jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lin Hu | Zhaorong Zhou | Guozhu Jia
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

A One-Shot Automated Framework Based on Large Language Model and Automl: Accelerating the Design of Porous Carbon Materials and Carbon Capture Optimization Carbon fibers ; Computer science ; Porosity ; Artificial intelligence ; Materials science ; Algorithm ; Composite material Machine Learning in Materials Science ; Graph Theory and Algorithms ; Geological Modeling and Analysis
Authors: Benjamin Ilo | Abraham Badjona | Yogang Singh | Alex Shenfield | Hongwei Zhang
Source: Processes (article, 2025-11-19)
Field: Chemistry — Analytical Chemistry

Artificial Intelligence in Rice Quality and Milling: Technologies, Applications, and Future Prospects The global demand for high-quality rice necessitates advancements in milling technologies and quality assessment techniques that are rapid, accurate, and scalable. Traditional methods of rice evaluation are time-consuming and subjective, and are increasingly being replaced by artificial intelligence driven solutions that offer non-destructive, real-time monitoring capabilities. This review presents a comprehensive synthesis of current AI applications including machine vision, deep learning, spectroscopy, thermal imaging, and hyperspectral imaging for the assessment and classification of rice quality across various stages of processing. Major emphasis is put on the recent advances in convolutional neural networks (CNNs), YOLO architectures, and Mask R-CNN models, and their integration into industrial rice milling systems is discussed. Additionally, the review highlights next steps, notably designing lean AI architectures suitable for edge computing, hybrid imaging systems, and the creation of open-access datasets. Across recent rice-focused studies, classification accuracies for grading and varietal identification are typically ≥90% using machine vision and CNNs, while NIR–ANN models for physicochemical properties (e.g., moisture/protein proxies) commonly report strong fits (R2≈0.90–0.99). End-to-end detectors/segmenters (e.g., YOLO/YO-LACTS) achieve high precision suitable for near real-time inspection. These results indicate that AI-based approaches can substantially outperform conventional evaluation in both accuracy and throughput. Hyperspectral imaging ; Convolutional neural network ; Applications of artificial intelligence ; Artificial neural network ; Quality (philosophy) ; Identification (biology) ; Deep learning ; Traceability Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Listeria monocytogenes in Food Safety
Authors: Miguel A. García‐Castro | Fausto Díaz-Sánchez | Maura Cárdenas-García | Jesús A. Arzola-Flores | Vladimir Carranza-Téllez
Source: ACS Omega (article, 2025-06-10)
Field: Chemistry — Organic Chemistry

Theoretical Study of Absolute Entropy, Entropy of Formation, and Gibbs Energy of Formation of Two Novel Macromolecules Obtained by the Solid State In this study, we present a solid-state reaction that enables the novel synthesis of diimidetricarboxylic acids (DITAs) in good yields without using harmful solvents. The synthesis route offers advantages, including satisfactory efficiency and easy product purification by sublimation. The reaction utilizes trimellitic anhydride (TMA) with 3,4- and 3,5-diaminobenzoic acid to obtain the desired DITAs. Products were characterized using differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR), proton nuclear magnetic resonance (<sup>1</sup>H NMR), and mass spectrometry (MS). The enthalpies of formation in the gas phase of DITAs (Δ <sub><i>f</i></sub> <i>H</i>°) were calculated, occupying absolute entropies (<i>S</i>°), entropies of formation (Δ <sub><i>f</i></sub> <i>S</i>°), and Gibbs energies of formation (Δ <sub><i>f</i></sub> <i>G</i>°). These properties were determined using the hybrid method B3LYP functional and the computational technique machine learning. Our theoretical studies revealed that it is possible to predict thermochemical properties with machine learning as accurately as with density functional theory (DFT). Gibbs free energy ; Entropy (arrow of time) ; Thermodynamics ; Macromolecule ; Statistical physics ; Solid-state ; Chemistry ; Physics ; Physical chemistry ; Biochemistry Chemical Thermodynamics and Molecular Structure ; Phase Equilibria and Thermodynamics ; thermodynamics and calorimetric analyses
Authors: Yuzhuo Wang | Yixin Li | Leteng Lin
Source: Materials & Design (article, 2025-11-20)
Field: Materials Science — Biomaterials

Integration of image processing and artificial neural networks (ANN) modeling for prediction of electrospun poly(ε-caprolactone) fibre diameters • DiameterJ shows better accuracy and throughput in fibre analysis than SIMPoly. • ANN exhibits excellent predictive performance with R 2 > 0.97 and errors <4 %. • ANN outperforms RSM in generalizability and reliability for diameter prediction. • Molecular weight and concentration are the dominant factors affecting diameter. • Image processing combined with machine learning aids fibre morphology prediction. Electrospun nanofibrous membranes are widely used in biomedical, filtration, and energy applications, where fibre diameter plays a key role in determining membrane morphology and performance. Conventional manual measurement of fibre diameters from scanning electron microscope (SEM) images is inefficient and subject to human error. This study compared two image-processing tools, DiameterJ and SIMPoly, to improve measurement efficiency and accuracy of fibre diameter. DiameterJ was selected for its higher reliability and batch-processing capability. Using DiameterJ, 144 datasets were collected and used to train an artificial neural network (ANN) model with four electrospinning parameters (molecular weight, solution concentration, flow rate, and tip-to-collector distance) as inputs. The ANN model showed high predictive accuracy, with correlation coefficients exceeding 0.97 and prediction errors below 4%. A response surface methodology (RSM) model was also developed for comparison, but showed limited predictive capability for unseen conditions, with errors up to 28.57%. The ANN exhibited superior reliability and generalizability. Index of relative importance (IRI) and contour analyses revealed molecular weight and concentration as dominant factors. The proposed integration of automated image analysis with ANN provides a data-driven and scalable framework for intelligent design and optimisation of electrospun materials, with potential applicability across diverse fibrous manufacturing systems. Artificial neural network ; Reliability (semiconductor) ; Generalizability theory ; Image processing ; Electrospinning ; Scanning electron microscope ; Approximation error ; Deep learning ; Pattern recognition (psychology) Electrospun Nanofibers in Biomedical Applications ; Advanced Cellulose Research Studies ; Natural Fiber Reinforced Composites
Authors: Qizhang Huang (4500037) | Zitong Xu (17054962) | Xueyu Gan (21539104)
Source: Figshare (article, 2025-06-13)
Field: Materials Science — Materials Chemistry

Facile Discoveryof Pristine MoS<sub>2</sub> MagneticOrdering via Machine Learning Prediction and DFT Validation Understanding the magnetic ordering in molybdenum disulfide (MoS<sub>2</sub>) is crucial for the advancement of novel magnetic devices, given the fascinating physical and chemical properties of MoS<sub>2</sub>. Our approach combines a preliminary prediction from machine learning (ML) models, trained on a limited data set of only 390 data points, with rigorous validation through density functional theory (DFT) calculations. This hybrid method provides valuable insights into the underlying mechanisms driving the predictions. The analysis facilely reveals a complex relationship between crystal structure and net magnetization in pristine MoS<sub>2</sub>, demonstrating that an increased cell volume, achieved through plane expansion in a trigonal crystal structure, is associated with the emergence of magnetization in MoS<sub>2</sub>. Specifically, expanding the cell volume weakens atomic interactions, thereby facilitating the formation of sufficient energy states while simultaneously reducing electron saturation near the Fermi level. This efficient ML-DFT hybrid methodology offers a streamlined path for tailored material design, as exemplified by our exploration of pristine MoS<sub>2</sub> for spintronic applications. Magnetization ; Spintronics ; Density functional theory ; Molybdenum disulfide ; Bridging (networking) ; Crystal (programming language) ; Saturation (graph theory) 2D Materials and Applications ; Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties
Authors: И. С. Никитин
Source: Věda a perspektivy (article, 2025-07-29)
Field: Materials Science — Materials Chemistry

ARTIFICIAL INTELLIGENCE FOR PREDICTION PROPERTIES AND DISCOVERY OF NEW SUPERHARD MATERIALS Based on the analysis of modern research directions and best practices in the use of artificial intelligence in the field of superhard materials, the most successful machine learning models for predicting properties and searching for new superhard materials have been established. Computer science ; Artificial intelligence ; Materials science Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Rare-earth and actinide compounds
Authors: Vivin Vinod | Peter Zaspel
Source: The Journal of Chemical Physics (article, 2025-07-11)
Field: Materials Science — Materials Chemistry

Benchmarking data efficiency in Δ-ML and multifidelity models for quantum chemistry The development of machine learning (ML) methods has made quantum chemistry (QC) calculations more accessible by reducing the computational cost incurred in conventional QC methods. This has since been translated into the overhead cost of generating training data. Increased work in reducing the cost of generating training data resulted in the development of Δ-ML and multifidelity machine learning methods, which use data at more than one QC level of accuracy, or fidelity. This work compares the data costs associated with Δ-ML, multifidelity machine learning (MFML), and optimized MFML in contrast with a newly introduced MultifidelityΔ-Machine Learning (MFΔML) method for the prediction of ground state energies, vertical excitation energies, and the magnitude of the electronic contribution of molecular dipole moments from the multifidelity benchmark dataset QeMFi. This assessment is made on the basis of the training data generation cost associated with each model and is compared with the single fidelity kernel ridge regression case. The results indicate that the use of multifidelity methods surpasses the standard Δ-ML approaches in cases of a large number of predictions. In applications where only a few numbers of predictions/evaluations are to be made using ML models, the herein developed MFΔML method is shown to provide an added advantage over conventional Δ-ML. Computer science ; Benchmark (surveying) ; Overhead (engineering) ; Regression ; Fidelity ; Kernel (algebra) ; Benchmarking ; Machine learning ; Artificial intelligence ; Algorithm ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Paulo M. Simon | Yijia Xu | Amandeep Kaur
Source: Advanced Sensor Research (article, 2025-09-26)
Field: Chemistry — Spectroscopy

Chemical Approaches for Array Based Detection of Amyloids Abstract Amyloids are protein aggregates implicated in both physiological functions and pathological conditions, including neurodegenerative diseases. Their polymorphic nature, dynamic aggregation behavior, and isoform complexity present significant challenges for detection and characterization. Traditional lock‐and‐key sensing methods often fall short in capturing this heterogeneity. Array‐based sensing has emerged as a powerful alternative, leveraging cross‐reactive sensors and multivariate data analysis to generate distinct response patterns or “fingerprints” for amyloids. This review highlights recent advances in arrays based on chemical sensors including colorimetric, fluorescent, and nanoparticle‐based, capable of discriminating amyloid isoforms, aggregation states, and polymorphs with high sensitivity and accuracy. The integration of machine learning techniques and neural networks, which enhance pattern recognition and predictive accuracy, even in complex biological matrices, is discussed. Notably, multidimensional approaches expand the analytical power of single‐sensor systems by exploiting excitation/emission spectral diversity. These innovations underscore the potential of array‐based platforms for early diagnosis, mechanistic studies, and therapeutic monitoring of amyloid‐related diseases. As the field evolves, combining sensor diversity with advanced computational tools promises to transform amyloid detection into a scalable and clinically relevant technology. Field (mathematics) ; Scalability ; Sensitivity (control systems) ; Focus (optics) ; Amyloid β ; Artificial neural network Molecular Sensors and Ion Detection ; Electrochemical sensors and biosensors ; Alzheimer's disease research and treatments
Authors: David Mareček | Andrew Nelson | Erwin Pfeiler | Maximilian Eder | Florian Bertram | Stefan Kowarik
Source: DESY Publication Database (PUBDB) (Deutsches Elektronen-Synchrotron) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

High-speed x-ray reflectometry for characterization of thin film growth at high deposition rates We present a high-throughput, real-time study of PTCDI-C8thin film growth using quick x-ray reflectivity (qXRR) with 12 ms time resolution, combined with machine learning-based analysis. In situ qXRR enables monitoring of vacuum deposition at growth rates from 1 to 30Å/s, accessing a previously unexplored regime in molecular beam deposition. To efficiently analyze the resulting ∼20000reflectivity curves, we employ a convolutional neural network trained on a physics-informed multilayer model. This approach robustly extracts key structural parameters—including thickness, roughness, and crystalline versus amorphous content—from noisy data. We quantify interface roughness as a function of both film thickness and growth rate, identifying rapid roughening with a scaling exponent of 𝛽=0.62with film thickness and a secondary scaling exponent of 𝛾=0.21with growth rate. Additionally, we observe a reduction in the coherently ordered film thickness and a rise in amorphous content at higher deposition rates. These results demonstrate that combining qXRR with machine learning provides quantitative access to fast kinetic growth processes, offering a powerful tool for in situ characterization and morphological control in organic thin film fabrication. Amorphous solid ; Thin film ; Deposition (geology) ; Characterization (materials science) ; Reflectometry ; Scaling ; Surface finish ; In situ Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Thin-Film Transistor Technologies
Authors: Andreas Burger | Luca Thiede | Alán Aspuru-Guzik | Nandamudi L. Vijaykumar
Source: ArXiv.org (preprint, 2025-09-10)
Field: Materials Science — Materials Chemistry

DEQuify your force field: More efficient simulations using deep equilibrium models Machine learning force fields show great promise in enabling more accurate molecular dynamics simulations compared to manually derived ones. Much of the progress in recent years was driven by exploiting prior knowledge about physical systems, in particular symmetries under rotation, translation, and reflections. In this paper, we argue that there is another important piece of prior information that, thus fa,r hasn't been explored: Simulating a molecular system is necessarily continuous, and successive states are therefore extremely similar. Our contribution is to show that we can exploit this information by recasting a state-of-the-art equivariant base model as a deep equilibrium model. This allows us to recycle intermediate neural network features from previous time steps, enabling us to improve both accuracy and speed by $10\%-20\%$ on the MD17, MD22, and OC20 200k datasets, compared to the non-DEQ base model. The training is also much more memory efficient, allowing us to train more expressive models on larger systems. Exploit ; Base (topology) ; Artificial neural network ; Deep learning ; Homogeneous space ; Knowledge base ; Work (physics) ; Deep neural networks Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: Юрій Ковальов | Віктор Аулін | Сергій Григорович Ковальов | Олександр Вікторович Кузик | Андрій Вікторович Гриньків
Source: The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy (article, 2025-12-30)
Field: Materials Science — Materials Chemistry

ЦИФРОВІЗАЦІЯ МАТЕРІАЛОЗНАВСТВА Й НАПРЯМИ ВИКОРИСТАННЯ ШТУЧНОГО ІНТЕЛЕКТУ Problem. The growing complexity of materials design, the expansion of high-dimensional compositional spaces, and the demand for sustainable innovation have exposed the limitations of traditional experimental approaches in materials science. These methods are often time-consuming, resource-intensive, and insufficiently scalable to meet the challenges of modern research and development. Goal. The aim of this study is to identify current directions in the development of materials science under conditions of digital transformation, with a particular focus on the application of artificial intelligence (AI) methods to enhance scientific approaches, model material properties, and support sustainability principles. Methodology. The research involves a com- parative analysis of leading digital platforms– Materials Project, NOMAD Repository, and Citrine Informatics–that integrate machine learning algo- rithms, simulation tools, and data management sys- tems. Case studies from recent publications (2021– 2025) are used to illustrate practical applications of AI in energy materials, polymers, and alloys. Results. Key areas of AI application in materials science have been identified, including virtual screening, active learning, interpretable modeling, and automated composition optimization. The study evaluates the prospects of digital transformation in the field, particularly in the context of Ukraine’s international integration and alignment with sustainable develop- ment goals. Originality. The paper substantiates the strategic role of materials informatics as a new research paradigm that combines AI, computational methods, and open science. It proposes approaches for integrating Ukrainian materials data into global platforms and outlines a roadmap for national infrastructure development. Practical Value. The findings can inform the creation of interdisciplinary educational programs, the development of a national materials informatics ecosystem, and the implementation of AI-driven innovation in critical sectors such as energy, medicine, defense, and environmental technologies. Sustainability ; Context (archaeology) ; Digital transformation ; Big data ; Sustainable development ; Informatics ; Open research Machine Learning in Materials Science ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry ; Catalysis and Oxidation Reactions
Authors: Paula Marielle Ababao | Ian B. Benitez
Source:  (article, 2025-08-26)
Field: Materials Science — Materials Chemistry

Review of Artificial Intelligence Applications in Performance Prediction of Advanced Energy Materials Artificial Intelligence (AI) is transforming the prediction and optimization of advanced energy materials by enabling accurate, scalable modeling beyond traditional methods. This review evaluates recent AI applications—including Graph Neural Networks (GNNs), Convolutional and Recurrent Neural Networks (CNNs, RNNs), tree-based ensembles, and Gaussian Process Regression (GPR)—for forecasting performance metrics such as overpotential, conductivity, capacity, and degradation. GNNs achieved R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> > 0.90 in structure-sensitive tasks; LSTM models predicted battery degradation with <10% error; and tree-based models balanced accuracy (MAE < 0.15 V) with interpretability. GPR excelled in low-data regimes via uncertainty quantification. Hybrid and physics-informed models improved generalizability and data efficiency. While challenges remain in data quality and integration with experiments, emerging strategies like autonomous labs and generative design offer promising advances. This review provides comparative benchmarks and highlights pathways for robust AI-driven materials discovery. Artificial neural network ; Performance prediction ; Kriging ; Scalability ; Gaussian process ; Convolutional neural network ; Predictive modelling ; Robustness (evolution) Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advanced battery technologies research
Authors: Deqing Xue | Bueryi Shen | Guojun Zhang | Ruihao Yuan
Source: Progress in Natural Science Materials International (article, 2025-02-20)
Field: Materials Science — Materials Chemistry

Multi-objective optimization for shape memory alloys with large recoverable strain and low hysteresis Materials science ; Shape-memory alloy ; Hysteresis ; Strain (injury) ; Composite material ; Condensed matter physics Shape Memory Alloy Transformations ; High Entropy Alloys Studies ; Topology Optimization in Engineering
Authors: Karoline Schjelde | Oscar Berlin Obel | Andreas Erbs Hillers‐Bendtsen | Kurt V. Mikkelsen
Source: Scientific Reports (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Semi-automated screening of azobezenes for solar energy storage using extended tight binding methods In the face of the pressing climate change crisis, Molecular Solar Thermal Energy Storage (MOST) Systems offer a promising avenue for efficient energy storage. This study focuses on the potential of systems based on azobenzene and gives a comprehensive framework for assessing unique azobenzene variations for MOST applications. A high-throughput screening process, underpinned by semi-empirical extended tight binding methods, has been developed to enable exploration of the vast chemical space of azobenzenes. The codebase for the established screening procedure, including methodologies and tools, is organized and shared through a GitHub repository ensuring transparency and reproducibility. We test our high throughput screening procedure on 37,729 azobenzene derivatives and highlight that it is robust enough to facilitate subsequent studies that will dive deeper into the potential of azobenzenes in MOST applications. Future endeavors will focus on expanding the dataset, correlating energies with higher-level calculations, and harnessing advanced statistical and machine learning techniques to optimize the selection and performance of azobenzenes in MOST systems. Computer science Photochromic and Fluorescence Chemistry ; Organic Electronics and Photovoltaics ; Photochemistry and Electron Transfer Studies
Authors: Zhengfang Qian | Guangwu Zhang | Zhizhu Lai | Ayaka Hanai | Yixin Xu | Guang Wang | Lü Yang | Jiaqi Gu | Yanguang Zhou | Takahiko Yanagitani | Junjun Jia | Qiye Zheng
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Tailoring Thermal Transport in (Sc,Yb)AlN Thin Films to the Glassy Limit Thin film ; Amorphous solid ; Phonon ; Thermal conductivity ; Wurtzite crystal structure ; Ternary operation ; Ionic bonding ; Lattice (music) Thermal properties of materials ; Thermodynamic and Structural Properties of Metals and Alloys ; GaN-based semiconductor devices and materials