jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wail Al Zoubi | Manar Alnaasan | Bassem Assfour | Stefano Leoni | Iftikhar Hussain | Sungho Kim | Young Gun Ko
Source: InfoMat (article, 2025-03-20)
Field: Materials Science — Materials Chemistry

Totally defined nanocatalysis: Detection of polyelement nanoparticles by deep learning Abstract High‐entropy alloys (HEAs), which are near‐equimolar alloys of four or more metal elements, have long been used to achieve the desired properties of catalytic materials. However, a novel alloying approach that includes multiple principal elements at high concentrations to generate HEAs as novel catalytic materials has been reported. The fabrication of well‐defined ultrastable supported HEAs, which provide superior performance and stability of catalysts owing to their augmented entropy and lower Gibbs free energy, remains a critical challenge. Supported HEA catalysts are sophisticated because of the variety of their morphologies and large sizes at the nanoscale. To address these challenges, PtPdInGaP@TiO 2 , comprising five different metals, is prepared via ultrasonic‐assisted coincident electro‐oxidation–reduction precipitation (U‐SEO‐P). The electronic structure and catalytic performance of HEA nanoparticles (NPs) are studied using hard scanning transmission electron microscopy (STEM), which is the first direct observation of the electronic structure of HEA NPs. This research takes an important step forward in fully describing individual HEA NPs. Combining STEM with deep learning with convolutional neural network (CNN) of selected individual HEA NPs reveals significant aspects of shape and size for widespread and commercially important PtPdInGaP@TiO 2 NPs. The proposed method facilitates the detection and segmentation of HEA NPs, which has the potential for the development of high‐performance catalysts for the reduction of organic compounds. image Nanotechnology ; Nanoparticle ; Materials science Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Nanocluster Synthesis and Applications
Authors: Maryam Shekari | Shahab Moghari | Elmuez A. Dawi | Hossein Ali Khonakdar
Source: Polymers for Advanced Technologies (article, 2025-08-01)
Field: Chemistry — Analytical Chemistry

Molecularly Imprinted Polymers for Diagnosis and Medical Applications: Recent Advances and Progress ABSTRACT Molecularly imprinted polymers (MIPs) represent a powerful class of synthetic materials engineered to mimic the molecular recognition capabilities of natural receptors with enhanced stability, selectivity, and adaptability. This review critically examines recent advances in the design, synthesis, and biomedical applications of MIPs, emphasizing their transformative potential in diagnostics, drug delivery, and health monitoring technologies. It outlines the evolution of MIP technology—from early developments to the integration of nanomaterials and digital design tools—and explores their utility in detecting disease biomarkers associated with chronic stress, diabetes, cancer, and infectious diseases. The review also discusses the implementation of MIPs in wearable sensors, lab‐on‐chip devices, and non‐invasive sampling platforms, highlighting their suitability for point‐of‐care diagnostics and personalized medicine. Furthermore, it addresses the emerging use of MIPs in tissue engineering, regenerative medicine, and chemical exposure monitoring. Key technical challenges such as imprinting large biomolecules, batch‐to‐batch variability, and biofouling resistance are analyzed alongside innovative strategies, including epitope imprinting, antifouling coatings, and green synthesis approaches. Finally, future directions are proposed, including the integration of MIPs with machine learning, self‐powered systems, and smart diagnostics to enable next‐generation, scalable healthcare solutions. Molecularly imprinted polymer ; Materials science ; Nanotechnology ; Polymer ; Molecular imprinting ; Polymer science ; Organic chemistry ; Composite material ; Catalysis Analytical chemistry methods development ; Biosensors and Analytical Detection ; Microfluidic and Capillary Electrophoresis Applications
Authors: Niklas Leimeroth | Linus C. Erhard | Karsten Albe | Jochen Rohrer
Source: Modelling and Simulation in Materials Science and Engineering (article, 2025-07-29)
Field: Materials Science — Materials Chemistry

