accuracy, with clear differences observed in their predictions. The integration of digital twin with machine learning approaches turns out to be the most efficient method of real‐time adaptation of the model as well as accurate performance prediction, enhancing the durability and reliability of the composite structures. Highlights Strength prediction of adhesive joints by using Machine learning and digital twin. SEM revealed moisture‐induced changes in aluminum surface morphology. XGBoost model showed high prediction accuracy. Materials science ; Adhesive ; Composite material ; Aluminium ; Structural engineering ; Engineering ; Layer (electronics) Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Tribology and Wear Analysis
Authors: Manzar Masud | Aamir Mubashar | Salman Sagheer Warsi | Volkan Esat | Saqib Anwar
Source: Journal of the Brazilian Society of Mechanical Sciences and Engineering (article, 2025-04-04)
Field: Materials Science — Polymers and Plastics

Optimizing bio-hybrid composites for impact resistance using machine learning Composite material ; Materials science ; Impact resistance ; Composite number Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Cellular and Composite Structures
Authors: Yisheng Xu | Wanxun Feng | Liquan Wang | Jiaping Lin | Xiangfei Ye | Xinyao Xu | Lei Du
Source: Advanced Functional Materials (article, 2025-05-16)
Field: Materials Science — Polymers and Plastics

Data‐Driven Design of High‐Performance Polyimides With Enhanced Heat Resistance and Dielectric Properties Abstract The evolution of electronic technology, such as high‐speed, high‐frequency, and high‐density integrated circuits, imposes higher performance requirements on advanced functional materials like polyimides. However, the prolonged development cycle linked with conventional trial‐and‐error methods results in a noticeable gap between material research and its practical application. Here, a materials genome approach is proposed to accelerate the discovery of polyimides exhibiting exceptional dielectric properties under elevated temperatures and high frequencies. To address the scarcity of data, theoretical high‐frequency dielectric properties are derived by employing the Havriliak‐Negami dielectric relaxation model to complement experimental data. With the augmented data of polyimides, a multi‐task learning model is proposed with hierarchical neural networks for the dielectric properties and conventional neural networks for the glass transition temperature. Structural design via genetic algorithms is implemented to engineer polyimide structures with enhanced dielectric properties. Several polyimides with high comprehensive performance are generated, and experimental validation is conducted. Shapley additive explanations analysis reveals crucial structural elements influencing the performance. The research framework established in this work can guide the design of other polymeric functional materials. Materials science ; Dielectric ; High heat ; Heat resistance ; Composite material ; Material Design ; Optoelectronics ; Engineering physics Synthesis and properties of polymers ; Dielectric materials and actuators ; Epoxy Resin Curing Processes