jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hannes Kneiding | David Balcells
Source: Journal of Chemical Information and Modeling (article, 2025-10-24)
Field: Materials Science — Materials Chemistry

tmQMg* Data Set: Excited State Properties of 74k Transition Metal Complexes The application of machine learning approaches to meaningful problems in chemistry and materials science is still challenged by the limited availability of data. In order to close this gap, we report the tmQMg* data set, which provides excited state properties for 74k mononuclear transition metal complexes extracted from the Cambridge Structural Database. All properties were computed at the TD-DFT ωB97xd/def2SVP level of theory. The strongest electron excitations in the ultraviolet, visible, and near-infrared ranges are included, together with the wavelengths and intensities of the first 30 excited states. Further, natural transition orbitals were computed for the strongest excitations in the visible range to determine the nature of the associated charge transfers. By computing the TD-DFT spectra in both gas phase and acetone, we quantified solvatochromic effects, which are also provided with the data set, in terms of both wavelength shifts and intensity changes. The tmQMg* data set will enable the development of discriminative and generative artificial intelligence models with respect to absorption spectra, charge transfer character, and solvatochromism, enabling novel advances in the field of transition metal photochemistry. Excited state ; Atomic orbital ; Solvatochromism ; Chromophore ; Charge (physics) ; Absorption (acoustics) ; Phase transition ; Wavelength ; Transition metal Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Uche Ejiofor | Lijuan Wang | Dayang Wang
Source: Kent Academic Repository (University of Kent) (article, 2025-07-18)
Field: Chemistry — Analytical Chemistry

Plastic identification using NIR spectral analysis and KNN model with correlation feature selection The increasing demand for high-purity postconsumer plastics for recycling highlights the importance of efficient material identification. Near-Infrared (NIR) spectroscopy is widely used to capture unique spectral fingerprints of materials for identification purposes. However, developing efficient and lightweight machine learning models with NIR spectral data for accurate and real-time classification of plastic materials is still in high demand. This paper presents a new hybrid approach combining the k-nearest neighbours (KNN) model with the filter-based correlation feature selection (CFS) technique for plastic identification. Experiments were conducted to test six key recyclable plastic polymers consisting of Polyethylene Terephthalate (PET), High-Density Polyethylene (HDPE), Low-Density Polyethylene (LDPE), Polyvinyl Chloride (PVC), Polypropylene (PP), and Polystyrene (PS). Feature selection techniques - CFS and principal component analysis (PCA) are applied to extract the essential discriminative features of each type of plastic. With the extracted features, KNN and support vector machine (SVM) models are developed respectively for plastic identification. Experimental results demonstrate that the CFS-KNN model achieves a success rate of 100% with a computational time of 40ms under laboratory conditions, outperforming the PCA-SVM, PCA-KNN and CFS-SVM models in terms of accuracy and computational efficiency. Feature selection ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Identification (biology) ; Correlation ; Selection (genetic algorithm) ; Feature (linguistics) ; Mathematics Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Food Supply Chain Traceability
Authors: Cody A. Nizinski | Alex Hagen | Carole Hall | James  Derek Tucker | Daniel Ries | Lucas E. Sweet | Richard A. Clark
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Multimodal data fusion for the joint estimation of plutonium oxide synthesis conditions Measure (data warehouse) ; Task (project management) ; Preprocessor ; Joint (building) ; Modalities ; Regression ; Data pre-processing ; Sensor fusion ; Data-driven Nuclear Materials and Properties ; Radioactive element chemistry and processing ; Machine Learning in Materials Science
Authors: Yuxuan Wang | Guoqiang Lan | Huicong Chen | J. J. Song
Source: ArXiv.org (preprint, 2025-07-22)
Field: Materials Science — Materials Chemistry

Thermophysical and Mechanical Properties Prediction of Rear-earth High-entropy Pyrochlore Based on Deep-learning Potential High-entropy pyrochlore oxides possess ultra-low thermal conductivity and excellent high-temperature phase stability, making them promising candidate for next-generation thermal barrier coating (TBC) materials. However, reliable predictive models for such complex and disordered systems remain challenging. Ab initio methods, although accurate in describing anharmonic phonon-phonon interactions, struggle to capture the strong inherent phonon-disorder scattering in high-entropy systems. Moreover, the limited simulation cell size, hundreds of atoms, cannot fully represent the configurational complexity of high-entropy phases. On the other hand, classical molecular dynamics (MD) simulations lack accurate and transferable interatomic potentials, particularly in multi-component systems like high-entropy ceramics. In this work, we employed Deep Potential Molecular Dynamics (DPMD) to predict the thermophysical and mechanical properties of rare-earth high-entropy pyrochlore oxide system. The deep-potential (DP) model is trained on a limited dataset from ab initio molecular dynamics (AIMD) calculations, enabling large-scale molecular dynamics simulations with on-the-fly potential evaluations. This model not only achieves high accuracy in reproducing ab initio results but also demonstrates strong generalizability, making it applicable to medium-entropy ceramics containing the same constituent elements. Our study successfully develops a deep potential model for rare-earth pyrochlore systems and demonstrates that the deep-learning-based potential method offers a powerful computational approach for designing high-entropy TBC materials. Pyrochlore ; Molecular dynamics ; Ab initio ; Anharmonicity ; Interatomic potential ; Thermal barrier coating ; Thermal conductivity ; Thermal Nuclear materials and radiation effects ; Nuclear Materials and Properties ; Geothermal Energy Systems and Applications
Authors: Lea E. Austermeier (21474806) | Karsten Voigt (17517156) | Alexander Böhme (1270689) | Nadin Ulrich (1646221)
Source: Figshare (article, 2025-06-03)
Field: Materials Science — Materials Chemistry

Prediction of MeltingPoints of Chemicals with a Data Augmentation-BasedNeural Network Approach The melting point (MP) of a chemical is an important physicochemical property that characterizes the transition from a solid to a liquid state. The MP is a key parameter in molecular design and relevant in many fields such as drug design and environmental science. Therefore, an accurate prediction of the MP is of huge interest. Here, we develop two graph convolutional neural network (GNN) models for the prediction of the MP: one where we do not apply a data augmentation strategy and one where we apply a data augmentation strategy. The models were developed on a data set containing 28,645 chemicals, where we removed duplicates and data points labeled as faulty. Then we split the data set into training, validation, and test sets. The model was trained on this initial data set and on a higher curated data set. Based on the data augmentation, we could enlarge the number of neurons in each of the two hidden layers in the GNN and reinforce the representation of large and complex molecules. We compared the influence of the curation step and the data augmentation and found that the curation step had no significant influence on the model performance, while the model could be improved by the application of data augmentation. With a consensus model, we achieved an rmse of 35.4 °C. Representation (politics) ; Data set ; Set (abstract data type) ; Artificial neural network ; Convolutional neural network ; Property (philosophy) ; Data point ; Data modeling ; Key (lock) ; Graph Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Harish V. Thakur | Asmita Das
Source:  (article, 2025-02-13)
Field: Chemistry — Analytical Chemistry

Comparative Analysis of in Silico Tools for Drug Side Effect Prediction In silico tools are being developed and used these days to make predictions in the field of medicine. They are being used in scientific research where they are helping to ease out the process of developing new techniques and products. Cancer research is also being benefitted from these tools. There are special algorithms developed which form basis of these in silico tools. Molecular Docking is one such tool that can be based on principles like Artificial Intelligence, Machine Learning etc. It is used to understand interactions between drugs and how do they impact the body, what makes the difference in the biologic system and how do these compounds actually cause side effects. Here is a review of Molecular Docking tools which can predict the interactions between two compounds on basis of their biding energy and other parameters. These tools will be analyzed on basis of predictions they make and on basis of this analysis, best tool will be used chosen for making adverse reaction analysis of Dabrafenib, which is an anti-cancer drug administered to melanoma patients. In silico ; Computer science ; Drug ; Data mining ; Pharmacology ; Chemistry ; Medicine Analytical Methods in Pharmaceuticals ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Shekhar Gaikwad | Thejas Kasilingam | Owais Ahmad | R. Mukherjee | Somnath Bhowmick
Source: Physical Review Materials (article, 2025-10-29)
Field: Materials Science — Materials Chemistry

Deep learning-driven prediction of microstructure evolution via latent space interpolation Phase-field models accurately simulate microstructure evolution, but their dependence on solving complex differential equations makes them computationally expensive. This work achieves a significant acceleration via a deep learning-based framework, utilizing a conditional variational autoencoder (CVAE) coupled with cubic spline interpolation and spherical linear interpolation (SLERP). We demonstrate the method for binary spinodal decomposition by predicting microstructure evolution for intermediate alloy compositions from a limited set of training compositions. First, using microstructures from phase-field simulations of binary spinodal decomposition, we train the CVAE, which learns compact latent representations that encode essential morphological features. Next, we use cubic spline interpolation in the latent space to predict microstructures for any unknown composition. Finally, SLERP ensures smooth morphological evolution with time that closely resembles coarsening. The predicted microstructures exhibit high visual and statistical similarity to phase-field simulations. This framework offers a scalable and efficient surrogate model for microstructure evolution, enabling accelerated materials design and composition optimization. Microstructure ; Interpolation (computer graphics) ; Spinodal decomposition ; Spline interpolation ; Autoencoder ; Maxima and minima ; Binary number Machine Learning in Materials Science ; Solidification and crystal growth phenomena ; Block Copolymer Self-Assembly
Authors: Esther Chioma Udochukwu | Okechukwu Onyelucheya | Anselem Orga | Innocent Ike
Source: NIPES Journal of Science and Technology Research (article, 2025-12-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optimization of Electrochemical Performance of Coconut Shell–Derived Activated Carbon via KHCO₃ Activation: Influence of Temperature, Time, and Ratio on Specific Capacitance Biomass-derived carbon materials have gained attention for their potential in energy storage applications, mainly due to their sustainability and cost-effectiveness. Among various sources, coconut shells offer high carbon content and are widely available. However, optimizing the activation process remains a key challenge in enhancing electrochemical performance. This study investigates the effects of particle size, activation temperature, activation time, and CSAC to potassium bicarbonate (KHCO₃) ratio on the specific capacitance of the electrode material. To better understand and predict the relationships between processing conditions and performance, a combination of statistical analysis and machine learning methods were used. These included correlation analysis, multivariate regression, Random Forest regression, and Response Surface Methodology (RSM). Results showed that activation temperature and time had the most significant influence, contributing to about 80% of the variance in capacitance. The best outcome of Run 52, was achieved using a 1:2 ratio (CSAC: KHCO3), with optimal activation conditions of 800 °C and 120 minutes, yielding a maximum actual specific capacitance of 298 F g⁻¹. The Run 52-CSAC attained 91.2 % capacitance retention at 1 A/g current density after 10,000 cycles. The Random Forest model performed well (R² = 0.913, RMSE = 8.21 F g⁻¹), closely aligned with regression results (R² = 0.904, p &lt; 0.001). RSM analysis also highlighted a nonlinear interaction between temperature and activation ratio. This integrated experimental and computational approach provides a reliable framework for optimizing activation processes in biomass-derived carbons. However, findings are limited to a single biomass type and activation method, suggesting the need for further studies on different materials and conditions to validate broader applicability. Capacitance ; Response surface methodology ; Activated carbon ; Carbon fibers ; Biomass (ecology) ; Specific surface area ; Activation energy ; Electrochemistry ; Particle size Supercapacitor Materials and Fabrication ; Adsorption and biosorption for pollutant removal ; Microbial Fuel Cells and Bioremediation
Authors: Lei Zhang | Pengyu Zhang | Mao Zhiyi | Wei Ziheng | Tianle Wang
Source: Research Review (article, 2025-02-20)
Field: Chemistry — Inorganic Chemistry

Study on the identification of steel slag mixed in fine aggregate for concrete based on color as feature elements The frequent quality problems caused by steel slag concrete construction have received extensive attention and attention from the construction industry.How to effectively prevent the mixing of steel slag in raw materials for concrete has become a difficult problem faced by the current industry.In this paper, the phase and chemical composition of steel slag were analyzed to clarify the characteristic identification elements of steel slags and common fine aggregate for concrete.With the content of high-iron steel slag particles mixed in common fine aggregate increasing, the color of the mixed samples deepens, the diffuse absorption intensity in the visible light and near-infrared light range gradually increases, and the main peak position of the grey value gradually decreases.Through deep learning analysis, the precision and recall of TransUNet model for the mixed sample containing common fine aggregate and steel slag particles can reach more than 90.00%.Therefore, based on the color as a characteristic identification element, it can effectively identify whether fine aggregates is mixed with steel slag, so as to ensure the safety and stability of concrete structures. Aggregate (composite) ; Feature (linguistics) ; Identification (biology) ; Slag (welding) ; Materials science ; Metallurgy ; Composite material Pigment Synthesis and Properties ; Concrete and Cement Materials Research ; Recycled Aggregate Concrete Performance
Authors: Abhishek Pawar | Bankat M. Patil | Rajgopal V Mutnali | Omkar S Powar | Ruby Dinakar
Source:  (article, 2025-06-09)
Field: Chemistry — Analytical Chemistry

Stability testing and shelf life prediction of pharmaceutical drugs (clarithromycin) using machine learning Stability testing is essential for predicting the shelf life of pharmaceutical drugs, ensuring they remain effective and safe under various storage conditions. Conventional stability testing methods, including real-time and accelerated studies, require significant time and resources. This paper introduces a machine learning-based framework leveraging linear regression and quadratic modeling to predict the shelf life of pharmaceutical products. By analyzing key factors such as temperature, humidity, light exposure, packaging materials, and chemical composition, the proposed approach achieves enhanced prediction accuracy and reduces the duration of stability studies. Experiments using real-world stability data demonstrate that the model achieves over 85providing pharmaceutical companies with a reliable and efficient alternative to traditional methods. The framework also supports faster decision making and regulatory compliance, potentially accelerating the product development lifecycle. Off the shelf ; Clarithromycin ; Computer science ; Machine learning ; Artificial intelligence ; Stability (learning theory) ; Medicine ; Internal medicine Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Chromatography ; Protein purification and stability
Authors: Yanjie Li | Jian Xu | Xueqing Chen | Lina Yu | Shiming Xiang | Weijun Li | Cheng-lin Liu
Source: arXiv (Cornell University) (preprint, 2025-12-23)
Field: Materials Science — Materials Chemistry

QE-Catalytic: A Graph-Language Multimodal Base Model for Relaxed-Energy Prediction in Catalytic Adsorption Adsorption energy is a key descriptor of catalytic reactivity. It is fundamentally defined as the difference between the relaxed total energy of the adsorbate-surface system and that of an appropriate reference state; therefore, the accuracy of relaxed-energy prediction directly determines the reliability of machine-learning-driven catalyst screening. E(3)-equivariant graph neural networks (GNNs) can natively operate on three-dimensional atomic coordinates under periodic boundary conditions and have demonstrated strong performance on such tasks. In contrast, language-model-based approaches, while enabling human-readable textual descriptions and reducing reliance on explicit graph -- thereby broadening applicability -- remain insufficient in both adsorption-configuration energy prediction accuracy and in distinguishing ``the same system with different configurations,'' even with graph-assisted pretraining in the style of GAP-CATBERTa. To this end, we propose QE-Catalytic, a multimodal framework that deeply couples a large language model (\textbf{Q}wen) with an E(3)-equivariant graph Transformer (\textbf{E}quiformer-V2), enabling unified support for adsorption-configuration property prediction and inverse design on complex catalytic surfaces. During prediction, QE-Catalytic jointly leverages three-dimensional structures and structured configuration text, and injects ``3D geometric information'' into the language channel via graph-text alignment, allowing it to function as a high-performance text-based predictor when precise coordinates are unavailable, while also autoregressively generating CIF files for target-energy-driven structure design and information completion. On OC20, QE-Catalytic reduces the MAE of relaxed adsorption energy from 0.713~eV to 0.486~eV, and consistently outperforms baseline models such as CatBERTa and GAP-CATBERTa across multiple evaluation protocols. Inverse ; Graph ; Adsorption ; Artificial neural network ; Reliability (semiconductor) ; Efficient energy use ; Energy (signal processing) ; Function (biology) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: P. H. Rivera | R. A. Montalvo
Source: Revista de Investigación de Física (article, 2025-12-31)
Field: Chemistry — Physical and Theoretical Chemistry

Métodos "tight binding" para el análisis topológico en materiales 2D The study of electronic, optical and magnetic properties involves Schrödinger and Heisenberg many body equations. Even for nanoscopic systems find solutions to theses system equations is a formidable task. In Chemistry, Linear Combination of Atomic Orbitals was developed for molecular systems studies. Using a heuristic approach to LCAO called Tight Binding, was developed in Physics to obtain initial insights for solid state matter properties. This approach involves a minimum computational infrastructure. In this pedagogical work, we analyze a square lattice toy model using tight binding approach to observe the evolution of eigenvalues and eigenstates of the system when we applied different interacctions such as intrinsic and Rashba Spin Orbit Coupling, the competition between them; and, the effect of magnetic and electric fields over the eigenvalues and eigenstates. We analyze the edge states of the system, the Berry phase and quantify the Chern number associated to conductivity. Also, we present the results of a deep learning analysis of these toy models, an initial results are shown. Eigenvalues and eigenvectors ; Tight binding ; Atomic orbital ; Ferromagnetism ; Square (algebra) ; Lattice (music) ; Phase (matter) ; Geometric phase Advanced Physical and Chemical Molecular Interactions ; Topological Materials and Phenomena ; Chemical and Physical Properties of Materials
Authors: Liang Xiao | Hanqi Wang | Xichen Zhou | Qianyong Zhu | Cheng Zhang | Wenlong Xiao | Ye Guo | Zhen Li | Yu Liu | Kang Peng | Lei Zheng | Hongbo Guo | Shiteng Zhao
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Revealing Cryo-loading Induced Twinning and its Impact on Titanium via Correlative Data-driven Mechanomicroscopy Crystal twinning ; Nanoindentation ; Titanium ; Characterization (materials science) ; Dislocation ; Hardening (computing) ; Electron backscatter diffraction ; Equiaxed crystals Microstructure and mechanical properties ; Metallurgy and Material Forming ; Microstructure and Mechanical Properties of Steels
Authors: Lee L
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

FUSIONNET-AI: An AI-Enabled Data Mining Algorithm for Multi-Information Chemometric Integration and Improved Analytical Accuracy Curse of dimensionality ; Dimensionality reduction ; Feature (linguistics) ; Pattern recognition (psychology) ; Feature selection ; Feature extraction ; Identification (biology) ; Principal component analysis ; Feature vector ; Enhanced Data Rates for GSM Evolution Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Machine Learning in Materials Science