jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anamika Dey | Nagarajan Janaki Sankarachari Krishnan | Shenthilnathan Sivakumar | S. Ravichandran | Muruganantham Bharathi | P. Santhana Krishnan
Source: Journal of Young Pharmacists (article, 2025-11-13)
Field: Chemistry — Analytical Chemistry

Advances in Green Analytical Techniques for Impurity Profiling in Pharmaceuticals This study provides an overview of the green analytical methods in pharmaceutical impurity profiling with focus on regulatory compliance, effectiveness, and environmental impact. We address recent trends in spectroscopic approaches such as Near-Infrared (NIR), Raman spectroscopy, Capillary Electrophoresis (CE), Green Liquid Chromatography (GLC) and Supercritical Fluid Chromatography (SFC). Analyses of the principles of green chemistry are done through avenues including energy efficiency, minimization of wastes generated, as well as the use of safer solvents. The review is based on state-of-the-art sample preparation strategies: miniaturized procedures and SPME. We also address the drawbacks, that is, sensitivity problems and legal restrictions, that halt the wider spread of these techniques. The future perspectives are related to the application of machine learning and artificial intelligence in technique development. This article aims at providing quality control specialists and pharmaceutical researchers with an overview of the latest green analytical techniques, their applications, pitfalls, and promises towards making impurity profiling more sustainable. Using case studies and comparative analysis, we demonstrate how such approaches can be useful in pharmaceutical quality assurance and control. Profiling (computer programming) ; Capillary electrophoresis ; Analytical technique ; SAFER ; Quality assurance ; Quality by Design ; Human use Spectroscopy and Chemometric Analyses ; Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Chromatography
Authors: Huy Manh Nguyen | Roxana Sühring | Carolin Marx | You Liang | Courtney D. Sandau | Gretchen Sullivan
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Spectroscopy

An Open-Access Computational Fingerprinting Workflow for Source Classifications of Neat Gasoline Using Gcxgc-Tofms and Machine Learning Gasoline ; Workflow ; Computer science ; Chromatography ; Chemistry ; Database ; Organic chemistry Mass Spectrometry Techniques and Applications
Authors: Bo Jiang | Xu Yin | Feng Cheng | Zhengwei Wang | Lili Zhao | Rong Zhang | Liang Zhao | Meina Liu
Source: Journal of Applied Polymer Science (article, 2025-03-03)
Field: Materials Science — Polymers and Plastics

Artificial Neural Network Prediction of <scp>pH</scp> in Nylon 66 Salt Solutions: A Machine Learning Approach ABSTRACT This study investigates the application of artificial neural networks (ANNs) to predict the pH of Nylon 66 salt solutions. The ANN was optimized by systematically evaluating key parameters—including the number of hidden layers and neurons, activation function type, and optimization algorithm—using a dataset of pH values measured across varying temperatures and concentrations. The results showed that when the number of hidden layers was 24, and the number of neurons in each hidden layer was 253, the mean squared error (MSE) between the predicted pH by ANN and the experimental data reached 10 −4 . The ReLU activation function and the lbfgs optimization algorithm were identified as the most effective for the prediction task. The optimized ANN demonstrated superior predictive accuracy with a determination coefficient ( R 2 ) exceeding 0.99, outperforming the traditional first‐order ionization theory. This research provides a robust method for controlling the Nylon 66 synthesis process and highlights the potential of ANN in complex chemical systems. Artificial neural network ; Salt (chemistry) ; Chemistry ; Computer science ; Artificial intelligence ; Organic chemistry Polymer Nanocomposites and Properties ; Hydrogels: synthesis, properties, applications ; Analytical Chemistry and Chromatography
Authors: Ming Huang | Xiong Jingxian | Peng Yongqian | Haijun Mao | Zhuofeng Liu | Wei Li | Fenglin Wang | Weijun Zhang | Chen Xingyu
Source: Materials Today Physics (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Thermal expansion prediction in oxide glasses via graph neural networks with temperature-encoded virtual nodes Artificial neural network ; Generalization ; Oxide ; Graph ; Visualization ; Thermal ; Thermal expansion Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity ; Catalysis and Oxidation Reactions
Authors: Jinglei Wang | Shuang Liu | Yangyaqi Mi | Xiao Wang | Hongjing Dong
Source: Analytical Methods (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Discrimination of different stir-frying degrees of Hordei Fructus Germinatus based on GC-IMS and machine learning Hordei Fructus Germinatus (MY) is a functional food primarily existing in two forms, namely sheng MY and chao MY, after the stir-frying process. At present, the identification of the stir-frying degree of MY predominantly relies on the subjective judgement of pharmaceutical scientists, which lacks clear, quantifiable indicators. In this study, Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) was employed to identify the volatile organic components (VOCs) in MY with different stir-frying times and a total of 54 VOCs were identified. Subsequently, a total of eight featured VOCs were screened, including (<i>E</i>)-2-hexanal, (<i>Z</i>)-3-hexenyl propionate, (2S-<i>trans</i>)-cyclohexanone, (<i>E</i>)-2-pentenal, ethyl 2-methylbutanoate, 3-methylpentanoic acid, ethyl formate, and methyl acetate. These changes in featured VOC content were attributed to chemical reactions, including the oxidation reaction, degradation reaction, and Maillard reaction. Finally, based on the featured VOCs, seven machine learning models were established, and their performance was comparatively studied. The results show that the three models, SVM-L, SVM-P and NB, could accurately discriminate MY with different stir-frying times, and the accuracy of all three models was above 96% in both training and testing sets. Overall, this study presents a rapid, low-cost, and accurate method for discriminating MY samples with different stir-frying degrees, providing a theoretical foundation for the quality control of stir-frying of MY processes. Maillard reaction ; Pattern recognition (psychology) ; Judgement ; Identification (biology) ; Training set Edible Oils Quality and Analysis ; Fermentation and Sensory Analysis ; Advanced Chemical Sensor Technologies
Authors: Keisuke Mukai | Taehyun Hwang | Minoru Kusaba | Jae-Hwan Kim
Source: Materials & Design (article, 2025-04-19)
Field: Materials Science — Materials Chemistry

Accident-tolerant hybrid ceramics for fusion breeding blanket • Li–Be–X–O is synthesized based on the prediction by the machine learning model. • Steam exposure tests at 1200 °C verified the accident tolerance of hybrid ceramics. • Two-phase hybrid ceramics can breed sufficient fuel tritium without metallic Be. • Hybrid ceramics allows a novel blanket design with enhanced safety margins. In a water-cooled ceramic breeding blanket for fusion reactors, hydrogen gas generation by steam oxidation of metallic Be compounds (i.e. neutron multiplier) in a loss-of-coolant accident (LOCA) raises major safety concerns. Li–Be hybrid ceramics has a potential to reduce hydrogen generation significantly, however, the stable compositions and structures for quaternary compositions have not been comprehensively understood. Herein, we report machine-learning based prediction, synthesis, structure, and properties of chemically stabile two-phase Li–Be–X–O hybrid ceramics. The steam exposure tests demonstrated a negligibly small H 2 generation from the two-phase powder of Li 2 BeSiO 4 and 5 at.% BeO below 1200 °C. The stability is explained by the intrinsic ionic/covalent bonding characters and little capacity for further oxidation by steam. Neutronic calculations with simplified one-dimensional model show that the two-phase hybrid cermaics has a sufficient tritium breeding capability without having metallic Be-based multiplier in the blanket. The hybrid ceramics is the first example of multi-functional oxide to breed sufficient fuel tritium with no metallic neutron multiplier, which allows a novel design of ceramic breeding blanket with enhanced safety margins during in-box LOCA. Blanket ; Materials science ; Ceramic ; Nuclear engineering ; Forensic engineering ; Composite material ; Engineering Nuclear Materials and Properties ; Fusion materials and technologies ; Nuclear materials and radiation effects
Authors: Bupathy, Arunkumar | Hall, Darian | Smalyukh, Ivan I. | Campos-Villalobos, Gerardo | Subert, Rodolfo | Dijkstra, Marjolein
Source: arXiv (Cornell University) (preprint, 2025-11-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

From Knots to Crystals: Machine-Learned Potentials for Self-Assembling Topological Solitons in Liquid Crystals Knotted fields in classical and quantum systems were long recognized for their non-trivial topologies and particle-like behavior, but practical applications have been limited by the difficulty of stabilizing them. Recently, stable knotted solitonic textures--heliknotons--have been discovered in chiral liquid crystals, forming adaptive crystal assemblies via elastic distortion-mediated interactions. We use machine learning to develop single-site coarse-grained potentials that accurately capture these chiral anisotropic interactions, enabling large-scale simulations beyond the reach of fine-grained methods. Our machine-learned potentials reproduce the experimentally observed assemblies and provide an efficient framework for modeling a wide range of topological textures. Liquid crystal ; Topology (electrical circuits) ; Network topology ; Topological defect ; Quantum ; Anisotropy ; Range (aeronautics) Liquid Crystal Research Advancements ; Topological Materials and Phenomena ; Material Dynamics and Properties
Authors: Ahmet Atasoy | Mehmed Özkan
Source: IEEE Access (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Force Control of SMA Springs Using RNNs Shape memory alloys (SMAs), generally composed of nickel-titanium, have unique properties that enable them to transition from the martensite phase to the austenite phase when heated. During this transformation, SMAs can shorten and revert to their original shape, which makes them valuable as actuators. SMAs’ biocompatibility, high power-to-weight ratio, low noise, and ability to generate substantial force position them as essential materials in robotic and biomedical applications. However, their control is complicated by inherent nonlinearity and hysteresis. When cooled in the austenite phase, SMAs elongate and deform, with different phase transformation temperatures observed during heating and cooling due to hysteresis. These complexities highlight the need for advanced control techniques. Artificial intelligence models, particularly recurrent neural networks (RNNs), are well-suited for capturing the intricate relationships within SMA behavior. This study introduces an approach that utilizes RNNs and real-time measurements for force control in SMAs. A compact test setup equipped with sensors collects data on voltage, current, and generated force, enabling the system’s dynamic behavior analysis. RNNs effectively model the nonlinear characteristics of SMA actuators, leveraging their capacity to learn complex temporal relationships from the collected data. This innovative approach aims to enhance the control of SMAs and expand their applications in various fields. SMA* ; Computer science ; Algorithm Shape Memory Alloy Transformations ; Titanium Alloys Microstructure and Properties
Authors: Jingyuan Yan | Tatsuya Yokoi | Yuuki Nakano | S. Kondo | Bin Feng | Naoya Shibata | Katsuyuki Matsunaga | Yuichi Ikuhara
Source: Advanced Science (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Atomic‐Scale Insights into Yttrium‐Induced Grain Boundary Structure Modification in Al <sub>2</sub> O <sub>3</sub> Impurity segregation at grain boundaries (GBs) often induces structural transformations at the atomic level and significantly influences materials' properties, underscoring the importance of understanding the underlying mechanisms of GB segregation at the atomic scale. Here, the atomic structure of a Y-segregated ∑13(10 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mover><mml:mn>1</mml:mn> <mml:mo>¯</mml:mo></mml:mover> <mml:annotation>$\bar 1$</mml:annotation></mml:semantics> </mml:math> 4)/[ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mover><mml:mn>1</mml:mn> <mml:mo>¯</mml:mo></mml:mover> <mml:annotation>$\bar 1$</mml:annotation></mml:semantics> </mml:math> 2 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mover><mml:mn>1</mml:mn> <mml:mo>¯</mml:mo></mml:mover> <mml:annotation>$\bar 1$</mml:annotation></mml:semantics> </mml:math> 0] GB is thoroughly studied in α-Al<sub>2</sub>O<sub>3</sub> via scanning transmission electron microscopy and Monte Carlo and molecular dynamics simulations based on a neural network potential. It is found that the Y segregation at the GB involves not only the simple substitution of Y for Al atoms but also a structural adaptation with a change in GB atomic density. Such a change alters the local bonding environment at the GB so that the absolute excess volume is minimized for the lowest-energy structure. This study offers a new insight into the atomic-scale mechanism of GB structural transformation. Grain boundary ; Impurity ; Molecular dynamics ; Transmission electron microscopy ; Scanning transmission electron microscopy ; Monte Carlo method ; Dislocation ; Atomic force microscopy Fusion materials and technologies ; Microstructure and mechanical properties ; Nuclear Materials and Properties
Authors: Vipin
Kurian Elavunkel (22430396) | Prahallad Padhan (2360749)
Source: Figshare (article, 2025-10-14)
Field: Materials Science — Materials Chemistry

Unlocking ThermoelectricPotential: A Machine LearningStacking Approach for Half-Heusler Alloys The thermoelectric properties of half-Heusler (HH) alloys are predicted using an ensemble modeling approach, specifically a stacking model that integrates random forest and XGBoost algorithms. Leveraging a diverse data set encompassing thermal conductivity (κ), electrical conductivity (σ), Seebeck coefficient (<i>S</i>), and the figure of merit (ZT), the study demonstrates superior accuracy with <i>R</i><sup>2</sup> scores of 0.93 for κ, 0.96 for σ, 0.99 for <i>S</i>, and 0.92 for ZT. Feature importance analysis revealed that temperature, mean covalent radius, and atomic volume deviations critically influence thermoelectric behavior. The stacking model demonstrates a clear advantage by substantially reducing both the mean absolute error and root-mean-square error across all predicted thermoelectric properties, indicating enhanced prediction accuracy and reliability compared to individual base models. The model was validated against first-principles density functional theory calculations for representative compounds ErNiBi, YPtBi, and YPdSb, showing strong agreement and capturing real-world experimental effects such as nonmonotonic variations and peak values prior to high-temperature declinephenomena that simplified density functional theory struggles to encapsulate. This comprehensive approach highlights the effectiveness of combining machine learning with physical descriptors to predict complex thermoelectric properties, offering valuable insights for the strategic design of high-performance HH thermoelectric materials. The ensemble modeling framework, coupled with thoughtful feature selection and meticulous engineering, establishes a robust foundation for future research aimed at developing high-performance thermoelectric materials. Thermoelectric effect ; Stacking ; Thermal conductivity ; Thermoelectric materials ; Random forest ; Seebeck coefficient ; Ensemble learning Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties ; Machine Learning in Materials Science
Authors: Xiangyang Deng (7915031) | Huan Ma (713125) | Jinjia Liu (2819824) | Wenping Guo (1716427) | Xiaodong Wen (1594522)
Source: Figshare (article, 2025-12-03)
Field: Chemistry — Inorganic Chemistry

A Benchmark Frameworkfor the Fast and Reliable Evaluationof Zeolite Surface Stability Accurately identifying the external surface structure and crystal morphology of zeolites is essential for establishing structure–performance relationships. Surface free energy serves as a quantitative descriptor of surface stability, which is a prerequisite for theoretical investigations of surface properties. However, a standard computational protocol that effectively balances accuracy and efficiency has yet to be established. To address this, we propose an efficient computational workflow developed through systematic benchmarking. This workflow integrates two key components: an analytical model developed using the SISSO machine learning approach to replace expensive frequency calculations and a configuration screening process based on machine learning potentials to account for the vast number of possible surface terminations. Using this workflow, we efficiently calculate the surface free energies of MFI and FAU zeolites and explore their morphological evolution under dynamic conditions. This study establishes a general and efficient theoretical framework for investigating the thermodynamic properties of zeolite surfaces. Benchmark (surveying) ; Surface (topology) ; Workflow ; Stability (learning theory) ; Process (computing) ; Key (lock) ; Zeolite ; Protocol (science) Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Bo Li | Junkai Huang | Jie Bai | Ming Ren | Hu Quan | Zengxin Pu
Source:  (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

Internal defect identification of epoxy potting materials based on multimodal data fusion During the preparation process and service period of epoxy resin, due to the comprehensive influence of numerous complex factors, typical defects such as bubble, crack, and delamination are highly likely to occur. The presence of these defects can easily trigger partial discharge (PD), which will severely deteriorate the insulation performance of epoxy resin. Traditional defect recognition algorithms often rely on the time-domain or frequency-domain characteristics of the discharge signals to determine defects. However, it is difficult to achieve accurate recognition of micro-defects in epoxy resin materials. In this study, a recognition model of the multimodal convolutional neural network is constructed and the time-domain, frequency-domain, and time-frequency-domain characteristics of PD signals are organically integrated. With the powerful learning ability of the convolutional neural network, the complex relationships among these characteristics are automatically learned. Finally, the efficient and accurate identification of the types of internal defects in epoxy resin is achieved. Epoxy ; Convolutional neural network ; Identification (biology) ; Delamination (geology) ; Potting ; Artificial neural network ; Pattern recognition (psychology) ; Process (computing) High voltage insulation and dielectric phenomena ; Epoxy Resin Curing Processes ; Electrical Fault Detection and Protection
Authors: Etienne Reboul (22261166) | Zoe Wefers (22261169) | Harish Prabakaran (22261172) | Jérôme Waldispühl (6658472) | Antoine Taly (377370)
Source: Figshare (article, 2025-09-17)
Field: Materials Science — Materials Chemistry

Improving the Reliabilityof Molecular String Representationsfor Generative Chemistry Generative modeling for chemistry has advanced rapidly in recent years, but this surge in popularity raises a foundational question: which molecular representation is best suited for modern machine learning models? Despite not being designed for generative tasks, SMILES remains the most commonly used string-based representation. However, while SMILES follows strict syntactic rules, grammatically correct SMILES strings do not always correspond to valid molecules. SELFIES, an alternative grammar, addresses this limitation by ensuring that every string of SELFIES tokens represents a valid molecule. In this study, we comprehensively evaluate the limitations of both SMILES and SELFIES as representations for generative models. We analyze two key criteria for robust molecular generation: viability, which means that generated strings represent novel, unique molecules with correct valence, and fidelity, where the distribution of physicochemical properties from sampled molecules resembles that of the training data. We find that approximately one-fifth of the molecules generated using RDKit default canonical SMILES are invalid, failing the viability criterion. In contrast, all SELFIES-generated molecules are viable, but they deviate significantly from the training distribution, indicating low fidelity. To address these limitations, we develop data augmentation procedures for both representations. While simplifying the SELFIES grammar yields only modest gains in fidelity, our stochastic augmentation method for SMILES, ClearSMILES, significantly improves both viability and fidelity. ClearSMILES simplifies syntax by reducing the vocabulary size and explicitly encoding aromaticity via Kekule SMILES, making the string representations easier for models to process. Using ClearSMILES, the rate of invalid samples decreases by an order of magnitude, from 20 to 2.2%, and fidelity to the training distribution is also moderately improved. String (physics) ; Generative grammar ; Generative model ; Representation (politics) ; Syntax ; Vocabulary ; Terminology ; Fidelity Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Bupathy, Arunkumar | Hall, Darian | Smalyukh, Ivan I. | Campos-Villalobos, Gerardo | Subert, Rodolfo | Dijkstra, Marjolein
Source: ArXiv.org (article, 2025-11-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

From Knots to Crystals: Machine-Learned Potentials for Self-Assembling Topological Solitons in Liquid Crystals Knotted fields in classical and quantum systems were long recognized for their non-trivial topologies and particle-like behavior, but practical applications have been limited by the difficulty of stabilizing them. Recently, stable knotted solitonic textures--heliknotons--have been discovered in chiral liquid crystals, forming adaptive crystal assemblies via elastic distortion-mediated interactions. We use machine learning to develop single-site coarse-grained potentials that accurately capture these chiral anisotropic interactions, enabling large-scale simulations beyond the reach of fine-grained methods. Our machine-learned potentials reproduce the experimentally observed assemblies and provide an efficient framework for modeling a wide range of topological textures. Liquid crystal ; Topology (electrical circuits) ; Network topology ; Topological defect ; Quantum ; Anisotropy ; Range (aeronautics) Liquid Crystal Research Advancements ; Topological Materials and Phenomena ; Material Dynamics and Properties
Authors: Wenguang Hu | Zebin Wu | Menglu Li | S. M. Feng | Hangbo Qi | Xingjian Lu | Xiaotao Zu | Liang Qiao | Haoruo Xiao
Source: Research Square (preprint, 2025-09-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Accelerated discovery of ferroelectric perovskites with giant polarization via machine learning Ferroelectricity ; Polarization (electrochemistry) ; Spintronics ; Giant magnetoresistance Multiferroics and related materials ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides