jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fatma A. A. Al Ani | Azhar S. Hamody | Ruaa M. Mahmood | Nahla A. Alassaf | Dhafir T.A. Al-Heetimi
Source: Studia Universitatis Babeș-Bolyai Chemia (article, 2025-03-20)
Field: Chemistry — Analytical Chemistry

GREEN SPECTROPHOTOMETRIC METHOD FOR CONCURRENT ESTIMATION OF PIROXICAM AND MEFENAMIC ACID MIXTURE The purpose of this work is to concurrently estimate the UV-visible spectra of binary combinations of piroxicam and mefenamic acid using the chemometric approach. To create the model, spectral data from 73 samples (with wavelengths between 200 and 400 nm) were employed. A two-layer artificial neural network model was created, with two neurons in the output layer and fourteen neurons in the hidden layer. The model was trained to simulate the concentrations and spectra of piroxicam and mefenamic acid. For piroxicam and mefenamic acid, respectively, the Levenberg-Marquardt algorithm with feed-forward back-propagation learning produced root mean square errors of prediction of 0.1679 μg/mL and 0.1154 μg/mL, with coefficients of determination of 0.99730 and 0.99942, respectively. The suggested approach’s ease of use, affordability, and environmental friendliness make it a suitable replacement for the use of hazardous chemicals in the routine investigation of the selected drugs. Mefenamic acid ; Piroxicam ; Chemistry ; Chromatography ; Estimation ; Mathematics ; Medicine ; Economics Analytical Methods in Pharmaceuticals
Authors: Sehyeok Park | Takuji Oda
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Efficient calculations of impurity diffusivity in metals by linearized multi-band embedded atom method potentials Impurity diffusivity in metals has attracted much attention in materials science and engineering due to its significant relevance to the performance and integrity of materials and components. In computational simulations of impurity diffusivity, although molecular dynamics (MD) simulations using empirical potentials such as embedded atom method potentials (EAMs) have been widely used, the accuracy is often insufficient, especially when it comes to details such as isotope effects. On the other hand, while machine learning (ML) potentials trained on first-principles calculations can achieve high accuracy, their computational speed poses a challenge for statistically precise calculations of diffusion coefficients. In this study, we propose an extended version of EAM, called linearized multi-band EAM (LMB-EAM), which can be constructed by a force-matching method using first-principles calculation data as a training set with regularization, and validate its performance for impurity diffusivity in metals. For two test cases, H diffusion in bcc-W and O diffusion in liquid Na, we demonstrate that LMB-EAMs can be constructed with a small number of training data, outperform empirical potentials, and determine the diffusion coefficient including the isotope effect with reasonable accuracy, better than tested empirical potentials and slightly inferior to a tested ML potential. We also investigate which properties are important for accurate simulations of impurity diffusivity in solid metals, providing guidance for the construction of potential models. Thermal diffusivity ; Impurity ; Atom (system on chip) ; Diffusion ; Thermodynamics ; Chemistry ; Molecular dynamics ; Statistical physics ; Materials science ; Computational physics ; Computational chemistry ; Computer science ; Physics Machine Learning in Materials Science ; Nuclear Materials and Properties ; Microstructure and mechanical properties
Authors: Zhongzhou Zhao | Bin Zuo | Bingbing Shen | Feifei Chu | Shaoqing Liu | Pengde Li | Guoze Yan | Likun Pan | Xingtao Xu
Source: Coordination Chemistry Reviews (article, 2025-12-30)
Field: Chemistry — Inorganic Chemistry

Data-driven photocatalytic uranium extraction: machine learning insights into structure–activity relationships of metal–organic and covalent organic frameworks Uranium ; Photocatalysis ; Scalability ; Bridging (networking) ; Random forest ; Covalent bond Radioactive element chemistry and processing ; Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Huu Trong Phan | Pei‐Kang Tsou | Hieu C. Dong | Po‐Jen Hsu | Jer‐Lai Kuo
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Chemistry — Spectroscopy

Unraveling the low-energy conformers of neutral and charged mono- and di-saccharides with first-principles accuracy assisted by neural network potentials We present an efficient structure sampling algorithm that combines neural network potentials (NNPs) and a well-sampled local minima dataset to explore the conformational space of mono-saccharides and di-saccharides. The structure sampling methodology leverages a "pattern transfer" approach, in which molecular initial guesses are created by utilizing existing local minima from a structurally relevant molecular system as a template. The NNP models are integrated into the structure sampling scheme to efficiently identify low-energy conformer candidates. Vibrational spectra simulated from these identified structures show qualitative alignment with experimental infrared spectra. This study demonstrates the potential of combining NNP models in an efficient structure sampling scheme to investigate flexible molecular systems, advancing understanding of carbohydrate structure-property relationships. Maxima and minima ; Conformational isomerism ; Artificial neural network ; Neutral network ; Sampling (signal processing) ; Energy (signal processing) ; Potential energy ; Chemistry ; Space (punctuation) ; Computational chemistry ; Biological system ; Computer science ; Artificial intelligence ; Physics ; Mathematics ; Molecule ; Atomic physics ; Statistics ; Computer vision ; Organic chemistry ; Biology Molecular spectroscopy and chirality ; Protein Structure and Dynamics ; Advanced Chemical Physics Studies
Authors: Mamduh J. Aljaafreh | Sajjad Hussain Sumrra | Sadaf Noreen
Source: Materials Today Communications (article, 2025-04-23)
Field: Materials Science — Materials Chemistry

A reverse engineering descriptor tokenization machine learning analysis to design new triphenylamine chromophores with tunable absorption/emission Triphenylamine ; Materials science ; Chromophore ; Absorption (acoustics) ; Optoelectronics ; Photochemistry ; Composite material Luminescence and Fluorescent Materials ; Photoreceptor and optogenetics research ; Organic Electronics and Photovoltaics
Authors: R. A. Oshiya | Arko Mohari | Ayan Datta
Source: Organic Letters (article, 2025-05-03)
Field: Chemistry — Organic Chemistry

Machine Learning Predicts Regioselectivity in Pd-Catalyzed Directing Group-Assisted C–H Activation Regioselectivity in C-H activation is crucial for applications in pharmaceuticals and advanced materials. Machine learning (ML) techniques enhance the speed and accuracy of regioselectivity prediction in palladium-catalyzed directing group-assisted C-H activation in aryl substrates. Among the models tested, the standard support vector machine (SVM) model demonstrated better generalizability, achieving an F1 score of 0.92 and MCC of 0.93 on the test set. This approach aids the development of efficient catalytic strategies for C-H activation. Regioselectivity ; Chemistry ; Catalysis ; Group (periodic table) ; Combinatorial chemistry ; Palladium ; Stereochemistry ; Medicinal chemistry ; Organic chemistry Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Ziyang Huang | Pei Li | Yuxuan Hou | Peili Zhao | Shanshan Wu | Ruilong Huang | Zheng He | Ru Xing | Shuangfeng Jia | Jianbo Wang
Source: Advanced Energy Materials (article, 2025-07-17)
Field: Materials Science — Materials Chemistry

Atomistic Phase Transition Mechanism and Its Relationship with the High Thermoelectric Performance in SnSe Abstract SnSe maintains a high figure of merit (ZT) at elevated temperatures, which is closely linked to its atomic structure. However, the nature of its high‐temperature (HT) phase remains controversial, primarily due to the lack of direct observations for thermal‐induced structural change. Here, the layer‐shuffling phase transition mechanism is characterized at the atomic scale by in situ transmission electron microscopy (TEM), assisted by theoretical calculations and machine learning. Notably, a slightly distorted HT α phase dominates at elevated temperatures, likely induced by the breakdown of long‐range coherence because of the dynamic disorder of Sn atoms. Further investigations into the electrical and thermal transport properties indicate that the HT α phase primarily contributes to the excellent thermoelectric performance of SnSe reported previously. This study provides a fundamental understanding of structure‐thermoelectric property correlations, paving the way for the development of advanced thermoelectric materials. Thermoelectric effect ; Materials science ; Thermoelectric materials ; Phase transition ; Condensed matter physics ; Chemical physics ; Phase (matter) ; Seebeck coefficient ; Figure of merit ; Transmission electron microscopy ; Atomic units ; Nanotechnology ; Nanoscopic scale ; Thermal conductivity ; Optoelectronics ; Thermodynamics ; Composite material ; Chemistry Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Thermal properties of materials
Authors: Sofia Albesa | Ezequiel Giménez | J Piccinini | María Guadalupe Vizoso-Pinto | Waldemar A. Marmisollé | Esteban Piccinini | Omar Azzaroni
Source: ACS Sensors (article, 2025-12-03)
Field: Materials Science — Materials Chemistry

Machine Learning-Augmented Graphene Transistor Biosensing: Quantitative Platform Validation and Immunotesting of Hepatitis E Graphene-based chips face persistent sensor-to-sensor variability due to manufacturing defects and polymer contamination, limiting their analytical reliability for healthcare applications. Here, we demonstrate that the integration of a machine learning (ML) model with graphene field-effect transistors (GFETs) enables quantitative and calibration-free analytical sensing. Using Random Forest Regression and field-effect-related figures of merit, the model enabled robust, quantitative predictions across analytes of varying chemical nature─from small ions to viral antigens. pH sensing was used as a reference system to validate the augmented platform. Compared with the reference analytical model, ML enabled a marked improvement of accuracy, from 93 to 97%, and a reduction of the coefficient of variability, from 14 to 3%. Then, the ML-integrated GFETs were applied to chloride detection, the gold standard for cystic fibrosis diagnosis. Finally, using GFETs functionalized with llama nanobodies, we targeted the ORF2 antigen of the Hepatitis E virus. ML integration significantly enhanced immunoassay sensitivity-specificity from 89-69% to 100-100% and allowed the quantitative prediction of antigen concentration. Furthermore, the ML-augmented test demonstrated a strong performance for HEV antigen detection in capillary blood samples without the need for any sample pretreatment. Analyte ; Graphene ; Reliability (semiconductor) ; Transistor ; Polymer ; Immunoassay ; Detection limit ; Limiting ; Lab-on-a-chip Graphene research and applications ; Nanowire Synthesis and Applications ; Nanopore and Nanochannel Transport Studies
Authors: Xuefei Guo | Yang Zhao | Xianle Rong | Xingyu Chen | Xiao Wang | Tianyi Liu | Yunfei Xiao | Yushu Zou | Tianming Chu | Xiangxi Wang | Qiang Liu | Fuping You
Source: Research Square (preprint, 2025-04-21)
Field: Chemistry — Inorganic Chemistry

Phosphines-Nitrogen-Phosphines chelated CoCl2 is a novel therapeutic agent for pancreatic cancer Pancreatic cancer ; Chelation ; Nitrogen ; Cancer ; Chemistry ; Internal medicine ; Medicine ; Organic chemistry Asymmetric Hydrogenation and Catalysis ; Chemical Reactions and Isotopes
Authors: Qiuyu Rong | Mengjie Hao | Zhongshan Chen | Hui Yang | Geoffrey I. N. Waterhouse | Shengqian Ma | Xiangke Wang
Source: Nanotechnology (article, 2025-06-06)
Field: Materials Science — Materials Chemistry

Advancing porous materials for radioiodine capture Radioactive iodine, released during nuclear accidents and spent fuel treatment, poses significant threats to ecosystems and human health due to its volatility, persistence, and potential for bioaccumulation. Porous materials with tunable structures and tailored adsorption sites have recently emerged as a promising technology for iodine capture. This paper reviews the progress of research on porous materials for iodine capture, focusing on improvements in iodine capture performance as well as adsorbent stability through innovative structure-function strategies. Finally, we propose innovative directions for future research, including the design of multifunctional composite materials, integration of electrochemical and photocatalytic technologies, and leveraging machine learning for material optimization and performance prediction. These approaches offer new pathways to enhance iodine capture efficiency and advance nuclear safety through more effective radionuclide management approaches. Materials science ; Volatility (finance) ; Iodine ; Adsorption ; Porous medium ; Porosity ; Nanotechnology ; Process engineering ; Systems engineering ; Business ; Engineering ; Composite material Covalent Organic Framework Applications ; Radioactive element chemistry and processing ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Sha Huang | Jin Yang | Xiaohong Hou | Zhengwei Huang | Jian Hu | Haojie Yu | Zhengfu He
Source: ACS Omega (article, 2025-09-11)
Field: Materials Science — Materials Chemistry

