jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yifan Wang | Debin Wang | Jin Gao | Zhang-jin Meng | Jianxin Hou | Jianqiang Wang | Xianpeng Wang
Source: Corrosion Science (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Accelerated discovery of corrosion-resistant multi-principal element alloys via data-augmented machine learning Corrosion ; Alloy ; Bayesian optimization ; Oxide ; Expansive ; Function (biology) ; Work (physics) ; Pitting corrosion Machine Learning in Materials Science ; High Entropy Alloys Studies ; High-Temperature Coating Behaviors
Authors: A. Sreeram | J. Kathirvelan
Source: Food Production Processing and Nutrition (article, 2025-10-13)
Field: Chemistry — Analytical Chemistry

Non-invasive detection and classification of chemically ripened mangoes and bananas through multiple deep learning frameworks using real-time dataset Abstract Chemically ripened mangoes and bananas are increasingly common worldwide and pose significant health risks due to the presence of carcinogens and other harmful substances. Owing to their cumbersome processes, the existing gold standard laboratory-based techniques for discriminating naturally or chemically ripened fruit often face challenges. This study aims to overcome these limitations by developing an onsite device specifically designed to detect chemically ripened mangoes and bananas to provide a faster and more cost-effective solution. This research uses advanced computer vision (CV) and deep learning (DL) techniques to detect and analyze chemically ripened mangoes and bananas. This research work employed several models, including K-nearest neighbor (KNN), random forest, support vector machine (SVM), convolutional neural networks (CNNs), and regional CNNs. In this study, the authors created their own real-time dataset for both naturally and chemically ripened mangoes and bananas. The proposed deep learning and machine learning models were trained and tested on a custom dataset of both fruits mango and banana images to discriminate chemically ripened fruits in an effective manner. Among all the models, the CNN achieved the highest accuracy of 93.24% and 96.25%, demonstrating its superior capability for this application. To scale up this approach, the authors implemented the system in real time via the Raspberry Pi board and a Pi camera. This prototype was instrumental for the authors to capture live images of fruits and process them via trained models to detect chemically ripened fruits. This approach enables efficient and accurate real-time detection, making this system feasible for practical applications. This work has the potential to leverage CV and DL techniques to combat fruit adulteration, providing a reliable and automated solution for ensuring food safety. The findings of this work infer that a CNN can accurately detect adulterated fruits, making it a promising tool for future developments in this field. Graphical Abstract Deep learning ; Convolutional neural network ; Support vector machine ; Process (computing) ; Pattern recognition (psychology) ; Deep neural networks Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Yan Zhang | Fei Gao | Guofeng Cui | Kuang Ya-fei | Zong Shutong | Fenghua Chen | Hongxia Liu | Zhigang Sun | Shen Baogen
Source: Journal of Magnetism and Magnetic Materials (article, 2025-05-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning of heavy rare earth-based low-temperature magnetocaloric materials under complex classification methods Magnetic refrigeration ; Rare earth ; Materials science ; Earth (classical element) ; Condensed matter physics ; Physics ; Metallurgy ; Magnetization ; Magnetic field Magnetic and transport properties of perovskites and related materials ; Multiferroics and related materials ; Machine Learning in Materials Science
Authors: Minglei Zhang | Jian Zeng | Xiaoya Chen | Quanan Li | Zheng Wu
Source: Journal of Materials Science (article, 2025-06-26)
Field: Materials Science — Biomaterials

Machine learning-based phase design of Mg-Re alloy LPSO phase Materials science ; Alloy ; Phase (matter) ; Solid mechanics ; Metallurgy ; Composite material Magnesium Alloys: Properties and Applications ; Titanium Alloys Microstructure and Properties ; Hydrogen Storage and Materials
Authors: Meysam Akbari | Andy Wang | Qigui Wang | Changlei Yan
Source: SAE technical papers on CD-ROM/SAE technical paper series (article, 2025-03-31)
Field: Materials Science — Materials Chemistry

Image-Based Machine Learning Methods in Materials Microstructure and Failure Analysis &lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Image-based machine learning (ML) methods are increasingly transforming the field of materials science, offering powerful tools for automatic analysis of microstructures and failure mechanisms. This paper provides an overview of the latest advancements in ML techniques applied to materials microstructure and failure analysis, with a particular focus on the automatic detection of porosity and oxide defects and microstructure features such as dendritic arms and eutectic phase in aluminum casting. By leveraging image-based data, such as metallographic and fractographic images, ML models can identify patterns that are difficult to detect through conventional methods. The integration of convolutional neural networks (CNNs) and advanced image processing algorithms not only accelerates the analysis process but also improves accuracy by reducing subjectivity in interpretation. Key studies and applications are further reviewed to highlight the benefits, challenges, and future directions of using ML in material failure analysis through image processing.&lt;/div&gt;&lt;/div&gt; Microstructure ; Computer science ; Artificial intelligence ; Computer vision ; Materials science ; Composite material Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; X-ray Diffraction in Crystallography
Authors: Lamara Maciel dos Santos | Dulce Maria de Araújo Melo | Rodolfo L.B.A. Medeiros | Ângelo A.S. Oliveira | Willian A. S. Farias | Rebecca Araújo Barros do Nascimento | Renata Martins Braga
Source: Molecular Catalysis (article, 2025-08-30)
Field: Materials Science — Materials Chemistry

Data-driven design of Ni-based catalysts for CO2 methanation using interpretable machine learning Methanation ; Catalysis ; Artificial intelligence ; Computer science ; Chemistry ; Machine learning ; Organic chemistry Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions
Authors: Shivam Yadav | T. Nandha Kumar | Kishore Kumar Sriramoju | Prakash Vislavath | T. Umasankar Patro | G. Harikrishnan
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Machine learning computes the elastic response of cross-linked polymers This article presents a generalized machine learning pathway to compute the elastic response of cross-linked, rubber like polymers. Natural rubber ; Polymer ; Polymer science ; Computer science ; Materials science ; Composite material Polymer composites and self-healing
Authors: Anton Yudhana | Julia Mega Reski
Source: International Journal of Advances in Data and Information Systems (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Classification of Crystallization Images of Pharmaceutical Raw Materials Using Convolutional Neural Network Algorithm The rapid advancement of artificial intelligence (AI) has opened new opportunities for automation in the pharmaceutical industry, particularly in the classification of raw drug materials. Manual classification methods are time-consuming and prone to human error, highlighting the need for reliable automated solutions. This study applied a deep learning approach for classifying crystallization images of pharmaceutical raw materials using a Convolutional Neural Network (CNN). A dataset of 300 crystallization images of Nicotinamide and Ferulic Acid was obtained through hot-stage microscopy, preprocessed with normalization, resizing, and augmentation, and divided into training, validation, and testing subsets. The CNN model was trained for 10 epochs and evaluated using a confusion matrix and standard performance metrics (accuracy, precision, recall, and F1-score). The model achieved perfect recall for Ferulic Acid and 90% recall with 100% precision for Nicotinamide, resulting in an overall accuracy of 95%. While these results are promising, the relatively small dataset may limit generalization, and further validation with larger or external datasets is required. The findings indicate that CNN-based methods hold strong potential for automating crystallization classification, improving pharmaceutical quality control, and reducing reliance on manual assessment, in line with recent advances in medical and pharmaceutical image analysis. Convolutional neural network ; Automation ; Artificial neural network ; Pattern recognition (psychology) ; Raw data ; Precision and recall Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Enzyme Structure and Function
Authors: Yannick Couzinié | Shunsuke Daimon | Hirofumi Nishi | N. Ito | Yusuke Harazono | Yu‐ichiro Matsushita
Source: arXiv (Cornell University) (preprint, 2025-05-06)
Field: Materials Science — Materials Chemistry

Data for Towards Improved Quantum Machine Learning for Molecular Force Fields This dataset contains the numerical data used to generate Figures 6 and 7 in the manuscript "Towards Improved Quantum Machine Learning for Molecular Force Fields". Quantum ; Field (mathematics) ; Quantum computer ; Computational learning theory ; Force field (fiction) ; Quantum machine learning ; Support vector machine Machine Learning in Materials Science
Authors: Talal M. Althagafi | Muhammad Ramzan Saeed Ashraf Janjua | Fatimah Mohammed A. Alzahrani | Rashid Iqbal | M.S. Al-Buriahi
Source: Solid State Communications (article, 2025-10-04)
Field: Materials Science — Materials Chemistry

Accelerating photovoltaic polymer discovery via machine learning and synthetic accessibility analysis Polymer ; Photovoltaic system ; Random forest ; Process (computing) ; Maxima Machine Learning in Materials Science ; Green IT and Sustainability
Authors: Zewen Zhang | Qinghua Song | Jing Qin | Yapeng Li | Kunyu Li | Zhanqiang Liu
Source: Materials Today Communications (article, 2025-08-21)
Field: Materials Science — Materials Chemistry

Mechanism-informed prediction of γ-TiAl alloy mechanical behavior via molecular dynamics and explainable machine learning Materials science ; Mechanism (biology) ; Alloy ; Molecular dynamics ; Dynamics (music) ; Metallurgy ; Computational chemistry Machine Learning in Materials Science ; Intermetallics and Advanced Alloy Properties ; Advanced Materials Characterization Techniques
Authors: Itsuki Yoshikawa | Omid Aghababaei Tafreshi | Yu Sun | Naomi Matsuura | Ken‐Ichiro Sotowa | Hani E. Naguib
Source: Industrial & Engineering Chemistry Research (article, 2025-05-23)
Field: Chemistry — Spectroscopy

Data-Driven Discovery of Multifunctional Organic Polymer Aerogels by Machine Learning-Assisted Large-Scale Property Screening Polyimide (PI) aerogels are next-generation thermal insulation materials. Due to their intrinsic nanoporous structures, they are ultralow density and have superior thermal stability, making them important materials in fields such as aeronautics, thermal management, energy storage, and life sciences. The material properties and performance of PI aerogels are strongly correlated to the types of monomers and cross-linking agents. However, the time-consuming and costly synthesis process poses challenges in exploring new formulations of multifunctional PI aerogels with optimized properties. To discover new aerogel formulations, this research utilizes different machine learning methods to model the relationship between pristine monomers, processing conditions, and the properties of cross-linked aerogels. In addition, we have utilized machine learning to discover and predict new chemical structures via large-scale property screening. By introducing the Morgan fingerprint method, the chemical structure, including the hybrid monomer backbone, was effectively used as input for machine learning. Random forest and Gaussian process regression models were trained to estimate the density, compression modulus, and decomposition temperature. Over 1,600,000 novel PI aerogel structures were generated and screened using the trained machine learning models. Consequently, several structural candidates were identified as multifunctional PI aerogels with optimized properties and will have superior performance to those of previously reported aerogels. Scale (ratio) ; Polymer ; Property (philosophy) ; Organic polymer ; Computer science ; Nanotechnology ; Materials science ; Process engineering ; Chemistry ; Organic chemistry ; Engineering ; Physics Aerogels and thermal insulation ; Catalysis and Oxidation Reactions ; Carbon dioxide utilization in catalysis
Authors: Cameron Cook | Carlos Blanco | Juri Smirnov
Source: Physical review. D/Physical review. D. (article, 2025-10-02)
Field: Materials Science — Materials Chemistry

Deep learning optimal molecular scintillators for dark matter direct detection Direct searches for sub-GeV dark matter are limited by the intrinsic quantum properties of the target material. In this proof-of-concept study, we argue that this problem is particularly well suited for machine learning. We demonstrate that a simple neural architecture consisting of a variational autoencoder and a multilayer perceptron can efficiently generate unique molecules with desired properties. In specific, the energy threshold and signal (quantum) efficiency determine the minimum mass and cross section to which a detector can be sensitive. Organic molecules present a particularly interesting class of materials with intrinsically anisotropic electronic responses and <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"><a:mrow><a:mi mathvariant="script">O</a:mi><a:mo stretchy="false">(</a:mo><a:mi>few</a:mi><a:mo stretchy="false">)</a:mo><a:mtext> </a:mtext><a:mtext> </a:mtext><a:mi>eV</a:mi></a:mrow></a:math> excitation energies. However, the space of possible organic compounds is intractably large, which makes traditional database screening challenging. We adopt excitation energies and proxy transition matrix elements as target properties learned by our network. Our model is able to generate molecules that are not in even the most expansive quantum chemistry databases and predict their relevant properties for high-throughput and efficient screening. Following a massive generation of novel molecules, we use clustering analysis to identify some of the most promising molecular structures that optimize the desired molecular properties for dark matter detection. Dark matter ; Autoencoder ; Curse of dimensionality ; Scintillator ; Artificial neural network ; Quantum ; Deep learning ; Cluster analysis Machine Learning in Materials Science ; Radiation Detection and Scintillator Technologies ; Medical Imaging Techniques and Applications
Authors: Fuying Gao | Weiwen Zhang | Xiaotao Liu | Xuan Luo | Zhi Wang | Ning Li | L.H. Liu
Source: Journal of Materials Science (article, 2025-09-06)
Field: Materials Science — Materials Chemistry

Review: Recent progress in titanium alloys development via machine learning and high-throughput preparation Materials science ; Solid mechanics ; Throughput ; Titanium ; Titanium alloy ; Nanotechnology ; Systems engineering ; Metallurgy ; Computer science ; Composite material ; Engineering ; Alloy Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes
Authors: Manik Kumar | Nikhil Upadhyay | Shishir Barai | Wesley F. Reinhart | Christian Peco
Source: Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials (article, 2025-01-25)
Field: Materials Science — Materials Chemistry

A bio-lattice deep learning framework for modeling discrete biological materials Lattice (music) ; Computer science ; Artificial intelligence ; Biochemical engineering ; Materials science ; Biological system ; Physics ; Engineering ; Biology Machine Learning in Materials Science ; Composite Material Mechanics ; Advanced Electron Microscopy Techniques and Applications
Authors: Z. Xiong | ZhaoKun Song | Jianwei Li | Heran Wang | Xiaoxin Zhang | Bing Liang | Dong Wang
Source: Journal of Materials Research and Technology (article, 2025-04-06)
Field: Materials Science — Materials Chemistry

Machine learning-driven design of rare metal doped niobium alloys with enhanced strength and ductility The doping of rare metal elements (RMEs) in niobium alloys offers significant potential for improving mechanical properties by refining grains and forming secondary phase particles. However, the inherent trade-off between strength and ductility, coupled with the complexity of alloy composition-performance relationships, limits the efficiency of traditional trial-and-error methods. In this study, a machine learning-based framework was developed to optimize the mechanical properties of niobium alloys. A comprehensive database of niobium alloys' properties was analyzed using feature engineering, and a high-accuracy prediction model, Gray Wolf Optimization-Extreme Learning Machine (GWO-ELM), was constructed, achieving R 2 values of 0.95 and 0.88 for tensile strength and elongation, respectively. The model was integrated with the Non-dominated Sorting Genetic Algorithm (NSGA-III) to design alloys with superior comprehensive properties. The optimized Nb521–0.116Sc alloy demonstrated a tensile strength of 772 MPa and an elongation of 10.3 %, with a maximum K value (representing comprehensive performance index) of 13.80, representing a 34.82 % improvement in the comprehensive performance index. Microstructural analysis revealed that the enhancements were primarily due to solid solution strengthening with high solubility. This study highlights the potential of machine learning in accelerating the design of high-performance niobium alloys and provides a robust strategy for developing advanced materials with balanced strength and ductility. Materials science ; Niobium ; Ductility (Earth science) ; Doping ; Metallurgy ; Engineering physics ; Optoelectronics Machine Learning in Materials Science ; MXene and MAX Phase Materials ; Intermetallics and Advanced Alloy Properties
Authors: Xiaoda Liu | Yiming Wang | Yukun Yuan | Yinghu Wang | Xu Li | Huan Wei | Qian Wang | Huayun Du | Lifeng Hou | Yinghui Wei
Source: Materials Genome Engineering Advances (article, 2025-06-10)
Field: Materials Science — Materials Chemistry

Research on discharge performance prediction of magnesium anode materials assisted by graph neural networks Abstract In Mg anode materials design, the second phase plays a significant role in influencing the alloy's properties. This paper proposes a fusion model based on graph neural networks (GNNs) aimed at optimizing alloy design by considering the impact of the second phase in the alloy. We calculate the types and mass percentages of the thermodynamically stable second phases in the alloy. A GNN‐based fusion model is then used to capture the interactions between the second phases within the alloy to predict the voltage of the magnesium anode. Our model outperforms the traditional MLP model. The average error is reduced from 0.096 to 0.075 V, representing a 21.8% decrease in error. We use Bayesian optimization and Pareto front analysis for multi‐objective optimization. The Mg‐2Sr alloy, which has high voltage values at 5, 10 and 20 mA cm −2 , is selected for experimental validation. The GNN fusion model improves the accuracy of the model, providing a more scientific basis for the design and optimization of magnesium alloy anodes. Magnesium ; Anode ; Artificial neural network ; Graph ; Materials science ; Computer science ; Artificial intelligence ; Metallurgy ; Chemistry ; Theoretical computer science ; Electrode ; Physical chemistry Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Guillem Simeon | Antonio Mirarchi | Raúl P. Peláez | Raimondas Galvelis | Gianni De Fabritiis
Source: Journal of Chemical Theory and Computation (article, 2025-02-11)
Field: Materials Science — Materials Chemistry

Broadening the Scope of Neural Network Potentials through Direct Inclusion of Additional Molecular Attributes Most state-of-the-art neural network potentials do not account for molecular attributes other than atomic numbers and positions, which limits its range of applicability by design. In this work, we demonstrate the importance of including additional electronic attributes in neural network potential representations with a minimal architectural change to TensorNet, a state-of-the-art equivariant model based on Cartesian rank-2 tensor representations. By performing experiments on both custom-made and public benchmarking data sets, we show that this modification resolves input degeneracy issues stemming from the use of atomic numbers and positions alone, while enhancing the model's predictive accuracy across diverse chemical systems with different charge or spin states. This is accomplished without tailored strategies or the inclusion of physics-based energy terms, while maintaining efficiency and accuracy. These findings should furthermore encourage researchers to train and use models incorporating these additional representations. Scope (computer science) ; Inclusion (mineral) ; Artificial neural network ; Computer science ; Data science ; Chemistry ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jacopo Moi | Davide Spallarossa | Stefano Bonetti | Raffaella Burioni | Guido Caldarelli
Source: Physical review. E (article, 2025-06-05)
Field: Materials Science — Materials Chemistry

Quest for new materials: Network theory and machine learning perspectives Understanding and predicting the emergence of novel materials is a fundamental challenge in condensed matter physics, materials science, and technology. With the rapid growth of materials databases in both size and reliability, the challenge shifts from data collection to efficient exploration of this vast and complex space. A key strategy lies in the smart use of descriptors at multiple scales, ranging from atomic arrangements to macroscopic properties, to represent materials in high-dimensional abstract spaces. Network theory provides a powerful framework to structure and analyze these relationships, capturing hidden patterns and guiding discovery. Machine learning complements this approach by enabling predictive modeling, dimensionality reduction, and the identification of promising material candidates. By integrating network-based methods with machine learning (ML) techniques, researchers can construct, analyze, and efficiently navigate the material space, uncovering novel materials with tailored properties. This review explores the synergy between network theory and ML, highlighting their role in accelerating materials discovery through a systematic and interpretable approach. Network theory ; Computer science ; Cognitive science ; Artificial intelligence ; Data science ; Psychology ; Mathematics Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Advanced Memory and Neural Computing
Authors: Jamshaid Ul Rahman | Hasnain Ali | Areen Rassol
Source: Computational Biology and Chemistry (article, 2025-06-14)
Field: Materials Science — Materials Chemistry

A comprehensive analysis of optimizers in message passing neural networks for molecular property prediction task Property (philosophy) ; Task (project management) ; Computer science ; Artificial neural network ; Message passing ; Artificial intelligence ; Machine learning ; Distributed computing ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks