jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xuchen Wang | S.Z. Zhou
Source:  (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

A Machine Learning-Based Framework for Parameter Optimization in Experimental Systems Optimizing experimental parameters in complex, high-dimensional systems pose significant challenges due to nonlinear interactions and resource constraints. This study presents a comprehensive artificial intelligence (AI) framework designed to accelerate and improve experimental parameter optimization. The framework integrates data preprocessing, feature selection, model comparison, and hyperparameter tuning to build robust predictive models that guide efficient exploration of parameter spaces. Six state-of-the-art machine learning algorithms—including decision trees, random forests, and eXtreme Gradient Boosting (XGBoost)—were systematically evaluated on a real-world dataset. The XGBoost model demonstrated superior predictive performance, achieving high accuracy and strong classification capability, making it the foundation of the proposed optimization pipeline. To validate the framework, the methodology was applied to the hydrothermal synthesis of two-dimensional Bi<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>O<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>Se nanosheets as a representative use case. The AI-driven model effectively predicted synthesis success, enabling exhaustive virtual exploration of a six-dimensional parameter space and identifying optimal experimental conditions with high confidence. This drastically reduces reliance on costly and time-consuming trial-and-error experimentation. The generalizable nature of the framework makes it applicable across various experimental domains where complex parameter optimization is critical. By leveraging advanced AI algorithms and computational optimization techniques, this work underscores the transformative potential of AI to enhance experimental efficiency, predictive accuracy, and resource utilization in scientific research. Hyperparameter ; Parameter space ; Hyperparameter optimization ; Nonlinear system ; Experimental data ; Gradient boosting ; Optimization problem ; Boosting (machine learning) ; Support vector machine Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Subcritical and Supercritical Water Processes
Authors: Zhi Lin | Jiaxin Li | Na Su | B. Li | X. Chen | Haitao Yu | Yanyu Qi
Source: Microchemical Journal (article, 2025-09-03)
Field: Chemistry — Spectroscopy

Perylene monoimide-based red-emitting ratiometric fluorescent probe for rapid and selective hypochlorite monitoring and machine learning-assisted sensing analysis Perylene ; Fluorescence ; Hypochlorite ; Chemistry ; Photochemistry ; Inorganic chemistry ; Optics ; Physics Molecular Sensors and Ion Detection ; Analytical Chemistry and Sensors ; Luminescence and Fluorescent Materials
Authors: Mohammed Ouali | Hamza Bennacer | Mohamed Ladjal | Inas Bouzateur
Source: Computational Materials Science (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Lattice constant prediction of ABX3 and A2BB′X6 perovskites using autoregressive type 3 fuzzy model optimized by extended Kalman filter Autoregressive model ; Fuzzy logic ; Kalman filter ; Robustness (evolution) ; Extended Kalman filter ; Nonlinear system ; Generalization ; Nonlinear autoregressive exogenous model Machine Learning in Materials Science ; Polyoxometalates: Synthesis and Applications ; Inorganic Chemistry and Materials
Authors: Xiang-Yang Liu (1408399) | Qing-Xin Xiang (22210810) | Sheng-Rui Wang (18436159) | Dongyi Xiao (22185291) | Wei-Hai Fang (1268982) | Ganglong Cui (1329708)
Source: Figshare (article, 2025-09-09)
Field: Chemistry — Inorganic Chemistry

Machine LearningParameters of Optimally Tuned Range-SeparatedHybrid Functionals for Transition Metal Complexes In this work, we present a machine learning (ML) approach for predicting the optimal range separation parameters in transition metal complexes (TMCs), aiming to reduce the computational cost associated with optimally tuned range-separated hybrid (OT-RSH) functionals while preserving their accuracy. A data set containing 4380 TMCs was constructed by screening the tmQM database, with each TMC represented by a 62 087-dimensional multiple-fingerprint feature (MFF) vector and labeled with its optimally tuned range separation parameter. Multiple regression models were applied to train the prediction model, and the support vector machine (SVM) model yielded the best performance. The resulting machine-learned functional, ML-LC-PBE0*, achieves ionization potentials comparable to those obtained from fully tuned LC-PBE0* calculations but at a reduced computational cost. Furthermore, comparisons with DLPNO-CCSD(T) reference values confirm that ML-LC-PBE0* consistently outperforms conventional global and fixed parameter RSH functionals. This work provides an efficient and accurate framework for high-throughput screening and electronic structure prediction of TMCs, paving the way for the discovery of advanced TMC-based optoelectronic materials. Support vector machine ; Range (aeronautics) ; Set (abstract data type) ; Feature (linguistics) ; Work (physics) ; Data set ; Regression ; Transition metal Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Synthesis and Properties of Aromatic Compounds
Authors: Yao Yang
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

( <i>Invited</i> ) Benchmark Electrochemistry in <i>Operando</i> liquid-Cell STEM Under Controlled Temperatures In this talk, I will introduce the development of multimodal operando methods to elucidate operando (operating) structure-property relationships of nanocatalysts with an emphasis on electrochemical liquid-cell scanning transmission electron microscopy (EC-STEM) and correlative synchrotron X-ray-based methods.[1] Operando electrochemical 4D-STEM in liquid, driven by machine learning, has shown great potential to interrogate complex structures of active sites of energy materials at solid-liquid interfaces. I will discuss the technical challenges in developing operando EC-STEM with quantitative electrochemistry and simultaneous quantitative STEM imaging, diffraction and spectroscopy at high spatiotemporal resolution. I will then introduce the instrument development on the first-of-its-kind operando heating/cooling EC-STEM to investigate the dynamic growth of heterogeneous nanomaterials in liquid across a wide range of controlled temperatures.[2] 1 Yang et al. Nature 2023, 614, 262 2 Yang* JACS 2025, Accepted Electrochemical energy storage ; Electrochemistry ; Synchrotron ; Nanomaterials ; Synchrotron radiation ; Range (aeronautics) ; Characterization (materials science) ; Scanning transmission electron microscopy Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Electron Microscopy Techniques and Applications
Authors: Cory Matsumoto | Xiaoyang Ma | Patrick Truong | Yu Gao
Source: ChemRxiv (preprint, 2025-05-15)
Field: Chemistry — Spectroscopy

Proteome-wide Alkylation to Improve Peptide Sequence Coverage and Detection Sensitivity Effective ionization of peptides is critical for the sensitivity and accuracy of proteomic analyses, especially in nanoproteomics and single-cell proteomics, where the material is scarce. Previous studies have demonstrated the potential of peptide alkylation to improve ionization on certain peptides. In this work, we extend the scope of peptide alkylation by systematically modifying peptides with alkyl chains ranging from one to six carbons, aiming to enhance the ionization signal across a broad peptide spectrum. Our global alkylation approach revealed a significant increase in the ionization signal for a large portion of peptides, underscoring the technique's potential to improve proteomic analyses' sensitivity and overall sequence coverage. By employing machine learning to examine the relationship between alkylation-induced signal enhancement and various peptide characteristics, we found that the increase in signal strength is closely connected to peptide hydrophobicity. This relationship suggests that alkylation can particularly augment the detectability of hydrophilic peptides. Our findings not only highlight the importance of hydrophobicity in peptide ionization efficiency but also suggest that strategic alkylation could be a powerful tool to overcome the limitations imposed by sample size in proteomic with limited sample size. Proteome ; Alkylation ; Sensitivity (control systems) ; Peptide ; Sequence (biology) ; Chemistry ; Computational biology ; Biochemistry ; Biology ; Engineering Advanced Proteomics Techniques and Applications ; Chemical Synthesis and Analysis ; Machine Learning in Bioinformatics
Authors: Benjamin Zelin | Jonathan R. Yates | Roger C. Reed
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

New Atomistic Insights Into Yield Anomalies in L12 Intermetallics Intermetallic ; Yield (engineering) ; Anisotropy ; Stacking ; Stacking fault ; Stacking-fault energy ; Instability ; Structural stability Machine Learning in Materials Science ; Intermetallics and Advanced Alloy Properties ; High-pressure geophysics and materials
Authors: Muhammad Irfan | Urooj Fatima | Aqsa Sajjad | Muhammad Naveed
Source: BioNanoScience (article, 2025-09-01)
Field: Materials Science — Biomaterials

Nanoinformatics: A Potential Tool for Precision Targeting in Pediatric Cancer Cancer ; Medicine ; Computer science ; Internal medicine Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Advanced biosensing and bioanalysis techniques
Authors: Shubhang Kare | Sachin Kumar Balakrishnamurthy | Srinivasan Anandhan
Source: Engineering Research Express (article, 2025-12-30)
Field: Materials Science — Biomaterials

Spinning success: mask R-CNN model for precise defect detection in electrospun nanofibers Abstract Localized defects in electrospun nanofibers present a significant barrier to their large-scale production, highlighting a critical need for intelligent monitoring and quality control in nanotechnology. This study introduces a novel defect detection framework for electrospun nanofibers, leveraging the capabilities of the Mask Regional Convolutional Neural Network (Mask R-CNN) to detect and quantify defects at an unprecedented pixel level. The Mask R-CNN model represents a substantial advancement over previous artificial intelligence (AI) systems, achieving an average precision (AP50) of 74.48% for bounding box detection and 84.94% for segmentation masks, coupled with a class accuracy exceeding 93%. This marks a significant leap in the accuracy and reliability of defect detection. A pivotal element of this research was the preparation and utilization of electrospun acrylonitrile-butadiene-styrene terpolymer (ABS) nanofibers. These nanofibers were instrumental in validating the model’s ability to detect defects on test scanning electron microscopy (SEM) images that had not been encountered during its training phase, thereby confirming the model’s robustness and adaptability. The study culminated in the precise quantification of defect areas and ratios, achieved through sophisticated image processing operations on the synthesized nanofiber test set SEM images. The present study not only establishes a new benchmark in defect detection for electrospun nanofibers, but also opens new avenues for enhanced quality control in the field of nanotechnology. It underscores the critical role of AI in advancing materials science, specifically in ensuring the quality and consistency of nanofibrous materials. Nanofiber ; Pixel ; Robustness (evolution) ; Segmentation ; Convolutional neural network ; Spinning ; Reliability (semiconductor) ; Consistency (knowledge bases) ; Image segmentation Electrospun Nanofibers in Biomedical Applications ; Machine Learning in Materials Science ; Nanofabrication and Lithography Techniques
Authors: Yung-Hsing Peng | Pai‐Feng Hsu | Szu-Hsuan Wang | Hsiu‐Ming Tsai | Jen-Sheng Tsai | Chia-Pei Lee | Ying-Hsu Chen | Po‐Cheng Huang | Harriet Lina
Source: IET conference proceedings. (article, 2025-08-01)
Field: Chemistry — Analytical Chemistry

Developing tea quality grading system with AI and GTN-9 measurements Taiwan's commercial tea market is rapidly growing, with over 27,000 tea shops, more than 30 hand-shaken beverage brands, and a market potential exceeding 55 billion NTD. In Taiwan, the qualities of tea raw materials are determined with professional tasters, so that tea prices can vary according to their qualities. However, it usually takes more than five years to train a professional taster, which is long time compared with rapid growth of markets. To address this issue, this study introduces an AI-assisted tea quality grading system combining spectroscopic measurements and professional taster assessments. We examine six different tea raw materials with the GTN-9 device, obtaining nine key ingredients including theanine, catechins, and caffeine. Simultaneously, we collaborate with professional tea tasters to grade the samples into A (high quality), B (tolerable quality), and C (unacceptable quality) categories based on their aroma and taste. An AI model is trained to correlate the measured data with taster evaluations, achieving a grading accuracy of 95%. This system offers a standardized, objective quality grading method, reducing the burden of tasters and improving supply chain efficiency. By integrating AI with scientific measurement, tea manufacturers can establish consistent grading criteria, ensuring more reliable raw material selection. Our study demonstrates the feasibility of AI-driven tea quality grading, paving the way for future advancements. Further research could improve predictive accuracy through deep learning techniques and expand the methodology to other tea varieties, providing consistency and transparency in the global tea industry. Grading (engineering) ; Quality (philosophy) ; Computer science ; Engineering ; Civil engineering ; Philosophy Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Shahrzad Mirzaei | Mariam Bebawy | Amr Mohamed Sharafeldin | Mohamed Hefeeda
Source: Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (article, 2025-12-02)
Field: Chemistry — Analytical Chemistry

MobiLyzer: Fine-grained Mobile Liquid Analyzer Most current methods for liquid analysis and fraud detection rely on expensive tools and controlled lab environments, making them inaccessible to lay users. We present MobiLyzer, a mobile system that enables fine-grained liquid analysis on unmodified commodity smartphones in realistic environments such as homes and grocery stores. MobiLyzer conducts spectral analysis of liquids based on how their chemical components reflect different wavelengths. Conducting spectral analysis of liquids on smartphones, however, is challenging due to the limited sensing capabilities of smartphones and the heterogeneity in their camera designs. This is further complicated by the uncontrolled nature of ambient illumination and the diversity in liquid containers. The ambient illumination, for example, introduces distortions in measured spectra, and liquid containers cause specular reflections that degrade accuracy. To address these challenges, MobiLyzer utilizes RGB images captured by regular smartphone cameras. It then introduces intrinsic decomposition ideas to mitigate the effects of illumination and interference from liquid containers. It further leverages the near-infrared (NIR) sensors on smartphones to collect complementary signals in the NIR spectral range, partially mitigating the limited sensing capabilities of smartphones. It finally presents a new machine-learning model that reconstructs the entire spectrum in the visible and NIR ranges using the captured RGB and NIR images, which enables fine-grained spectral analysis of liquids on smartphones without the need for expensive equipment. Unlike prior models, the presented spectral reconstruction model preserves the original RGB colors during reconstruction, which is critical for liquid analysis since many liquids differ only in subtle spectral cues. We demonstrate the accuracy and robustness of MobiLyzer through extensive experiments with multiple liquids, four different smartphones, and seven illumination sources. Our results show, for example, that MobiLyzer can accurately detect adulteration with small ratios, identify quality grades of the same liquid (e.g., refined vs. extra virgin olive oil), differentiate the country of origin of oils (e.g., olive oil from Italy versus USA), and analyze the concentration of materials in liquids (e.g., protein concentration in urine for early detection of kidney diseases). RGB color model ; Robustness (evolution) ; Mobile device ; Interference (communication) ; Spectral imaging ; Hyperspectral imaging ; Spectral signature Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Advanced Chemical Sensor Technologies
Authors: Koichi Ozaki | Naoki Nohira | Masaki Tahara | Itsuo Kumazawa | Hideki Hosoda
Source: Computational Materials Science (article, 2025-11-19)
Field: Materials Science — Materials Chemistry

Transfer learning approach with Voronoi-based synthetic data for high-accuracy segmentation of equiaxed grain boundaries Accurate segmentation of microstructural images plays a critical role in quantitative materials analysis. However, it remains a challenging task due to the scarcity of annotated data and the inherent diversity of microstructures. While transfer learning is a promising solution to data scarcity, conventional approaches using models pre-trained on general-purpose datasets like ImageNet are often suboptimal due to their low similarity to microstructural images. To overcome this limitation, we propose a novel transfer learning approach utilizing synthetically generated Voronoi diagrams as a source dataset for equiaxed grain microstructure analysis. Unlike images produced by simulations or deep learning models, Voronoi diagrams can be created quickly with simple and intuitive parameters, while still resembling key structural features of equiaxed grain microstructures. This simplicity allows for efficient exploration of how generation conditions affect downstream model performance. Our results demonstrate that adding Gaussian noise to the Voronoi diagrams improves model performance, achieving a higher Intersection over Union (IoU) score than models pre-trained on ImageNet. Furthermore, the model learns microstructure-specific features such as triple junctions, enabling accurate grain boundary extraction even in low-contrast regions. This approach offers a simple and practical solution for pre-training task-specific models in materials science. • Synthetic Voronoi data mitigate the scarcity of annotated microstructure datasets. • Adding Gaussian noise improved grain boundary segmentation accuracy. • Voronoi-based pretraining outperforms generic ImageNet pretraining in IoU score. • The model learns microstructure-specific features, such as triple junctions. • This approach provides a practical solution for data-driven materials analysis. Voronoi diagram ; Segmentation ; Synthetic data ; Equiaxed crystals ; Transfer of learning ; Pattern recognition (psychology) ; Noise (video) ; Intersection (aeronautics) Machine Learning in Materials Science ; Advanced Neural Network Applications ; Mineral Processing and Grinding
Authors: Guindo Mahamed L, Sokhar Samb, Mor Gueye
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-07)
Field: Chemistry — Analytical Chemistry

THE POWER OF IA AND SPECTROSCOPY FOR FOOD Ensuring the quality, safety, and authenticity of food is a critical issue that remains among the paramount globalpriorities because of the challenges of food fraud, contamination, and complex supply chains. In recent years, theconvergence of Artificial Intelligence and spectroscopic technologies has emerged as a transformative solution forfast, accurate, and non-destructive food analysis. Spectroscopy techniques, such as infrared, near-infrared, Raman,and fluorescence spectroscopy, give information on the molecular composition of food, while AI will improve theirinterpretative strength with more advanced data processing, pattern recognition, and predictive modeling. This paperdiscusses how AI-driven chemometrics and machine learning algorithms interface with spectroscopic data to realizereal-time detection of adulteration, prediction of nutritional value, and freshness and safety monitoring. Case studiesfrom various food industries showed significant improvements in the accuracy, speed, and automation of detection.Despite promising advances, challenges related to data standardization, model generalization, and equipment costremain persistent. The full paper concludes that synergistic use of AI and spectroscopy will be one of the key driverstoward establishing a transparent, smart, and sustainable global food system. Transformative learning ; Chemometrics ; Automation ; Food supply ; Fluorescence spectroscopy ; Spectroscopy ; Power (physics) ; Key (lock) Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Melamine detection and toxicity
Authors: Qinyang Li | Rongzhi Dong | Nicholas Miklaucic | Jeffrey Hu | Sadman Sadeed Omee | Lai Wei | Sourin Dey | Ming Hu | Jianjun Hu
Source: arXiv (Cornell University) (preprint, 2025-12-31)
Field: Materials Science — Materials Chemistry

In context learning Foundation models for Materials Property Prediction with Small datasets Foundation models (FMs) have recently shown remarkable in-context learning (ICL) capabilities across diverse scientific domains. In this work, we introduce a unified in-context learning foundation model (ICL-FM) framework for materials property prediction that integrates both composition-based and structure-aware representations. The proposed approach couples the pretrained TabPFN transformer with graph neural network (GNN)-derived embeddings and our novel MagpieEX descriptors. MagpieEX augments traditional features with cation-anion interaction data to explicitly measure bond ionicity and charge-transfer asymmetry, capturing interatomic bonding characteristics that influence vibrational and thermal transport properties. Comprehensive experiments on the MatBench benchmark suite and a standalone lattice thermal conductivity (LTC) dataset demonstrate that ICL-FM achieves competitive or superior performance to state-of-the-art (SOTA) models with significantly reduced training costs. Remarkably, the training-free ICL-FM outperformed sophisticated SOTA GNN models in five out of six representative composition-based tasks, including a significant 9.93\% improvement in phonon frequency prediction. On the LTC dataset, the FM effectively models complex phenomena such as phonon-phonon scattering and atomic mass contrast. t-SNE analysis reveals that the FM acts as a physics-aware feature refiner, transforming raw, disjoint feature clusters into continuous manifolds with gradual property transitions. This restructured latent space enhances interpolative prediction accuracy while aligning learned representations with underlying physical laws. This study establishes ICL-FM as a generalizable, data-efficient paradigm for materials informatics. Suite ; Feature learning ; Property (philosophy) ; Artificial neural network ; Feature (linguistics) ; Benchmark (surveying) ; Disjoint sets ; Context (archaeology) ; Deep learning Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices