jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Quanwei Yu | Yujie Liu | Zhijia Wang | Chengyong Wu | Ruofei Zhang | Lun Tan | Lele Zhang | Cuiyu Guo | Lantu Gou | Weimin Li | Yang He | Yuxi Wang
Source: Journal of Medicinal Chemistry (article, 2025-09-20)
Field: Chemistry — Organic Chemistry

Discovery of Novel 4,5-Dihydropyrrolo[3,4-<i>c</i>]pyrazol-6(<i>2H</i>)-one-Based Tubulin Inhibitors Targeting Colchicine Binding Site with Potent Anti-Ovarian Cancer Activity To address the toxicity of current microtubule inhibitors, we employed the GeminiMol deep learning model to screen the Zinc20 database, identifying a novel 4,5-dihydropyrrolo[3,4-<i>c</i>]pyrazol-6(2<i>H</i>)-one scaffold (<b>Y1</b>) targeting the colchicine binding site. Subsequent optimization culminated in <b>Y60s</b>, a potent antiproliferative agent (SKOV3 IC<sub>50</sub> = 0.025 μM) that inhibits clonogenic formation, migration, and invasion of ovarian cancer cells. <b>Y60s</b> inhibited tubulin polymerization which in turn induced G2/M arrest and apoptosis in SKOV3 cells. <b>Y60s</b> demonstrated potent antitumor activity without observable toxicity in an SKOV3 xenograft model. The cocrystal structure of <b>Y8</b> in complex with tubulin was resolved, confirming the key binding mode of 4,5-dihydropyrrolo[3,4-<i>c</i>]pyrazol-6(2<i>H</i>)-one compounds. This work showcases <b>Y60s</b> as a promising novel tubulin inhibitor and highlights the utility of deep learning model for rapid identification of bioactive compounds from large chemical databases. Colchicine ; Tubulin ; Microtubule ; Cancer cell ; Cancer ; Clonogenic assay ; Binding site ; Apoptosis Synthesis and biological activity ; Computational Drug Discovery Methods ; Click Chemistry and Applications
Authors: Imran B. Chaudhry | Mark J. Bronson | Lasse Jensen
Source: Journal of Chemical Theory and Computation (article, 2025-07-09)
Field: Materials Science — Materials Chemistry

Δ-Machine Learning of Polarizability Tensors Using a Dipole Interaction Model As a fundamental response property, the molecular polarizability is responsible for a wide variety of physical phenomena relevant for understanding light-matter and intermolecular interactions. Therefore, it is important to have accurate and efficient methods to calculate the polarizability tensor. In this work, we introduce a model that combines a polarizable dipole interaction model (PIM) with Δ-machine learning to predict polarizability tensors, which we refer to as Δ<sub>PIM</sub><sup>CCSD</sup>. To describe the rotational symmetry, we adapt a unique reference geometry obtained by diagonalizing the PIM polarizability tensor. A major benefit is that only the diagonal elements of the polarizability tensor needs to be learned. The model was parameterized to the coupled cluster singles and doubles (CCSD) polarizabilities from the QM7b data set and was used to predict the polarizabilities for various systems, such as small molecules and molecules from the QM9 data set. We show that the Δ<sub>PIM</sub><sup>CCSD</sup> is comparable in accuracy to density-functional theory with the B3LYP exchange correlation functional (DFT/B3LYP) at a lower cost for molecules with similar chemical composition as the QM7b data set. For the QM9 data set, this was also found, although only after correcting for the smaller basis set used for calculating the polarizabilities in this data set. For molecules smaller and chemically more diverse than the training set, we find that the model performs worse than DFT/B3LYP. Ultimately, our work suggests that larger and more chemically diverse data sets with polarizabilities obtained at a high level of theory are needed. Finally, our results suggest that the model can be improved by incorporating atom-specific polarizabilities into PIM to better account for local environments. In summary, the combination of PIM with Δ-machine learning provides a simple and promising approach for predicting polarizability tensors. Polarizability ; Dipole ; Computer science ; Artificial intelligence ; Machine learning ; Physics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Ziqi Guo | Daniel Carne | Krutarth Khot | Dudong Feng | Guang Lin | Xiulin Ruan
Source: Journal of Computing and Information Science in Engineering (article, 2025-10-23)
Field: Materials Science — Materials Chemistry

A Review of Artificial Intelligence-Driven Approaches for Nanoscale Heat Conduction and Radiation Abstract Heat conduction and radiation are two of the three fundamental modes of heat transfer, playing a critical role in a wide range of scientific and engineering applications ranging from energy systems to materials science. However, traditional physics-based simulation methods for modeling these processes often suffer from prohibitive computational costs. In recent years, the rapid advancements in artificial intelligence (AI) and machine learning (ML) have demonstrated remarkable potential in the modeling of nanoscale heat conduction and radiation. This review presents a comprehensive overview of recent AI-driven developments in modeling heat conduction and radiation at the nanoscale. We first discuss the ML techniques for predicting phonon properties, including phonon dispersion and scattering rates, which are foundational for determining material thermal properties. Next, we explore the role of machine learning interatomic potentials (MLIPs) in molecular dynamics simulations and their applications to bulk materials, low-dimensional systems, and interfacial transport. We then review the ML approaches for solving radiative heat transfer problems, focusing on data-driven solutions to Maxwell’s equations and the radiative transfer equation. We further discuss the ML-accelerated inverse design of radiative energy devices, including optimization-based and generative model-based methods. Finally, we discuss open challenges and future directions, including data availability, model generalization, uncertainty quantification, and interpretability. Through this survey, we aim to provide a foundational understanding of how AI techniques are reshaping thermal science and guiding future research in nanoscale heat transfer. Thermal conduction ; Heat transfer ; Thermal radiation ; Radiative transfer ; Nanoscopic scale ; Phonon ; Thermal ; Radiative cooling ; Range (aeronautics) Thermal properties of materials ; Heat Transfer and Optimization ; Gas Dynamics and Kinetic Theory
Authors: Sarlin Pohthmi | Bhabesh Deka | Manuj Kumar Hazarika | D. P. CHAKRABORTY
Source: Multidisciplinary Research Journal (article, 2025-03-31)
Field: Chemistry — Analytical Chemistry

Early Detection of Bruises in Khasi mandarin (Citrus reticulata Blanco) for the Assessment of Post Harvesting Losses: A Machine Learning Approach This paper explores the application of machine learning (ML) and thermal imaging (TI) for early detection of bruises in Khasi mandarin (Citrus reticulata Blanco), aiming to reduce supply chain losses by identifying damaged fruit before deterioration becomes visually apparent. Leveraging the principle that materials emit distinct infrared radiation based on their physicochemical properties, thermal imaging is used to differentiate bruise from unbruise Khasi mandarins. A machine learning model is applied for classification, successfully analyzing thermal images to identify subtle variations indicating early damage. The thermal images showed that a temperature difference of more than 0.5°C between the bruise and unbruise areas enhanced the detection process. The results demonstrate the possibilities of combining thermal imaging and ML for non-destructive and efficient fruit quality monitoring. This approach offers a reliable method for early identification of fruit damage, enabling timely interventions that prevent further deterioration and minimize post-harvest losses. The study underscores the possibilities for integrating advanced imaging and machine learning techniques in agricultural quality control. Future research with larger datasets can improve model accuracy, benefiting stakeholders across the fruit supply chain and supporting industry sustainability. Khasi ; Mandarin Chinese ; Horticulture ; Art ; Biology ; Linguistics ; Literature Spectroscopy and Chemometric Analyses ; Plant Surface Properties and Treatments ; Date Palm Research Studies
Authors: R. Bhattacharya | Reshmi Mukherjee | Priyabrata Banerjee | Soumyabrata Goswami
Source: Chemistry - An Asian Journal (review, 2025-07-21)
Field: Chemistry — Spectroscopy

Coordination Polymer‐Based Sensors for Pesticide Detection and Decontamination The extensive use of pesticides in agriculture has enhanced both food production and pest management, but it has also raised serious ecological and human health related problems. Addressing these problems requires advanced materials for efficient pesticide detection and decontamination. From this aspect, coordination polymers (CPs) have emerged as a potential approach because of its distinctive features. This review focuses on CP-based solutions for pesticide detection and removal, emphasizing their luminescent properties for developing extremely sensitive and selective sensors. The remarkable adsorption potential of CPs for efficiently eliminating pesticide residues from diverse environmental samples is also a key highlight of this review. It underscores the significance of structural modifications, the integration of CPs with advanced composite materials, and the inclusion of cutting-edge technologies such as artificial intelligence (AI) and machine learning (ML) enhance their effectiveness and broaden their practical applications. By illuminating these advancements, this review aspires to inspire further research, paving the way for innovative and more effective solutions to combat pesticide contamination. Human decontamination ; Pesticide ; Environmental chemistry ; Environmental science ; Chemistry ; Waste management ; Engineering ; Biology Molecular Sensors and Ion Detection ; Luminescence and Fluorescent Materials ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Nicholas Marcella | Ryuichi Shimogawa | Yongchun Xiang | Anatoly I. Frenkel
Source: Journal of Catalysis (article, 2025-04-17)
Field: Materials Science — Materials Chemistry

First shell EXAFS data analysis of nanocatalysts via neural networks Chemistry ; Catalysis ; Extended X-ray absorption fine structure ; Nano- ; Shell (structure) ; Artificial neural network ; Nanotechnology ; Chemical engineering ; Organic chemistry ; Absorption spectroscopy ; Artificial intelligence ; Optics ; Composite material ; Physics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; X-ray Diffraction in Crystallography
Authors: Ruilin Xiao | K. L. Liu | H.R. Li | Ying Ruan
Source: Review of Materials Research (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Active learning prediction and electromagnetic levitation measurement of metastable liquid properties of Ti-Al-Zr alloy The accurate knowledge of liquid properties for Ti-Al based alloys is of great significance for scientific explorations such as revealing the atomic-scale behaviors in alloy processing and realizing the optimization of manufacture technology. By means of the active learning method based on deep neural network (DNN) and electromagnetic levitation technology (EML), we investigate the microstructure evolution of Ti 47 Al 47 Zr 6 alloy, and with a focus on obtaining its liquid state properties. The density, surface tension, viscosity of this liquid alloy and the related self-diffusion coefficient are predicted by the DNN potential. The calculated surface tension exhibits only a deviation of less than 2 ​% as compared with EML experimental values. The liquid local structure characteristics acquired through Voronoi polyhedron analysis indicate that the fraction of relatively high-coordinated clusters with Al as the central atom displayed an anomalous decrease with the falling of liquid temperature. This effect is attributed to the tendency of these clusters to form icosahedral-like geometries, resulting in an increased fraction of icosahedral-like geometries in liquid alloy. Metastability ; Levitation ; Flash (photography) ; Alloy ; Materials science ; Magnetic levitation ; Metallurgy ; Mechanical engineering ; Thermodynamics ; Composite material ; Optics ; Physics ; Engineering ; Magnet Solidification and crystal growth phenomena ; Metallurgical and Alloy Processes ; Crystallization and Solubility Studies
Authors: Mingchun Li | Yang Liu | Dali Chen | Jining Bao | Yan Huo
Source: Scientific Reports (article, 2025-12-16)
Field: Materials Science — Materials Chemistry

Patch-sampled contrastive learning for dense prediction pretraining in metallographic images The shapes, sizes, and distributions of microstructures determine the mechanical properties of alloy products, necessitating identification of microstructures in metallographic images during the manufacturing process. Previous studies demonstrated the success of deep learning in analyzing such images. However, existing methods do not fully consider the high cost of annotations, which limits the application of deep learning-based methods. Considering the popularity of self-supervised learning for natural images, further research is required to design a microstructure-specific pretraining framework. To this end, we propose a novel patch-sampled contrastive learning (PSCL) method, considering the characteristics of metallographic image segmentation tasks. First, image- and patch-level contrastive learning frameworks are designed to help the model effectively capture visual features. The former captures global features, whereas the latter captures local features in greater detail. Second, a multiscale strategy is introduced to improve the adaptability of the subsequent microstructure segmentation. Finally, considering that patches may concurrently contain multiple microstructures, a sampling method based on feature similarity was designed to capture the discriminable features of different categories. In experiments, after model fine-tuning of only one annotated image, the proposed method achieved a Dice score of 0.6296 which is higher than those of existing self-supervised learning methods with identical model structure, proving the effectiveness of the method in microstructure segmentation. The source code and dataset of the proposed PSCL in this work are available at https://github.com/neulmc/PSCL. Segmentation ; Similarity (geometry) ; Deep learning ; Feature (linguistics) ; Pattern recognition (psychology) ; Sampling (signal processing) ; Code (set theory) ; Identification (biology) Machine Learning in Materials Science ; Advanced Neural Network Applications ; Welding Techniques and Residual Stresses
Authors: Arslan Mazitov | Sofiia Chorna | Guillaume Fraux | Marnik Bercx | Giovanni Pizzi | Sandip De | Michele Ceriotti
Source: ArXiv.org (preprint, 2025-06-24)
Field: Materials Science — Materials Chemistry

Massive Atomic Diversity: a compact universal dataset for atomistic machine learning The development of machine-learning models for atomic-scale simulations has benefited tremendously from the large databases of materials and molecular properties computed in the past two decades using electronic-structure calculations. More recently, these databases have made it possible to train universal models that aim at making accurate predictions for arbitrary atomic geometries and compositions. The construction of many of these databases was however in itself aimed at materials discovery, and therefore targeted primarily to sample stable, or at least plausible, structures and to make the most accurate predictions for each compound - e.g. adjusting the calculation details to the material at hand. Here we introduce a dataset designed specifically to train machine learning models that can provide reasonable predictions for arbitrary structures, and that therefore follows a different philosophy. Starting from relatively small sets of stable structures, the dataset is built to contain massive atomic diversity (MAD) by aggressively distorting these configurations, with near-complete disregard for the stability of the resulting configurations. The electronic structure details, on the other hand, are chosen to maximize consistency rather than to obtain the most accurate prediction for a given structure, or to minimize computational effort. The MAD dataset we present here, despite containing fewer than 100k structures, has already been shown to enable training universal interatomic potentials that are competitive with models trained on traditional datasets with two to three orders of magnitude more structures. We describe in detail the philosophy and details of the construction of the MAD dataset. We also introduce a low-dimensional structural latent space that allows us to compare it with other popular datasets and that can be used as a general-purpose materials cartography tool. Consistency (knowledge bases) ; Stability (learning theory) ; Space (punctuation) ; Scalability ; Interatomic potential ; Key (lock) Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: Shahin Alipour Bonab | T. R. Waite | Mohammad Yazdani-Asrami
Source: Journal of Physics Energy (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

Artificially intelligent framework for multi-output performance prediction in diverse solid oxide electrolyzer cells for green hydrogen production plants Abstract Hydrogen sourced from renewable resources has been increasingly regarded as a promising alternative chemical energy carrier to fossil fuels, with support being given to efforts for climate change mitigation and the achievement of net-zero emission targets. Solid oxide electrolyzer cells (SOECs) operating in higher temperatures have shown greater system-level efficiency in comparison to other types of electrolyzers, making them a promising option for mass production of hydrogen. However, diverse SOEC architectures—such as varying anode and cathode materials—and wide operating ranges make it difficult to develop a single, multi-output, multi-parameter, broadly applicable performance model. In this paper, artificial intelligence (AI) techniques have been applied to SOEC data to develop an advanced model based on a cascade-forward neural network (CFNN) that holistically captures these design and operating variables. The resulting CFNN model reliably predicts ohmic resistance, current density, and hydrogen production rate with coefficients of determination ( R 2 ) of 0.9981, 0.9752, and 0.9878, respectively, in comparison with experimentally sourced testing data and across a wide range of SOEC configurations and conditions. Essentially, this paper delivers a highly accurate and computationally efficient digital surrogate model based on an AI technique, enabling smarter design, operation, and scaling of SOECs, and contributing to broader decarbonization goals through cost-effective and efficient sustainable hydrogen production. Hydrogen production ; Renewable energy ; Production (economics) ; Hydrogen ; Anode ; Range (aeronautics) ; Oxide ; Artificial neural network ; Polymer electrolyte membrane electrolysis Machine Learning in Materials Science ; Hybrid Renewable Energy Systems ; Advancements in Solid Oxide Fuel Cells
Authors: Lifei Hu | Yifan Wang | Xinyu Wu | Yuanyuan Shan | Feng Zhu | Fan Zhang | Qiang Yang | Mingxing Liu
Source: Journal of Agriculture and Food Research (article, 2025-08-21)
Field: Chemistry — Analytical Chemistry

Geographic origin discrimination and quantification of phenolic compounds and moisture in Artemisia argyi folium using NIRS and chemometrics Artemisia argyi Folium (AAF), the leaf of the perennial plant Artemisia argyi H. Léveillé & Vaniot, has a long history of medicinal and edible use in East Asia. This study developed a rapid method, which integrated near-infrared spectroscopy (NIRS) and chemometrics to discriminate the geographic origin of AAF and quantify its phenolic components and moisture content. Phenolic-based analysis of adjacent-origin samples with varying storage times demonstrated that storage duration minimally affected geographical origin discrimination of AAF, and it provided a chemical basis for using cross-year samples in NIRS analysis. Multivariate statistical analysis (four methods) and six machine learning algorithms were employed for origin discrimination. The results showed that partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) outperformed unsupervised methods, with key wavenumbers in high and low-frequency regions showing similarities, but exhibiting differences mainly in the 7783–6773 cm −1 range. Spectral preprocessing methods (Savitzky-Golay smoothing, normalization, standard normal variate, and multiplicative scatter correction) enhanced machine learning performance, with support vector machine (SVM), radial basis function (RBF), and convolutional neural network (CNN) models achieving scores of 1.0000 across performance metrics, indicating strong generalization and robustness. Partial least squares regression (PLSR) models for 17 phenolic components and moisture content were screened using different preprocessing methods, identifying three parameters suitable for rapid quantification: eupatilin, jaceosidin, and moisture. This NIRS-based approach achieved a higher detection efficiency and lower cost compared to conventional methods and thus provides a rapid and efficient solution for the geographic traceability and quantitative evaluation of AAF. • Storage time had minimal impact on identifying the origin of Artemisia argyi Folium. • PLS-DA and OPLS-DA models achieved high classification accuracy (AUC ≥0.9955). • Spectral preprocessing methods enhanced NIR machine learning model performance. • SVM, RBF, and CNN models exhibited exceptional stability and accuracy. • Eupatilin, jaceosidin, and moisture content were suitable for NIRS-PLSR quantitation. Folium of Descartes ; Chemometrics ; Artemisia ; Moisture ; Chemistry ; Traditional medicine ; Chromatography ; Medicine ; Organic chemistry Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Bee Products Chemical Analysis