Machine-learning interatomic potentials from a users perspective: A comparison of accuracy, speed and data efficiency Abstract Machine learning interatomic potentials (MLIPs) have massively changed the field of atomistic modeling. They enable the accuracy of density functional theory in large-scale simulations while being nearly as fast as classical interatomic potentials. Over the last few years, a wide range of different types of MLIPs have been developed, but it is often difficult to judge which approach is the best for a given problem setting. For the case of structurally and chemically complex solids, namely Al-Cu-Zr and Si-O, we benchmark a range of machine learning interatomic potential approaches, in particular, the Gaussian approximation potential (GAP), high-dimensional neural network potentials (HDNNP), moment tensor potentials (MTP), the atomic cluster expansion (ACE) in its linear and nonlinear version, neural equivariant interatomic potentials (NequIP), Allegro, and MACE. We find that nonlinear ACE and the equivariant, message-passing&amp;#xD;graph neural networks NequIP and MACE form the Pareto front in the accuracy vs. computational cost trade-off. In case of the Al-Cu-Zr system we find that MACE and Allegro offer the highest accuracy, while NequIP outperforms them for Si-O. Furthermore, GPUs can massively accelerate the MLIPs, bringing them on par with and even ahead of non-accelerated classical interatomic potentials (IPs) with regards to accessible timescales. Finally, we explore the extrapolation behavior of the corresponding potentials, probe the smoothness of the potential energy surfaces, and finally estimate the user friendliness of the corresponding fitting codes and molecular dynamics interfaces. Massively parallel ; Computer science ; Extrapolation ; Artificial neural network ; Interatomic potential ; Statistical physics ; Materials science ; Algorithm ; Machine learning ; Molecular dynamics ; Physics ; Quantum mechanics ; Mathematics ; Parallel computing ; Mathematical analysis Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Zhengwu Long | Lingyun You
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

New chemical average model for molecular simulations of the asphalt system This study refined the Brown-Ladner (B-L) method and combined it with elemental analysis to construct average molecular structure models of virgin asphalt, aiming to achieve more precise molecular simulations of asphalt materials. The chemical structure accuracy of the proposed average molecular models was validated using proton nuclear magnetic resonance (<sup>1</sup>H NMR) and Fourier transform infrared spectroscopy (FTIR). Furthermore, the electronic structural properties of these models were calculated <i>via</i> density functional theory (DFT) to predict potential reaction sites. Additionally, weak interaction mechanisms within the average molecular models of virgin asphalt were revealed by combining the aRDG approach with molecular dynamics (MD) simulations, and their stability was analyzed through the thermal fluctuation index (TFI). The effectiveness of the improved average molecular models of virgin asphalt was verified from multiple dimensions including thermodynamics, structural properties, transport characteristics, and interfacial adsorption behavior based on MD simulation. Notably, the LYM2 model accurately reflects hydrogen distribution and aromatic-to-aliphatic hydrogen ratios in real samples, identifies reactive sites, and predicts material properties like density, solubility parameters, glass transition temperature, and viscosity effectively, especially excelling in viscosity predictions based on the Rouse model. Moreover, significant π-π stacking effects and steric hindrance effects among average molecules of virgin asphalt were found, which show high kinetic stability due to the negligible influence from thermal motion. The new model of asphalt proposed in this study provides more efficient and accurate molecular structures for <i>ab initio</i> molecular dynamics (AIMD) simulations and machine learning force field training of asphalt materials for future research. Asphalt ; Environmental science ; Materials science ; Statistical physics ; Physics ; Composite material Petroleum Processing and Analysis ; Asphalt Pavement Performance Evaluation ; Surfactants and Colloidal Systems
Authors: Yong Huang | Haoran Wang | Huasheng Huang | Zhiping Tan | Chaojun Hou | Jiajun Zhuang | Yu Tang
Source: Agriculture (article, 2025-01-17)
Field: Chemistry — Analytical Chemistry

Raman Spectroscopy and Its Application in Fruit Quality Detection Raman spectroscopy is a spectral analysis technique based on molecular vibration. It has gained widespread acceptance as a practical tool for the non-invasive and rapid characterization or identification of multiple analytes and compounds in recent years. In fruit quality detection, Raman spectroscopy is employed to detect organic compounds, such as pigments, phenols, and sugars, as well as to analyze the molecular structures of specific chemical bonds or functional groups, providing valuable insights into fruit disease detection, pesticide residue analysis, and origin identification. Consequently, Raman spectroscopy techniques have demonstrated significant potential in agri-food analysis across various domains. Notably, the frontier of Raman spectroscopy is experiencing a surge in machine learning applications to enhance the resolution and quality of the resulting spectra. This paper reviews the fundamental principles and recent advancements in Raman spectroscopy and explores data processing techniques that use machine learning in Raman spectroscopy, with a focus on its applications in detecting fruit diseases, analyzing pesticide residues, and identifying origins. Finally, it highlights the challenges and future prospects of Raman spectroscopy, offering an effective reference for fruit quality detection. Raman spectroscopy ; Quality (philosophy) ; Spectroscopy ; Analytical Chemistry (journal) ; Materials science ; Chemistry ; Botany ; Biology ; Physics ; Optics ; Environmental chemistry ; Astronomy Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Hirpha Adugna Areti | Abas Siraj Hamda | Lata Deso Abo | Abdisa Jabesa | Melkiyas Diriba Muleta
Source: Environmental Advances (article, 2025-01-10)
Field: Chemistry — Organic Chemistry

Biowastes as sustainable catalysts for water treatment: A comprehensive overview • Biowaste catalysts offer sustainable solutions for water purification • Synthesis methods like pyrolysis and hydrothermal carbonization affect efficiency • Contaminants such as heavy metals, pollutants, and emerging threats are removed • Challenges include stability, reusability, and scalability of bio-waste catalysts • Economic and environmental benefits emphasize cost-effectiveness and sustainability Water purification remains a critical global challenge that requires innovative and sustainable solutions. This review highlights the transformative potential of catalysts derived from biowaste for water treatment, offering eco-friendly and cost-effective alternatives to traditional methods. Agricultural residues, food waste, and biomass are converted into efficient catalysts through processes such as pyrolysis, hydrothermal carbonization, and chemical activation. These catalysts enable the removal of heavy metals, organic pollutants, and emerging contaminants through adsorption and oxidation mechanisms. They address both waste management and water treatment needs, aligning with circular economy principles by promoting resource recovery and sustainability. Integrating advanced technologies, such as membrane filtration and advanced oxidation processes, further enhances the efficacy of these catalysts. Additionally, artificial intelligence and machine learning provide tailored solutions for optimizing catalyst design and enabling real-time monitoring. Despite challenges related to scalability, stability, and reusability, strategies such as functionalization, hybridization, and the incorporation of computational tools offer pathways for improvement. This comprehensive review underscores the economic and environmental benefits of biowaste-based catalysts while identifying future research directions to advance this promising technology. It emphasizes the importance of green innovation in sustainable water purification. Environmental science ; Catalysis ; Environmental planning ; Business ; Chemistry ; Organic chemistry Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal ; Advanced oxidation water treatment
Authors: Ye min Thant | Taishiro Wakamiya | Methawee Nukunudompanich | Keisuke Kameda | Manabu Ihara | Sergei Manzhos
Source: Chemical Physics Reviews (article, 2025-02-13)
Field: Materials Science — Materials Chemistry

Kernel regression methods for prediction of materials properties: Recent developments Machine learning (ML) is increasingly used in chemical physics and materials science. One major area of thrust is machine learning of properties of molecules and solid materials from descriptors of chemical composition and structure. Recently, kernel regression methods of various flavors—such as kernel ridge regression, Gaussian process regression, and support vector machine—have attracted attention in such applications. Kernel methods allow benefiting simultaneously from the advantages of linear regressions and the superior expressive power of nonlinear kernels. In many applications, kernel methods are used in high-dimensional feature spaces, where sampling with training data is bound to be sparse and where effects specific to high-dimensional spaces significantly affect the performance of the method. We review recent applications of kernel-based methods for the prediction of properties of molecules and materials from descriptors of chemical composition and structure and related purposes. We discuss methodological aspects including choices of kernels appropriate for different applications, effects of dimensionality, and ways to balance expressive power and reliability of the model in high-dimensional feature spaces and with sparse data. We also discuss kernel regression-based hybrid ML approaches. Kernel (algebra) ; Regression ; Computer science ; Artificial intelligence ; Machine learning ; Statistics ; Econometrics ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Titanium Alloys Microstructure and Properties
Authors: Rutu Patel | Mayankkumar L. Chaudhary | Alessandro F. Martins | Ram K. Gupta
Source: Industrial & Engineering Chemistry Research (article, 2025-04-25)
Field: Materials Science — Materials Chemistry

