jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Peikun Zheng | Olexandr Isayev
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

High-throughput electronic property prediction of cyclic molecules with 3D-enhanced machine learning Complex organic molecules play a pivotal role in bioactive compounds and organic functional materials, yet existing molecular datasets lack structural diversity for such systems, limiting the generalizability of machine learning (ML) models. This study introduces a high-quality dataset, Ring Vault, comprising 201 546 cyclic molecules, including monocyclic, bicyclic, and tricyclic systems, spanning 11 non-metallic elements. This dataset covers a wide chemical space and provides a robust foundation for molecular property prediction. Leveraging quantum mechanical (QM) calculations on a subset (36 000 molecules), we trained three ML models (Graph Attention Network, Chemprop, and AIMNet2) to predict five key electronic properties: HOMO-LUMO gap, ionization potential (IP), electron affinity (EA), and redox potentials (<i>E</i> <sub>ox</sub>, <i>E</i> <sub>red</sub>). The fine-tuned AIMNet2 model, incorporating 3D conformational information, outperformed 2D-based models, achieving <i>R</i> <sup>2</sup> values exceeding 0.95 and reducing mean absolute errors (MAEs) by over 30%. Principal component analysis (PCA) of AIMNet2 embeddings revealed intrinsic correlations between electronic properties and structural features, such as conjugation extent and functional group effects. This work establishes a robust framework for high-throughput screening and rational design of cyclic molecules, with applications spanning drug discovery, organic electronics, and energy materials. The dataset and methodology provide a foundation for exploring complex structure-property relationships and accelerating functional molecule discovery. Generalizability theory ; Limiting ; Chemical space ; Principal component analysis ; Molecule ; Property (philosophy) ; Ionization energy ; Quantitative structure–activity relationship ; Electronic structure ; Quantum chemical Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Inorganic and Organometallic Chemistry
Authors: Biniyam Kahsay Mezgebo | Ryan J. Chaffee | Luis R. Castellanos | Shoaib Ashraf | Jack Burke-Gaffney | Johann Pitout | Bogdan I. Iorga | M. Ethan MacDonald | Dylan R. Pillai
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-03-21)
Field: Materials Science — Materials Chemistry

You Only Look Once (YOLO) Based Machine Learning Algorithm for Real-Time Detection of Loop-Mediated Isothermal Amplification (LAMP) Diagnostics Abstract Loop-mediated isothermal amplification (LAMP) is a widely used rapid and affordable molecular DNA amplification method with minimal resource requirements. However, visual interpretation of results is subjective and prone to errors, leading to potential false-positive and negative results. To address this limitation, a machine-learning approach is proposed for automated LAMP classification based on digital images. The approach utilizes You Only Look Once (YOLOv8), a fast and robust object detection algorithm to locate and classify tubes within LAMP images, enabling automated categorization as positive or negative. The trained model achieved a high overall accuracy of 95.5% in classifying LAMP images into positive or negative. Additionally, the approach had a 98.0% precision and 92.7% recall for positive cases and 93.4% precision and 98.2% recall for negative cases, demonstrating its potential for real-time LAMP diagnosis and enhanced assay performance. This project demonstrated the platform’s suitability for real-time testing, offering an easy operation and rapid results. Loop-mediated isothermal amplification ; Loop (graph theory) ; Isothermal process ; Computer science ; Algorithm ; Artificial intelligence ; Physics ; Mathematics ; Chemistry ; Thermodynamics Machine Learning in Materials Science ; Fault Detection and Control Systems
Authors: Biqing Chen | Jiayin Gao | Haizhu Sun | Yinghan Zhao | Yan Liu | Xiaohong Qiu
Source: Microchimica Acta (article, 2025-11-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

SERS-powered precision: revolutionizing therapeutic drug monitoring with nanoscale sensitivity Therapeutic drug monitoring ; Personalized medicine ; Drug ; Drug discovery ; Precision medicine ; Microfluidics ; Targeted drug delivery Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Tereza Benešová | Kateřina Pokorná | Andreas Erlebach | Christopher J. Heard
Source: ChemRxiv (preprint, 2025-07-23)
Field: Materials Science — Materials Chemistry

Mobility and Sintering of Silica-Supported Platinum Clusters via Reactive Neural Network Potentials Supported Pt atoms and their sub-nanometre cluster counterparts are a promising avenue for the targeted, bottom-up development of high specific activity heterogeneous catalysts. However, they suffer from instability with respect to growth and deactivation via sintering. The atomistic processes which comprise the early stages of sintering are difficult to access, as the timescales are too long for traditional electronic structure calculation methods and too short for most experimental techniques to probe. Pinning via hydroxyl groups has been suggested as a means to stabilize small clusters, but the means and extent of this sintering resistance is currently unclear. In this work, neural network potentials (NNPs), trained at the dispersion-corrected metaGGA level of theory, are employed to probe the dynamic behaviour of sub-nanometre Ptx species (x = 1, 3, 15) on a model flat oxide support: silicatene. We observe that mobility is surprisingly lowest for the single Pt atom and highest for the larger cluster, which influences the sintering routes, leading to non-Ostwald ripening coalescence pathways. When a silanol defect is introduced to the surface, the clusters are stabilised by the defect via hydrogen transfer from the surface to the clusters, thereby suppressing sintering via electrostatic trapping. These findings provide a mechanistic underpinning of sintering routes and stabilisation mechanisms on a model non-reducible oxide support and provide insights into the mechanisms of sub-nanometre metal particle mobility at the atomistic scale. Sintering ; Platinum ; Artificial neural network ; Materials science ; Chemical engineering ; Chemistry ; Computer science ; Catalysis ; Composite material ; Organic chemistry ; Artificial intelligence ; Engineering Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Yinan Shu | Zoltán Varga | Dayou Zhang | Qinghui Meng | Aiswarya M. Parameswaran | Jian‐Ge Zhou | Donald G. Truhlar
Source: Journal of Chemical Theory and Computation (article, 2025-03-21)
Field: Materials Science — Materials Chemistry

Learning Multiple Potential Energy Surfaces by Automated Discovery of a Compatible Representation Creating analytic representations of multiple potential energy surfaces for modeling electronically nonadiabatic processes is a major challenge being addressed in various ways by the chemical dynamics community. In this work, we introduce a new method that can achieve convenient learning of multiple potential energy surfaces (PESs) and their gradients (negatives of the forces) for a polyatomic system. This new method, called compatibilization by deep neural network (CDNN), is demonstrated to be accurate and, even more importantly, to be automatic. The only required input is a database with geometries and potential energies. The method produces a matrix, called the compatible potential energy matrix (CPEM), that may be interpreted as the electronic Hamiltonian in an implicit nonadiabatic basis, and the analytic adiabatic potential energy surfaces and their gradients are obtained by diagonalization and automatic differentiation. We show that the CPEM, which is neither adiabatic nor necessarily diabatic, can be discovered automatically during the learning procedure by the special design of a CDNN architecture. We believe that the CDNN method will be very useful in practice for learning coupled PESs for polyatomic systems because it is accurate and fully automatic. Computer science ; Representation (politics) ; Data science ; Energy (signal processing) ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Welding Techniques and Residual Stresses
Authors: Dong-Jin Kim | Xiaoyu Wang | Peichen Zhong | David A. King | Théo Jaffrelot Inizan | Bingqing Cheng
Source: ArXiv.org (article, 2025-07-18)
Field: Materials Science — Materials Chemistry

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Most current machine learning interatomic potentials (MLIPs) rely on short-range approximations, without explicit treatment of long-range electrostatics. To address this, we recently developed the Latent Ewald Summation (LES) method, which infers electrostatic interactions, polarization, and Born effective charges (BECs), just by learning from energy and force training data. Here, we present LES as a standalone library, compatible with any short-range MLIP, and demonstrate its integration with methods such as MACE, NequIP, CACE, and CHGNet. We benchmark LES-enhanced models on distinct systems, including bulk water, polar dipeptides, and gold dimer adsorption on defective substrates, and show that LES not only captures correct electrostatics but also improves accuracy. Additionally, we scale LES to large and chemically diverse data by training MACELES-OFF on the SPICE set containing molecules and clusters, making a universal MLIP with electrostatics for organic systems including biomolecules. MACELES-OFF is more accurate than its short-range counterpart (MACE-OFF) trained on the same dataset, predicts dipoles and BECs reliably, and has better descriptions of bulk liquids. By enabling efficient long-range electrostatics without directly training on electrical properties, LES paves the way for electrostatic foundation MLIPs. Electrostatics ; Set (abstract data type) ; Dipole ; Benchmark (surveying) ; Ewald summation ; Charge (physics) ; Electric potential energy ; Coulomb ; Scale (ratio) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Gurukarthik Babu Balachandran | Muthu Eshwaran Ramachandran | Hariharasudhan Thangaraj | Pravin Murugesan
Source: Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science (article, 2025-04-11)
Field: Materials Science — Materials Chemistry

Unleashing the future of wind energy systems using SiO <sub>2</sub> and TiO <sub>2</sub> based insulation: A deep dive into Artificial Intelligence and Response Surface Methodology This study presents the design and optimization of an induction generator for wind energy system applications. Artificial neural networks (ANNs) with feedforward backpropagation and response surface methodology (RSM) are widely used for modeling complex engineering processes. In this work, wt.%, stirring speed, and stirring time were optimized using ANN and RSM to achieve the desired response. The developed models demonstrated high predictive accuracy, with significant R 2 values of 0.9982 for the RSM model and 0.99686 for the ANN model. For SiO 2 samples, the optimal parameters were determined as wt.% of 2.15, stirring speed of 617, and stirring time of 16, while for TiO 2 samples, the optimum values were wt.% of 6.96, stirring speed of 823, and stirring time of 20. This parameter optimization is crucial as it directly affects generator components’ properties, which affect the efficiency and reliability of power conversion. The improved material properties make induction generators more effective for wind energy applications because they reduce energy loss and enhance durability. These findings provide a systematic approach to improving material selection and processing parameters, contributing to enhanced performance and efficiency in induction generators for wind energy applications. The developed models serve as valuable tools for optimizing generator design, ultimately supporting more reliable and efficient renewable energy systems. The efficiency and dependability of induction generators are enhanced by the optimized parameters, which are essential for raising the effectiveness and sustainability of wind energy systems. These results suggest a methodical approach to material optimization to enhance electrical performance, which enriches the technology of renewable energy. Energy (signal processing) ; Wind power ; Aerospace engineering ; Environmental science ; Systems engineering ; Artificial intelligence ; Computer science ; Materials science ; Engineering ; Physics ; Electrical engineering High voltage insulation and dielectric phenomena
Authors: Ashok Srivastava | Soumya Majumder | Torthi Ravichandra | R. Vidhya | Murugananth Gopal Raj | S. P. Santhoshkumar
Source:  (article, 2025-05-09)
Field: Chemistry — Analytical Chemistry

Leveraging Artificial Neural Networks for Fruit Quality Prediction: Advancements in Food Technology and Quality Control For the food and agriculture industries to guarantee consumer happiness and cut waste, accurate fruit quality prediction is essential. Artificial Neural Networks (ANNs) are used to evaluate fruit eminence based on characteristics such as color, consistency, volume, and chemical content is investigated in this paper. Current models do a good job of classifying fresh fruits, but they are not very good at spotting rotten ones, which is crucial for reducing waste and preserving quality control. This study fills this gap by compiling a dataset of both fresh and rotting fruits in order to establish a comprehensive strategy. To distinguish between them, the ANN model is trained on a variety of photos. Additionally, fruit maturity is evaluated using color changes and OpenCV, a popular computer vision toolkit. Fruit quality assessment and classification are improved by this integrated method. The suggested approach helps with fruit sorting and guarantees that buyers receive high-quality product, which benefits sectors like retail and agriculture. The ultimate objective of this project is to increase sustainability and efficiency in the food supply chain by automating quality monitoring throughout the application of state-of-the-art ML algorithms. Artificial neural network ; Computer science ; Quality (philosophy) ; Artificial intelligence ; Control (management) ; Machine learning Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Paulo Gonçalves Simões | Alessandro M.B. Martins | Márcio Felipe Oliveira | Julio Cesar Siqueira | Aderval S. Luna | Licarion Pinto
Source: Food Control (article, 2025-12-27)
Field: Chemistry — Analytical Chemistry

Coupled models for food fraud evaluation of unripe banana flour: Enhanced prediction with XGBoost and variable importance assessed by SHAP Interpretability ; Adulterant ; Chemometrics ; Partial least squares regression ; Preprocessor ; Pattern recognition (psychology) ; Linear discriminant analysis Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Dye analysis and toxicity
Authors: Sumit Rai | Sanvi Gupta | Shraddha Sajwan | Vandemataram Aditi | Chhaya Ravi Kant
Source: Journal of Energy Storage (article, 2025-12-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Artificial intelligence models for supercapacitor performance prediction in energy storage Interpretability ; Artificial neural network ; Support vector machine ; Energy storage ; Curse of dimensionality ; Random forest ; Benchmark (surveying) ; Renewable energy ; Supercapacitor Supercapacitor Materials and Fabrication ; MXene and MAX Phase Materials ; Nanoporous metals and alloys
Authors: Wenying Li | Jing‐Chun Feng | Kexuan Wu | Si Zhang | Yuelu Jiang | Guozhong Wu
Source: Energy & Fuels (article, 2025-02-03)
Field: Chemistry — Analytical Chemistry

Equilibrium and Temporal Evolution of Asphaltene Nanoaggregates during Dynamic Heteroaggregation with Polysaccharides Asphaltene blockage and microbiologically induced corrosion (MIC) in oil pipes pose substantial flow assurance challenges, but their interrelationship remains poorly understood despite extensive individual research efforts. This study utilized molecular dynamics simulation and machine learning techniques to elucidate the effects of alginate, a representative biofilm polysaccharide associated with MIC, on asphaltene aggregation. Results underscored the ability of alginates to disperse large asphaltene nanoaggregates into smaller ones with more pronounced dispersion effects at higher concentrations. This was achieved through forming asphaltene–alginate heteroaggregates wherein asphaltene nanoaggregates were tightly wrapped by cross-linked alginate chains stabilized by Na+ bridging. The presence of alginates significantly increased the solvent accessible surface area (SASA) of asphaltenes, while the heteroaggregation process resulted in abundant hydroxyl and carboxyl groups coating the surface of the asphaltene nanoaggregates. Furthermore, this study showed that the time-varying asphaltene aggregation parameters such as nanoaggregate number, intermolecular contact number, and SASA could be accurately predicted by alginate structural properties using optimized random forest models built from 3636 data sets extracted from molecular trajectories (R2 = 0.9628–0.9827). This enabled fast prediction of asphaltene aggregation when the polysaccharide composition and concentration changed during the MIC process. These findings provided theoretical support for developing strategies to address asphaltene-related issues in the presence of a biofilm polysaccharide. Polysaccharide ; Asphaltene ; Chemical engineering ; Chemistry ; Organic chemistry ; Engineering Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Surfactants and Colloidal Systems
Authors: Wei‐Hsin Chen | Argel A. Bandala | Ding Luo | Aristotle T. Ubando | Manuel Carrera Uribe | Maxine Camille O. Mallari
Source: Energy Conversion and Management X (article, 2025-08-23)
Field: Materials Science — Materials Chemistry

A review of next-generation thermoelectric generators: Advanced geometric and nanomaterial optimization and intelligent performance enhancement • TEG designs are improved with AI, nanomaterials, and advanced geometric optimization. • Segmented and asymmetrical TEGs increase efficiency and power output. • ANNs and DNNs enable fast and accurate TEG design prediction and tuning. • Hybrid organic–inorganic materials enhance thermoelectric performance. • Nanomaterials reduce thermal conductivity, boosting TEG effectiveness. The demand for sustainable energy solutions has accelerated the development of thermoelectric generators (TEGs) as an effective technology for harvesting waste heat. TEGs employ the Seebeck effect to convert thermal energy into electricity. They offer advantages such as solid-state operation, scalability, and minimal maintenance. However, their widespread implementation is hindered by low energy conversion efficiency. This review comprehensively analyzes state-of-the-art in TEG technology, focusing on geometric optimization, material enhancements, and AI-driven performance improvements. Innovations in TEG designs, including segmented, variable-geometry, asymmetrical, and multistage architectures, are examined in relation to their impact on power output and efficiency. In addition, the integration of artificial intelligence (AI), computational fluid dynamics (CFD), and nanomaterials in predictive modeling and real-time optimization is discussed. AI-driven strategies such as evolutionary computation and machine learning have demonstrated significant potential in optimizing TEG configurations for maximum efficiency. Despite recent breakthroughs, challenges persist in large-scale implementation, fabrication complexity, and cost-effectiveness. Future research should prioritize the development of high-performance thermoelectric materials, advanced manufacturing techniques, and multi-physics simulation models to facilitate the next generation of TEG applications in waste heat recovery, automotive applications, and renewable energy generation. This review highlights emerging trends and outlines strategic research directions to accelerate the adoption of TEGs in sustainable energy generation. Nanomaterials ; Thermoelectric effect ; Thermoelectric generator ; Materials science ; Nanotechnology ; Computer science ; Process engineering ; Systems engineering ; Mechanical engineering ; Engineering ; Physics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Radiation and Cooling Technologies
Authors: Katrul Nadia Basri
Source: Halalsphere (review, 2025-07-31)
Field: Chemistry — Analytical Chemistry

Integration of spectroscopy and chemometric analysis for food authentication: A review The high demand in the food sector necessitates an authentication process to verify products and prevent food fraud. In the food industry, applying spectroscopy techniques combined with chemometric analysis has become an efficient tool for authentication, thanks to its rapid results, cost-effectiveness, and reliability. Among the spectroscopy techniques reviewed in this paper are near-infrared (NIR), Fourier transform infrared (FTIR), ultraviolet (UV), and Raman spectroscopy. This paper reviews recent spectroscopy applications to food, such as oils, agricultural products, and beverages. This paper reviews various types of classification and regression algorithms in chemometric analysis to demonstrate the capabilities of these algorithms for use in food authentication processes. The chemometric model can accurately identify authentic samples with minimal risk of misclassification. This review presents a comparative synthesis of algorithms for various types of food samples, highlighting the performance of predictive algorithms. This review will provide a practical framework for researchers seeking to develop a robust predictive system for food authentication using spectroscopy and chemometrics. The review discusses the emerging trend of integrating spectroscopy into real-time authentication systems, particularly in the application of data fusion and deep learning techniques. Spectroscopy ; Authentication (law) ; Computer science ; Analytical Chemistry (journal) ; Environmental science ; Chemistry ; Biological system ; Environmental chemistry ; Physics ; Biology ; Astronomy ; Computer security Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food