Mapping the Evolution of Artificial Intelligence in Medical Materials The application of artificial intelligence (AI) in medical materials has been extensively explored, encompassing areas such as composition optimization, shape design, and manufacturing processes. However, a comprehensive study and analysis of the overall research landscape in this field remain scarce. To bridge this gap, this study employs bibliometric analysis to evaluate 108 publications from the Web of Science Core Collection database. The results reveal that research in this domain spans nearly two decades, with a notable surge in publications since 2018, probably driven by advancements in deep learning. Additionally, the study identifies significant challenges in AI for medical materials, such as clinical validation, de novo design, and precise manufacturing. These insights may provide guidance for future research in AI-enabled medical materials development. Field (mathematics) ; Bridge (graph theory) ; Domain (mathematical analysis) ; Applications of artificial intelligence ; Medical research ; Medical science Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Manufacturing Process and Optimization
Authors: M. Lakshmi Varshika | Jonathan Hollenbach | Nícolas Bohm Agostini | Ankur Limaye | Antonino Tumeo | Anup Das
Source:  (article, 2025-03-31)
Field: Materials Science — Materials Chemistry

Online Learning for Dynamic Structural Characterization in Electron Energy Loss Spectroscopy In-situ Electron Energy Loss Spectroscopy (EELS) is a crucial technique for determining the elemental composition of materials through EELS Spectrum Images (EELS-SI). While recent innovations have made it possible for EELS-SI data acquisition at rates of 400 frames per second with near-zero read noise, the challenge lies in processing this massive stream of real-time data to capture nanoscale dynamic changes. This task demands advanced machine learning methods capable of identifying subtle and complex features in EELS spectra. Furthermore, the EELS data acquired in difficult experimental conditions often suffer from a low signal-to-noise ratio (SNR), leading to unreliable classification and limiting their utility. In response to this critical need, we introduce a spiking neural network (SNN)-based Variational Autoencoder (VAE) that embeds spectral data into a latent space, facilitating precise prediction of structural changes. VAEs are designed to learn efficient low-dimensional representations while capturing the inherent variability in the data, making them highly effective for processing multidimensional data. Additionally, SNNs, which use biological neurons, offer unmatched scalability and energy efficiency by processing information through binary spikes, making them ideal for high-throughput data. We validate our framework using MXene annealing data, achieving denoised spectrum images with an SNR of 28.3dB. For the first time, we present a fully online learning solution for dynamic structural tracking, implemented directly in hardware, eliminating the traditional bottleneck of offline training. Our method achieves reliable, real-time, on-device characterization of high-speed EELS data when evaluated on an FPGA platform. Joint experiments with the SNN-VAE model on both spiking autoencoder hardware and a softwaretrained hybrid configuration of hardware spiking encoders demonstrated latency reductions of 25.2x, 93.7x, and 1.04x, 4.5x in energy savings, respectively, compared to baseline. Characterization (materials science) ; Electron energy loss spectroscopy ; Spectroscopy ; Electron ; Computer science ; Materials science ; Energy (signal processing) ; Nanotechnology ; Physics ; Nuclear physics Machine Learning in Materials Science
Authors: Juanshu Wu | Yingming Pu | Jin Wang | Bing Gu | Xin Chen | Hongyu Chen
Source: Advanced Optical Materials (article, 2025-02-21)
Field: Materials Science — Biomaterials

Machine Learned Structure‐Property Correlation Between Nanohelices and Circular Dichroism Abstract Rational design of chiral nanostructures with desired Circular Dichroism (CD) spectra requires a quantitative structure‐property relationship, which has so far been unavailable. Using a data‐driven method, the aim is to establish such a relationship for nanohelices, a prevalent structural element of chiral nanostructures. Given the challenges in synthesizing nanohelices and separating racemic mixtures, obtaining extensive CD data has been difficult. Instead, CD spectra of 1260 nanohelices are stimulated using finite‐difference time‐domain method. This dataset is used to train a convolutional neural network that can accurately predict the CD spectra using a few key structural parameters such as pitch and curl. Moreover, an inverse design model is developed that can generate the right helix with the desired CD. To establish quantitative relationships, Shapley Additive explanations analysis and case studies are devised for the prediction model. The algorithm efficiently analyzes the structure‐property correlation, revealing the specific degrees of structural influence on the spectroscopic characteristics. Furthermore, the neural‐network‐based model can be extended via transfer learning to predict CD spectra of nanohelices made of other noble metals (Ag, Cu). It is believed that AI‐based approaches can significantly broaden the scope of wet‐chemistry nanosynthesis and computational techniques in the design of chiral nanostructures. Materials science ; Circular dichroism ; Property (philosophy) ; Nanotechnology ; Crystallography Supramolecular Self-Assembly in Materials ; Nanocluster Synthesis and Applications ; Graphene research and applications
Authors: Sudeshna Mondal | Aritra Roy | Rene Pfeifer | Felipe Fantuzzi | Amitava Choudhury | K. Mukherjee
Source: ACS Applied Nano Materials (article, 2025-10-10)
Field: Chemistry — Electrochemistry

Pristine Nanostructured α-Ni(OH) <sub>2</sub> as a Nonenzymatic Electrochemical Strip Sensor for Trace Detection of Phenolic Compounds Developing electrochemical sensors capable of detecting multiple analytes at distinct potentials is vital for applications in environmental, biomedical, and quality monitoring. Here, we explore nanostructured, nonenzymatic α-Ni(OH)2 as a versatile sensing material for the selective detection of phenol, catechol, and p-nitrophenol using two platforms: a standard three-electrode system and a portable strip sensor. α-Ni(OH)2 was synthesized via a wet-chemical method and coated onto glassy carbon and screen-printed carbon electrodes for the respective configurations. Electron microscopy confirmed semicrystalline nanoscale morphology (nanoparticulate films), and cyclic voltammetry revealed clear redox signatures for each analyte, enabling selective detection with distinct peak positions across both systems. The three-electrode setup reached limits of detection of 0.003 μM (phenol), 0.1 μM (catechol), and 1 μM (p-nitrophenol), whereas the portable sensor achieved 0.3, 1, and 2 μM, respectively. Amperometric measurements confirmed sensor performance at target potentials. Additionally, machine learning algorithms were applied to model signal behavior and support analyte classification. This combined approach demonstrates a robust strategy for sensitive, selective, and portable detection of multiple phenolic compounds. Amperometry ; Analyte ; Cyclic voltammetry ; Electrode ; Electrochemistry ; Detection limit ; Electrochemical gas sensor ; Glassy carbon Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Analytical Chemistry and Sensors
Authors: Dennis Possart | Leonid Mill | Florian Vollnhals | Tor Hildebrand | Peter M. Suter | Mathis Hoffmann | Jonas Utz | Daniel Augsburger | Mareike Thies | Mingxuan Wu | Fabian Wagner | George Sarau | Silke Christiansen | Katharina Breininger
Source: npj Computational Materials (preprint, 2025-07-01)
Field: Materials Science — Materials Chemistry

Addressing data scarcity in nanomaterial segmentation networks with differentiable rendering and generative modeling Abstract Nanomaterials’ properties, influenced by size, shape, and surface characteristics, are crucial for their technological, biological, and environmental applications. Accurate quantification of these materials is essential for advancing research. Deep learning segmentation networks offer precise, automated analysis, but their effectiveness depends on representative annotated datasets, which are difficult to obtain due to the high cost and manual effort required for imaging and annotation. To address this, we present DiffRenderGAN, a generative model that produces annotated synthetic data by integrating a differentiable renderer into a Generative Adversarial Network (GAN) framework. DiffRenderGAN optimizes rendering parameters to produce realistic, annotated images from non-annotated real microscopy images, reducing manual effort and improving segmentation performance compared to existing methods. Tested on ion and electron microscopy datasets, including titanium dioxide (TiO 2 ), silicon dioxide (SiO 2 ), and silver nanowires (AgNW), DiffRenderGAN bridges the gap between synthetic and real data, advancing the quantification and understanding of complex nanomaterial systems. Rendering (computer graphics) ; Scarcity ; Generative grammar ; Segmentation ; Computer science ; Differentiable function ; Artificial intelligence ; Deep learning ; Machine learning ; Data science ; Economics ; Mathematics ; Microeconomics Machine Learning in Materials Science ; Mineral Processing and Grinding