Mastering Material Insights: Advanced Characterization Techniques The function of advanced characterization methods in materials science is discussed in this review article, which primarily focuses on how these methods are used in structural composites, energy storage materials, and semiconductors. Discovering the intricate chemical and structural features of these materials has been greatly aided by the combination of methods like scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR). Understanding materials’ thermal stability and behavior under operating settings is essential for maximizing performance. Technology such as thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) have further contributed to this understanding. A key area where in situ and operando characterization techniques are becoming increasingly important is the study of energy storage devices. Researchers can gain invaluable insights into degradation mechanisms and failure points by monitoring real-time material changes under working conditions. The interaction of a material’s thermal, chemical, and structural characteristics can be better understood when spectroscopy and microscopy are used together. This study focuses on a significant material characterization trend involving combining high-throughput screening with machine learning (ML) and artificial intelligence (AI). AI can shorten the time it takes from ideation to implementation by improving the accuracy and speed of material discovery using massive data sets produced by sophisticated methods. Researchers can better anticipate material behavior using this data-driven method, particularly in operational or severe situations that are hard to replicate experimentally. Researchers in the field of materials science have concluded that improving characterization methods is crucial to the field’s future progress. Critical will be multidimensional and hybrid approaches that integrate several types of analysis. In addition, studying and forecasting the behavior of next-gen materials, especially those used in energy storage, semiconductor technology, and nanocomposites, will rely heavily on AI-driven research. New opportunities for creating sustainable, high-functioning materials will arise because of these advancements. Characterization (materials science) ; Computer science ; Process engineering ; Materials science ; Nanotechnology ; Engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Ion-surface interactions and analysis
Authors: Eslam M. Hamed | Mostafa M. Elsaady | Sam Fong Yau Li
Source: Analytical Chemistry (review, 2025-10-17)
Field: Materials Science — Materials Chemistry

Single-Atom Nanozymes in Analytical Chemistry: Opportunities and Challenges Single-atom nanozymes (SANs) represent a groundbreaking innovation in analytical chemistry, offering a new paradigm for molecular detection and environmental sensing. Due to their unique atomic-level dispersion, SANs exhibit superior catalytic activity and specificity, making them ideal candidates for a wide range of applications, including disease biomarker detection and environmental monitoring. This article explores the potential of SANs in molecular diagnostics, highlighting their use in biosensors for ultrasensitive detection of proteins, nucleic acids, and metabolites. Their integration into fluorescence-based and electrochemical sensors has enabled rapid, accurate, and label-free detection, crucial for point-of-care testing. SANs also show promise in environmental monitoring, where they have been utilized for detecting pollutants such as heavy metals, pesticides, and CO<sub>2</sub>. Additionally, their role in miniaturized analytical devices, such as lab-on-a-chip systems, showcases their compatibility with portable diagnostic tools, ensuring quick and reliable analysis in real-world settings. Still, challenges such as fabrication complexity, stability under various environmental conditions, and high material costs remain barriers to widespread adoption. The article also discusses the integration of SANs with advanced data analytics, such as machine learning and artificial intelligence, to enhance sensor performance. Despite these challenges, the transformative potential of SANs in analytical chemistry promises significant advances in both molecular detection and environmental sensing. Transformative learning ; Environmental analysis ; Molecular diagnostics ; Compatibility (geochemistry) Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Electrochemical sensors and biosensors
Authors: Yonghyuk Lee | Xiaobo Chen | Sabrina M. Gericke | Meng Li | Dmitri N. Zakharov | Ashley R. Head | Judith Yang | Anastassia N. Alexandrova
Source: Angewandte Chemie International Edition (article, 2025-04-22)
Field: Materials Science — Materials Chemistry

Machine‐Learning‐Driven Exploration of Surface Reconstructions of Reduced Rutile TiO <sub>2</sub> Titanium dioxide (TiO<sub>2</sub>) is widely used as a catalyst support due to its stability, tunable electronic properties, and surface oxygen vacancies, which are crucial for catalytic processes such as the reverse water-gas shift (RWGS) reaction. Reduced TiO<sub>2</sub> surfaces undergo complex surface reconstructions that endow unique properties but are computationally challenging to describe. In this study, we utilize machine-learning interatomic potentials (MLIPs) integrated with an active-learning workflow to efficiently explore reduced rutile TiO<sub>2</sub> surfaces. This approach enabled the prediction of a phase diagram as a function of oxygen chemical potential, revealing a variety of reconstructed phases, including a previously unreported subsurface shear plane structure. We further investigate the electronic properties of these surfaces and validate our results by comparing experimental and theoretical high-resolution transmission electron microscopy (HRTEM). Our findings provide new insights into how extreme surface reductions influence the structural and electronic properties of TiO<sub>2</sub>, with potential implications for catalyst design. Rutile ; High-resolution transmission electron microscopy ; Materials science ; Anatase ; Transmission electron microscopy ; Nanotechnology ; Titanium dioxide ; Phase diagram ; Surface reconstruction ; Catalysis ; Surface (topology) ; Chemical physics ; Chemical engineering ; Phase (matter) ; Chemistry ; Composite material ; Photocatalysis ; Geometry ; Engineering ; Mathematics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides