jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiang Qiu | Heung‐Fai Lam | Xiuyuan Hu | Wentao Li | Siwei Fu | Fankun Zeng | Hao Zhang | Xiaonan Wang
Source: ArXiv.org (preprint, 2025-03-31)
Field: Materials Science — Materials Chemistry

Accelerating High-Efficiency Organic Photovoltaic Discovery via Pretrained Graph Neural Networks and Generative Reinforcement Learning Organic photovoltaic (OPV) materials offer a promising avenue toward cost-effective solar energy utilization. However, optimizing donor-acceptor (D-A) combinations to achieve high power conversion efficiency (PCE) remains a significant challenge. In this work, we propose a framework that integrates large-scale pretraining of graph neural networks (GNNs) with a GPT-2 (Generative Pretrained Transformer 2)-based reinforcement learning (RL) strategy to design OPV molecules with potentially high PCE. This approach produces candidate molecules with predicted efficiencies approaching 21\%, although further experimental validation is required. Moreover, we conducted a preliminary fragment-level analysis to identify structural motifs recognized by the RL model that may contribute to enhanced PCE, thus providing design guidelines for the broader research community. To facilitate continued discovery, we are building the largest open-source OPV dataset to date, expected to include nearly 3,000 donor-acceptor pairs. Finally, we discuss plans to collaborate with experimental teams on synthesizing and characterizing AI-designed molecules, which will provide new data to refine and improve our predictive and generative models. Photovoltaic system ; Reinforcement learning ; Transformer ; Generative grammar ; Artificial neural network ; Graph Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; CO2 Reduction Techniques and Catalysts
Authors: Jie Li | Linxue Zhen | Zhichun Bi | Yu Ma | Hui Huang | Yongxin Li
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Three-Channel Sensor Array Based on Switchable Nanozymatic Reactions of a Novel Trimetallic Nanozyme for Anti-Interference Identification of Tea Adulteration Interference (communication) ; Identification (biology) ; Channel (broadcasting) ; Nanotechnology ; Computer science ; Chemistry ; Combinatorial chemistry ; Materials science ; Telecommunications ; Biology Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Oksana A. Mayorova | Mariia S. Saveleva | Ekaterina S. Prikhozhdenko
Source: Preprints.org (preprint, 2025-12-30)
Field: Chemistry — Analytical Chemistry

Raman Spectroscopy of Protein-Polysaccharide Conjugates: A Comparative Study of Tree-Based Ensemble Models Proteins with additives, especially in small quantities, are of great interest as a subject of a study. Machine learning approaches implemented to Raman spectroscopy data could provide an insight into chemical structure of such mixtures or conjugates. Although, de-cision tree model could be powerful in solving either classification or regression task and could provide accessible predictions, it is prone to overfitting. Ensemble models that implement several decision trees could overcome the determined problem. Five different model types are discussed: RandomForest, GradientBoosting, AdaBoost, Voting, and Stacking. Raman spectroscopy data of whey protein isolate (5 wt. %) with different amounts of hyaluronic acid (0, 0.1, 0.25, and 0.5 wt. %) were used as datasets. Optimiza-tion established that ensembles of 200 decision trees with a maximum depth of four were optimal. AdaBoostClassifier found to be the most efficient in finding differences between whey protein isolate and its conjugates with hyaluronic acid: 99.5% accuracy, 100% sen-sitivity, and 98.0% specificity. Stacking of RandomForest, GradientBoosting, and Ada-Boost regressors with final estimator of RidgeCV was the most effective approach in the regression task (R2 = 0.963). According to the feature importance plots, the Raman bands that were most influential in predicting the results were 1003 cm-1 (phenylalanine, ring breath), 1206 cm-1 (C–C stretching), 1240 cm-1 (amide III (β-sheet), N−H in-plane bend, C−N stretch), and 1399 cm-1 (aspartic and glutamic acids, C=O stretch of COO−). Raman spectroscopy ; Decision tree ; Ensemble learning ; Ensemble forecasting ; Spectroscopy ; Stacking ; Pattern recognition (psychology) ; Feature (linguistics) ; Task (project management) Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Molecular spectroscopy and chirality
Authors: Handong Zhao | M. Li | Kang Shi-Zhao | Taiyang Zhang | Lixia Qin | Li Xiangqing
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Intelligent identification of Raman-inactive molecules on a plasmonic ZIF-derived SERS platform Substrate (aquarium) ; Plasmon ; Zeolitic imidazolate framework ; Molecule ; Raman scattering ; Detection limit ; Raman spectroscopy Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced oxidation water treatment
Authors: Tianhao Tan | Lian Duan | Dong Wang
Source: Advanced Intelligent Discovery (article, 2025-08-08)
Field: Materials Science — Materials Chemistry

Interpretable Machine Learning for Solvent‐Dependent Carrier Mobility in Solution‐Processed Organic Thin Films Understanding how solvent properties influence charge transport in solution‐processed organic films is essential for the rational optimization of processing conditions. In this study, we employed a transfer learning‐based multiscale simulation framework to predict the carrier mobility of quadruple thiophene (QT) films prepared using thirteen different solvents. By combining iterative leave‐one‐out cross‐validation with interpretable machine learning (ML), we developed a quantitative model capable of predicting carrier mobility based on solvent properties, achieving an R 2 value of up to 0.80 in independent tests. Shapley value analysis revealed that the Hansen solubility parameters δD and δH, as well as the solvent molar volume, are the key factors determining film performance. Specifically, strong dispersion interactions (δD), moderate hydrogen bonding (δH), that closely match and are compatible with the QT, and a small molar volume were found to enhance structural relaxation and the formation of percolated transport networks. These findings provide a systematic approach for solvent selection in the fabrication of high‐mobility organic semiconductors and demonstrate the potential of ML in constructing reliable physical models from limited data. Solvent ; Organic semiconductor ; Materials science ; Dispersion (optics) ; Solubility ; Electron mobility ; Computer science ; Machine learning ; Chemistry ; Physical chemistry ; Organic chemistry ; Physics ; Optoelectronics ; Optics Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Conducting polymers and applications
Authors: Siva Peddareddigari | Sindhu Chaudhary | Annamalai Manickavasagan
Source: Journal of Food Process Engineering (article, 2025-12-01)
Field: Chemistry — Analytical Chemistry

Detection of Peanut Contamination in Wheat Flour Using a Digital Light Processing ( <scp>DLP</scp> ) Based Near‐Infrared Spectrometer and Ensemble Machine Learning ABSTRACT Peanut flour contamination poses significant health and regulatory challenges within food production systems, often originating from adulterated raw materials or cross‐contamination during processing. Rapid and non‐destructive detection methods are essential for real‐time monitoring and mitigation. This study aimed to develop a non‐destructive detection system using a portable digital light processing (DLP) based near‐infrared (NIR) spectrometer integrated with machine learning (ML) models to identify and quantify peanut adulteration in wheat flour. Samples were systematically prepared by blending wheat flour with peanut flour at six contamination levels: 0%, 2%, 4%, 6%, 8%, and 10% (w/w). Spectral data were acquired using a handheld DLP‐NIR device operating in the 900–1700 nm range. Due to limited separability among contamination levels, a binary classification framework was adopted to distinguish pure wheat flour (0%) from contaminated samples (≥ 2%) using models such as Linear Discriminant Analysis, Logistic Regression, and boosted tree ensembles. Regression models including Extra Trees, Random Forest, and LightGBM were trained to estimate contamination levels. Spectral preprocessing involved normalization and Yeo‐Johnson transformation, while model evaluation employed stratified K‐fold cross‐validation with Optuna‐based hyperparameter tuning. The top five performing classifiers were selected for ensemble learning to enhance predictive performance. The final ensemble classifier achieved accuracy, precision, recall, and F1‐score values of 0.9937, 0.9976, 0.9948, and 0.9962, respectively. Ensemble regressors yielded R 2 = 0.7477, RMSE = 1.7157, and MAE = 1.148, demonstrating promising semi‐quantitative estimation capabilities. These results highlight the feasibility of combining portable NIR spectroscopy with ensemble ML for rapid, in‐line detection of peanut contamination in wheat flour. Linear discriminant analysis ; Random forest ; Contamination ; Ensemble learning ; Support vector machine ; Preprocessor ; Normalization (sociology) ; Wheat flour ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Listeria monocytogenes in Food Safety ; Advanced Chemical Sensor Technologies
Authors: Youwen You | Y. Z. Huo | Guo Chun-mei | Zhimin He | Yuan Zhao | Bin Yang | Jianxing Chen | Chong Meng | Xizhang Chen
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Spectroscopy

Prediction on the Thermodynamic Performance Evolution of a Novel Aerogel Composite Insulation Material Under Service Conditions Based on Data-Driven Methods Aerogel ; Composite number ; Materials science ; Composite material ; Service (business) ; Business Aerogels and thermal insulation ; Building Energy and Comfort Optimization ; Urban Heat Island Mitigation
Authors: Yuriі Kovalоv | Віктор Аулін | Serhii Kovalov | Olexandr Kuzyk | Andrii Hrynkiv
Source: Central Ukrainian Scientific Bulletin Technical Sciences (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Materials Research: Trends, Tools, and Transformations This paper examines the evolving role of digital platforms in reshaping materials science through the lens of materials informatics. It focuses on three prominent initiatives—Materials Project, NOMAD Repository, and Citrine Informatics—that illustrate how artificial intelligence, computational modeling, and structured data infrastructures are converging to accelerate discovery and innovation. Each platform integrates machine learning algorithms, simulation tools, and data management systems to support tasks such as high-throughput screening, property prediction, and compositional optimization. The study provides a comparative analysis of the functional architecture of these platforms, identifying core components that could inform the development of a national ecosystem for materials informatics. Particular attention is given to the principles of FAIR data (Findable, Accessible, Interoperable, Reusable), which underpin transparency, reproducibility, and collaborative research across borders. By examining how these platforms operationalize FAIR principles, the paper highlights pathways for aligning Ukrainian research infrastructure with international standards. Traditional experimental approaches, while foundational, often face limitations in terms of scalability, cost, and time. The integration of AI-driven methods offers a promising alternative, enabling researchers to simulate, predict, and refine materials with greater efficiency. This shift calls for strategic investment not only in technical infrastructure but also in education. The paper proposes the development of interdisciplinary academic programs that combine materials science with data analytics, computational physics, and AI literacy. Furthermore, the research outlines strategic directions for incorporating Ukrainian materials data into global platforms, emphasizing the importance of two-way knowledge exchange. Such integration would enhance the visibility of Ukrainian research and foster deeper international collaboration. The insights presented here aim to support both academic inquiry and policy development, offering a foundation for future initiatives in sustainable innovation and digital transformation within the materials science domain. Operationalization ; Ukrainian ; Nexus (standard) ; Software deployment ; Face (sociological concept) ; Visibility ; Data management Machine Learning in Materials Science ; Research Data Management Practices ; Scientific Computing and Data Management
Authors: Anna Kryshchyshyn‐Dylevych | Roman Lesyk
Source: ScienceRise Pharmaceutical Science (article, 2025-06-30)
Field: Chemistry — Organic Chemistry

Application of clustering algorithms and pharmacophore screening for identification of thiazolidinone and pyrazoline derivatives with dual antiparasitic and anticancer activity Thiazolidinones and related heterocycles exhibiting antimicrobial, antiparasitic, anticancer, antidiabetic, and anti-inflammatory activities are considered privileged scaffolds for the development of novel, drug-like molecules. 4-Thiazolidinone-based hybrids with alkanecarboxylic acid moieties, pyrazoline, phenylindole or imidazothiadiazole fragments have been thoroughly investigated as potential antiparasitic agents. Along with numerous studies that proved their high anticancer potential, this class of compounds is attractive for the promising strategy of redirecting antiparasitic drugs for cancer treatment. The aim of the study. We aimed to investigate the correlation between the antileukemic and antiparasitic properties of various thiazolidinone and pyrazoline derivatives. Materials and methods. The anticancer activity of a data set of 31 compounds against five Leukemic cell lines was studied at a single concentration (10−5M). The antitrypanosomal activity data has been collected under the same assay protocol against Trypanosoma brucei brucei (Tbb). The clustering algorithms were implemented in Python using the NumPy, Pandas, Scikit-learn, Matplotlib, and Plotly libraries. LigandScout 4.4 software was used for the 3D-pharmacophore design. Results. The compounds with antitrypanosomal activity were divided into 3 classes according to the IC50 values calculated in the growth inhibition assay against Tbb. The percentage of cell growth in the in vitro assay of studied compounds on five Leukemic cell lines was used for the machine learning study. Applying both the K-means and Agglomerative hierarchical clustering algorithms, compounds from class 1 were grouped into one cluster. The pharmacophore screening using merged pharmacophore derived from BCL-2-venetoclax complexes showed good pharmacophore-fit scores for the compounds selected in one cluster by both algorithms. The same pharmacophore model, when applied to a dataset of thiazolidinone/thiazole-indole/imidazothiadiazole hybrid molecules with high antitrypanosomal activity in vitro, assigned them as active. Conclusions. The findings of the study suggest that thiazolidine derivatives and related compounds exhibit dual anti-parasitic and anticancer properties, which may help to identify their antiproliferative mechanism of action in parasitic and cancer cells. Pharmacophore ; Antiparasitic ; Identification (biology) ; Combinatorial chemistry ; Chemistry ; Antiparasitic agent ; Cluster analysis ; Pharmacology ; Stereochemistry ; Computer science ; Medicine ; Biology ; Artificial intelligence Synthesis and biological activity
Authors: Banghua Wu | Yujie Peng | Lin Gao | Canghao Xu | Changjian Liu | Haihong Guo | Yong Huang | Junsheng Yu
Source: Sensors and Actuators B Chemical (article, 2025-12-02)
Field: Materials Science — Polymers and Plastics

Eutectogel-gated ultra-flexible organic electrochemical transistors for humidity sensing and neuromorphic computing Neuromorphic engineering ; Electrolyte ; MNIST database ; Transistor ; Electrochemistry ; Bioelectronics ; Memristor ; Ionic liquid Conducting polymers and applications ; Advanced Memory and Neural Computing ; Organic Electronics and Photovoltaics
Authors: H.-Y. He | Bin Cheng | Qianbing Wan | Xibo Pei
Source: Journal of Materials Chemistry B (review, 2025-11-25)
Field: Materials Science — Materials Chemistry

Application of copper based single-atom nanozymes in wound healing and tumor treatment Single-atom nanozymes (SANs) with transition metals as their core are nanomaterials characterized by atomically dispersed active sites and intrinsic enzyme-mimicking activity, exhibiting ultrahigh catalytic efficiency. As an essential trace element and critical cofactor in organisms, copper plays vital roles in physiological processes such as energy metabolism and antioxidant defense. This biological significance, combined with the excellent tunability and stability of SANs, drives extensive research into copper based single-atom nanozymes (Cu-SANs) in biomedical fields, particularly in wound healing and tumor treatment. This review systematically summarizes recent advances in Cu-SANs for these two applications. Firstly, we detail diverse synthetic strategies, encompassing pyrolysis, hydrothermal synthesis, electrochemical deposition, <i>in situ</i> bombardment embedding, intracellular synthesis, solid-phase migration, ball milling, and machine learning-aided design. Then, the multi-enzymatic activities and corresponding catalytic mechanisms are analyzed. Besides, the applications of Cu-SANs in wound healing and tumor treatment are systematically summarized. Finally, we discuss persistent challenges in active site precision control, biocompatibility enhancement, artificial intelligence-aided design, manufacturing scalability and hybrid system development to meet clinical demands. This work may facilitate the development of high-performance next-generation Cu-SANs for wound healing and tumor treatment. Biocompatibility ; Wound healing ; Nanomaterials ; Copper ; Catalysis ; Biocompatible material Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Nanocluster Synthesis and Applications
Authors: Shuyi Lu (8479908) | Qian Wu (209382) | Shanshan Wang (283009) | Dongru Sun (9260648) | Yan Liu (25061) | Shaohua Huang (1310163) | Biling Huang (11943781)
Source: Figshare (article, 2025-10-07)
Field: Chemistry — Spectroscopy

<sup>19</sup>F NMR-Guided Chiral Analysis and MachineLearning for Absolute Configuration Prediction of Carboxylic Acids <sup>19</sup>F-labeled molecular probes offer significant advantages in chiral analysis, with their stereostructures playing a critical role in enantiodiscrimination. Rapid and accurate assignment of absolute configurations is particularly important for the pharmaceutical field and asymmetric synthesis. This work presents a comparative evaluation of two <sup>19</sup>F-labeled probes as CDAs: aromatic <b>P</b><sub><b>1</b></sub> ((<i>S</i>)-1-(2-fluorophenyl)ethylamine) and aliphatic <b>P</b><sub><b>2</b></sub> (<i>trans</i>-2-fluorocyclohexanamine), focusing on their enantiomeric recognition of chiral carboxylic acids. The results demonstrate that <b>P</b><sub><b>1</b></sub> outperformed <b>P</b><sub><b>2</b></sub>, particularly in analyzing carboxylic acids with chiral centers positioned several carbons away from the carboxyl group or possessing multiple chiral centers. Density functional theory calculations elucidated the underlying mechanisms, revealing that <b>P</b><sub><b>1</b></sub>’s great recognition efficiency arises from a larger energy difference between diastereomers and the formation of hydrogen bonds between its fluorine atom and the analytes’ protons. These findings emphasize the importance of probe–analyte interactions in CDA-based enantiorecognition systems for achieving high-resolution discrimination. Moreover, <b>P</b><sub><b>1</b></sub> exhibited practical applications in determining ee values and analyzing various brands of ibuprofen capsules and tablets, highlighting its potential for pharmaceutical quality control. Furthermore, we introduced a novel online platform combining a <b>P</b><sub><b>1</b></sub>-guided <sup>19</sup>F NMR approach with machine learning for automated prediction of absolute configurations. This work sheds light on the differences between aromatic and aliphatic probes in enantiorecognition, while introducing an online predictive tool, in favor of the design of <sup>19</sup>F molecular probes and the rapid assignment of absolute configurations. Diastereomer ; Absolute configuration ; Enantiomer ; Hydrogen bond ; Absolute (philosophy) ; Carboxylic acid ; Hydrogen ; Fluorine ; Mean absolute error Molecular spectroscopy and chirality ; Axial and Atropisomeric Chirality Synthesis ; Origins and Evolution of Life
Authors: Mahule Roy | G. Lambard
Source: ArXiv.org (preprint, 2025-12-13)
Field: Materials Science — Materials Chemistry

Quantum-Aware Generative AI for Materials Discovery: A Framework for Robust Exploration Beyond DFT Biases Conventional generative models for materials discovery are predominantly trained and validated using data from Density Functional Theory (DFT) with approximate exchange-correlation functionals. This creates a fundamental bottleneck: these models inherit DFT's systematic failures for strongly correlated systems, leading to exploration biases and an inability to discover materials where DFT predictions are qualitatively incorrect. We introduce a quantum-aware generative AI framework that systematically addresses this limitation through tight integration of multi-fidelity learning and active validation. Our approach employs a diffusion-based generator conditioned on quantum-mechanical descriptors and a validator using an equivariant neural network potential trained on a hierarchical dataset spanning multiple levels of theory (PBE, SCAN, HSE06, CCSD(T)). Crucially, we implement a robust active learning loop that quantifies and targets the divergence between low- and high-fidelity predictions. We conduct comprehensive ablation studies to deconstruct the contribution of each component, perform detailed failure mode analysis, and benchmark our framework against state-of-the-art generative models (CDVAE, GNoME, DiffCSP) across several challenging material classes. Our results demonstrate significant practical gains: a 3-5x improvement in successfully identifying potentially stable candidates in high-divergence regions (e.g., correlated oxides) compared to DFT-only baselines, while maintaining computational feasibility. This work provides a rigorous, transparent framework for extending the effective search space of computational materials discovery beyond the limitations of single-fidelity models. Generative grammar ; Benchmark (surveying) ; Divergence (linguistics) ; Generator (circuit theory) ; Matching (statistics) ; Artificial neural network ; Generative model Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electronic and Structural Properties of Oxides