jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Daijun Liu | Xin Tong | Libao Liu | Tao Chen | Jinzhu Tang | Wenkai Tang | Liming Wang | Bin Cao | Zimin Luo
Source: Polymers (article, 2025-04-15)
Field: Materials Science — Materials Chemistry

Research on the Thermal Aging Characteristics of Cured Epoxy Resin Insulating Materials for DC Bushings High-temperature-resistant epoxy composites play a crucial role in enhancing the operational reliability and service life of devices such as DC bushings, which is of great significance for the long-term stable operation of ultra-high voltage and flexible power transmission and distribution systems. In this study, the epoxy composite was prepared, and long-term thermal aging tests were conducted at 250 °C and 270 °C. The changes in physical properties, electrical characteristics, and bending strength of epoxy composite were systematically investigated, and the thermal aging mechanism of these materials was elucidated. The experimental results revealed that with the progression of thermal aging, the epoxy composites exhibited volume shrinkage due to the breaking of chemical bonds. After 10 thermal aging cycles at 270 °C, the mass loss rate of the epoxy composite reached 20.52%. At 250 °C, the breakdown strength decreased by 9.9% compared to the unaged state. After aging at 250 °C and 270 °C, the volume resistivity decreased by a maximum of 53.75% and 76.94%, respectively, while the dielectric constant decreased by a maximum of 50.34% and 41.94%, respectively. After 10 aging cycles at 250 °C and 270 °C, the bending strength of the cured epoxy composite decreased by 39.79% and 53.91%, respectively. These findings provide valuable insights into the thermal aging characteristics of epoxy composites used in DC bushings and other electrical devices, offering a scientific basis for material selection and reliability assessment in high-voltage insulation applications. Epoxy ; Materials science ; Composite material ; Composite number ; Accelerated aging ; Shrinkage High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Smart Materials for Construction
Authors: Enrique Dehaerne | Bappaditya Dey | Víctor Blanco | Jesse Davis
Source: Journal of Micro/Nanopatterning Materials and Metrology (review, 2025-05-15)
Field: Materials Science — Surfaces, Coatings and Films

Scanning electron microscopy-based automatic defect inspection for semiconductor manufacturing: a systematic review We identified, categorized, and discussed automatic defect inspection algorithms that analyze scanning electron microscopy (SEM) images for semiconductor manufacturing (SM). This is a topic of critical importance for the SM industry as the continuous shrinking of device patterns has led to increasing defectivity and a greater prevalence of higher-resolution imaging tools such as SEM. Among others, these aspects threaten to increase costs due to increased inspection time-to-solution and decreased yield. Relevant research papers were systematically identified in four popular publication databases in January 2024. A total of 103 papers were selected after screening for contributions relating to automatic SEM image analysis algorithms for semiconductor defect inspection. We then categorized based on the inspection tasks they addressed, their evaluation metrics, and the type of algorithms used. A notable finding from this categorization is that reference-based defect detection algorithms were the most popular algorithm type until 2020 when deep learning (DL)-based inspection algorithms became more popular, especially for defect classification. Furthermore, four broader research questions were discussed to come to the following conclusions: (i) the key components of inspection algorithms are set up, pre-processing, feature extraction, and final prediction; (ii) the maturity of the manufacturing process affects the data availability and required sensitivity of inspection algorithms; (iii) key challenges for these algorithms relate to the desiderata of minimizing time-to-solution which pushes for high imaging throughput, reducing manual input during algorithm setup, and higher processing throughput; and (iv) three promising directions for future work are suggested based on gaps in the reviewed literature that address key remaining limitations. Semiconductor ; Materials science ; Scanning electron microscope ; Semiconductor device fabrication ; Nanotechnology ; Computer science ; Optoelectronics ; Composite material Electron and X-Ray Spectroscopy Techniques ; Industrial Vision Systems and Defect Detection ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Shuiping Li | Yue-Yue Chen | Xiaobo Zhang | Jun-Bo Wang | Xin Gao | Yunhong Jiang | Zhaojun Ban | Cunkun Chen
Source: Food Innovation and Advances (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Back Propagation Neural Network model for analysis of hyperspectral images to predict apple firmness The potential of employing hyperspectral imaging (HSI) in the near-infrared (NIR) range (386.82−1,004.50 nm) for predicting the firmness of 'Fuji' apples cultivated in Aksu has been evaluated. The performance of seven preprocessing algorithms and two feature selection algorithms was evaluated. The coefficient of determination (<i>R</i><sup>2</sup>) and root mean square error (RMSE) of Partial Least Squares (PLS) models are contrasted using various inputs. These results confirm that the Multiplicative Scatter Correction (MSC) preprocessing algorithm was the optimal choice ($ {R}_{p}^{2} $ = 0.7925, <i>RMSEP</i> = 0.6537), and the Competitive Adaptive Reweighted Sampling (CARS) feature selection algorithm demonstrated superior performance ($ {R}_{p}^{2} $ = 0.8325, <i>RMSEP</i> = 0.6257). Based on the aforementioned findings, PLS, Multiple Linear Regression (MLR), Heterogeneous Transfer Learning (HTL), and Back Propagation Neural Network (BPNN) models were constructed for cross-validation purposes. The experimental results indicate that the CARS-BPNN model exhibits the optimal prediction performance, with an $ {R}_{p}^{2} $ value of 0.9350 and an <i>RMSEP</i> value of 0.4654. The results of the research indicated that a deep learning method combined with hyperspectral imaging technology could be utilized to non-destructively detect the firmness of 'Fuji' apples, which will be beneficial and potentially applicable for post-harvest fruit firmness monitoring. This research provides a reference point for the non-destructive detection of apple in the selection of preprocessing, feature selection algorithms, and predicting firmness model. Hyperspectral imaging ; Artificial neural network ; Artificial intelligence ; Computer science ; Computer vision ; Remote sensing ; Geology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Lu Zhang | Remko M. Boom | Yizhou Ma
Source: Annual Review of Food Science and Technology (review, 2025-04-28)
Field: Chemistry — Analytical Chemistry

Machine Learning in Automated Food Processing: A Mini Review Industrial food processing is rapidly transforming into automation and digitalization. Automated food processing systems adapt to variations in raw materials and product quality requirements. Implementing automated processing systems can potentially improve the sustainability of our food systems by improving productivity while reducing environmental impacts. Nevertheless, the adoption of automated food processing systems is still relatively low. In this review, we discuss the concept of automated food processing and summarize the recent advances in applications of machine learning technologies to enable automated food processing. Machine learning can find its applications in formulation development, process control, and product quality assessment. We share our vision on the potential of automated food processing systems to adapt to complex raw materials, mass customization, personalized nutrition, and human-machine interaction. Finally, we pinpoint relevant research questions and stress that future research on automated food processing requires multidisciplinary approaches. Food processing ; Automation ; Computer science ; Product (mathematics) ; Personalization ; Mass customization ; Quality (philosophy) ; Artificial intelligence ; Engineering ; World Wide Web Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Food Supply Chain Traceability
Authors: Fanzhe Ma | Fuyi Chen | Peng Xu | Xiaoqing Liu | Wanxuan Zhang
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

High-throughput screening of stable Ag–Pd–F catalysts for formate oxidation reaction using machine learning The crystal graph convolutional neural networks predict stable Ag–Pd–F catalysts for the formate oxidation reaction from Materials Project data with DFT verifying their thermodynamic, dynamic stability and catalytic activity. Throughput ; Catalysis ; Formate ; High-throughput screening ; Computer science ; Chemistry ; Combinatorial chemistry ; Organic chemistry ; Operating system ; Biochemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Aasim Ayaz Wani
Source: Materials Sciences and Applications (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Advancing Material Stability Prediction: Leveraging Machine Learning and High-Dimensional Data for Improved Accuracy Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional Theory (DFT), are accurate but computationally expensive and unsuitable for high-throughput screening. This study introduces a machine learning (ML) framework trained on high-dimensional data from the Open Quantum Materials Database (OQMD) to predict formation energy, a key stability metric. Among the evaluated models, deep learning outperformed Gradient Boosting Machines and Random Forest, achieving up to 0.88 R2 prediction accuracy. Feature importance analysis identified thermodynamic, electronic, and structural properties as the primary drivers of stability, offering interpretable insights into material behavior. Compared to DFT, the proposed ML framework significantly reduces computational costs, enabling the rapid screening of thousands of compounds. These results highlight ML’s transformative potential in materials discovery, with direct applications in energy storage, semiconductors, and catalysis. Materials science ; Stability (learning theory) ; Machine learning ; Artificial intelligence ; Nanotechnology ; Computer science Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Nuclear Materials and Properties
Authors: Dante Mora‐Mariano | Antonio Flores‐Tlacuahuac | Iván Zapata‐González | Enrique Saldívar‐Guerra
Source: The Canadian Journal of Chemical Engineering (article, 2025-02-18)
Field: Materials Science — Materials Chemistry

Data‐driven deep learning prediction of full molecular weight distribution in polymerization processes Abstract The mathematical modelling of the full molecular weight distribution (MWD) results in a large set of ordinary differential equations (ODEs), which usually requires considerable computation time because of stiffness behaviour. This study applies state‐of‐the‐art deep learning (DL) methods to model three academically and industrially relevant polymerization processes: free radical polymerization (FRP), reversible addition–fragmentation (RAFT), and coordination catalyst polymerization (CCP). The DL models were trained with datasets generated from the numerical solution of the first principles kinetic model of each polymerization process. Then, the applied DL models were used to predict the conversion rate, average molar weights, and molecular weight distributions with minimum deviations and reduced computational load. Therefore, by reducing the large computational load, this type of DL models can make feasible the application of on‐line optimal control strategies to complex and economically important polymerization processes. Polymerization ; Molar mass distribution ; Distribution (mathematics) ; Deep learning ; Artificial intelligence ; Computer science ; Materials science ; Mathematics ; Polymer ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Chemometric Analyses
Authors: Xiangyu Song | Shuoyang Wang | Fan Wang | Y.L. Liu | Zongliang Zuo | Siyi Luo | Dong Chen | Fangchao Zhao
Source: International Journal of Hydrogen Energy (article, 2025-04-24)
Field: Materials Science — Materials Chemistry

Machine learning-assisted catalyst synthesis and hydrogen production via catalytic hydrolysis of sodium borohydride Sodium borohydride ; Catalysis ; Hydrogen production ; Hydrolysis ; Chemistry ; Hydrogen ; Sodium ; Organic chemistry Machine Learning in Materials Science ; Hydrogen Storage and Materials ; Asymmetric Hydrogenation and Catalysis
Authors: Junnan Song | Jorge García-Balduz | Tangyu Yang | André G. Skirtach | Bogdan V. Parakhonskiy
Source: Journal of Colloid and Interface Science (article, 2025-04-03)
Field: Materials Science — Biomaterials

Machine learning accelerated analysis of microwave-assisted synthesis parameters on calcium carbonate particles Calcium carbonate ; Microwave ; Calcium ; Carbonate ; Mineralogy ; Chemical engineering ; Chemistry ; Materials science ; Computer science ; Metallurgy ; Engineering ; Telecommunications Calcium Carbonate Crystallization and Inhibition ; Chemical Synthesis and Characterization ; X-ray Diffraction in Crystallography
Authors: Xian-Yi Sha | Zhuo Chen | Daiqian Xie | Yanzi Zhou
Source: Journal of Chemical Theory and Computation (article, 2025-04-24)
Field: Materials Science — Materials Chemistry

Modeling Enzyme Reaction and Mutation by Direct Machine Learning/Molecular Mechanics Simulations Accurately modeling enzyme reactions through direct machine learning/molecular mechanics simulations remains challenging in describing the electrostatic coupling between the QM and MM subsystems. In this work, we proposed a reweighting ME (mechanic embedding) REANN (recursively embedded atom neural network) method that trains the potential and point charges of the QM subsystem in vacuo. The charge equilibration approach has been encoded into REANN to ensure conservation of the total charge of the QM subsystem. Electrostatic coupling is measured by point charges, and the polarization of the MM subsystem on the coupling can be corrected by thermodynamic perturbation after molecular dynamics simulations. We first constructed the REANN surfaces of potential energy and charges for the acylation of cyclooxygenase-1 (COX-1) and cyclooxygenase-2 (COX-2) by aspirin. These surfaces allowed us to reproduce the free energy curves of B3LYP/MM-MD with a chemical accuracy. Subsequently, they were successfully applied to R513A of COX-2, reproducing the free energy barrier simulated by B3LYP/MM MD with a difference of less than 0.5 kcal mol<sup>-1</sup> and a speedup of 80-fold, revealing our method can predict the activity of mutants accurately and rapidly. This method is expected to be applied in virtual screening in the future. Molecular mechanics ; Molecular dynamics ; Computer science ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Kit Joll | Philipp Schienbein | Kevin M. Rosso | Jochen Blumberger
Source: The Journal of Physical Chemistry Letters (article, 2025-01-16)
Field: Materials Science — Materials Chemistry

Mechanism of Fe(II) Chemisorption on Hematite(001) Revealed by Reactive Neural Network Potential Molecular Dynamics Atomic-scale understanding of important geochemical processes including sorption, dissolution, nucleation, and crystal growth is difficult to obtain from experimental measurements alone and would benefit from strong continuous progress in molecular simulation. To this end, we present a reactive neural network potential-based molecular dynamics approach to simulate the interaction of aqueous ions on mineral surfaces in contact with liquid water, taking Fe(II) on hematite(001) as a model system. We show that a single neural network potential predicts rate constants for water exchange for aqueous Fe(II) and for the exergonic chemisorption of aqueous Fe(II) on hematite(001) in good agreement with experimental observations. The neural network potential developed herein allows one to converge free energy profiles and transmission coefficients at density functional theory-level accuracy outperforming state-of-the-art classical force field potentials. This suggests that machine learning potential molecular dynamics should become the method of choice for atomistic studies of geochemical processes. Molecular dynamics ; Hematite ; Aqueous solution ; Nucleation ; Chemistry ; Dissolution ; Chemical physics ; Sorption ; Chemisorption ; Materials science ; Computational chemistry ; Mineralogy ; Physical chemistry ; Adsorption Machine Learning in Materials Science ; Iron oxide chemistry and applications ; Spectroscopy and Quantum Chemical Studies
Authors: Hao Ting-yue | Tianle Yue | Ying Li
Source: Journal of Chemical Information and Modeling (article, 2025-06-25)
Field: Materials Science — Materials Chemistry

Assessing Uncertainty in Machine Learning for Polymer Property Prediction: A Benchmark Study Machine learning (ML) has emerged as a transformative tool in material science, enabling accelerated discovery and design of novel molecules while reducing experimental costs. Uncertainty quantification (UQ) is crucial for enhancing the reliability of ML predictions, particularly in high-stakes applications, such as functional polymer discovery. In this study, we present a comprehensive evaluation of nine UQ methods in ML─ensemble, Gaussian Process Regression (GPR), Monte Carlo Dropout (MCD), mean-variance estimation (MVE), Bayesian Neural Network based on Variational Inference (BNN-VI) and Markov Chain Monte Carlo (BNN-MCMC), evidential deep learning (EDL), quantile regression (QR), natural gradient boosting (NGBoost)─for predicting key polymer properties, including glass transition temperature (<i>T</i><sub>g</sub>), band gap (<i>E</i><sub>g</sub>), melting temperature (<i>T</i><sub>m</sub>) and decomposition temperature (<i>T</i><sub>d</sub>). The models are assessed using three independent metrics, including prediction accuracy (<i>R</i><sup>2</sup>), Spearman's rank correlation coefficient and calibration area, offering a robust framework for evaluating both mean predictions and uncertainty estimates. Our analysis spans data sets of four properties, out-of-distribution (OOD) experimental and molecular dynamics (MD)-derived data, high-<i>T</i><sub>g</sub> polymers and diverse polymer types, providing a holistic perspective on model performance. Our findings reveal that optimal UQ method selection is highly context-dependent. Ensemble method consistently excelled for general in-distribution predictions across four properties. For challenging OOD scenarios, BNN-MCMC offered a strong balance of predictive accuracy and reliable UQ. NGBoost emerged as the top-performing method for high-<i>T</i><sub>g</sub> polymers, effectively balancing accuracy and uncertainty characterization, with Ensemble method also providing excellent accuracy in this case. Furthermore, BNN-VI demonstrated superior and consistent performance across the nine distinct polymer classes evaluated. This comprehensive benchmark underscores the critical importance of selecting tailored UQ strategies to enhance the trustworthiness of ML predictions, optimize experimental validation efforts, and ultimately accelerate the discovery of advanced functional polymers. Benchmark (surveying) ; Property (philosophy) ; Artificial intelligence ; Machine learning ; Computer science ; Geography Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Zhenyu Wang | Xiaoshan Luo | Q. Wang | Heng Ge | Pengyue Gao | Wei Zhang | Jian Lv | Yanchao Wang
Source: Matter and Radiation at Extremes (article, 2025-03-14)
Field: Materials Science — Materials Chemistry

Advances in high-pressure materials discovery enabled by machine learning Crystal structure prediction (CSP) is a foundational computational technique for determining the atomic arrangements of crystalline materials, especially under high-pressure conditions. While CSP plays a critical role in materials science, traditional approaches often encounter significant challenges related to computational efficiency and scalability, particularly when applied to complex systems. Recent advances in machine learning (ML) have shown tremendous promise in addressing these limitations, enabling the rapid and accurate prediction of crystal structures across a wide range of chemical compositions and external conditions. This review provides a concise overview of recent progress in ML-assisted CSP methodologies, with a particular focus on machine learning potentials and generative models. By critically analyzing these advances, we highlight the transformative impact of ML in accelerating materials discovery, enhancing computational efficiency, and broadening the applicability of CSP. Additionally, we discuss emerging opportunities and challenges in this rapidly evolving field. High pressure ; Engineering ; Data science ; Construction engineering ; Forensic engineering ; Computer science ; Engineering physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Bingbo Zhang | Xueqing Li | Ming Zeng | Jingguo Cao
Source: Polymer (article, 2025-01-24)
Field: Materials Science — Polymers and Plastics

COMFO: Integrated deep learning model facilitates discovery of multifunctional polyimide materials Polyimide ; Materials science ; Nanotechnology ; Layer (electronics) Synthesis and properties of polymers ; Fuel Cells and Related Materials ; Epoxy Resin Curing Processes
Authors: Qi Yu | Ruitao Ma | Chen Qu | Riccardo Conte | Apurba Nandi | Priyanka Pandey | Paul L. Houston | Dong H. Zhang | Joel M. Bowman
Source: Nature Computational Science (article, 2025-04-14)
Field: Materials Science — Materials Chemistry

Extending atomic decomposition and many-body representation with a chemistry-motivated approach to machine learning potentials Decomposition ; Representation (politics) ; Computer science ; Artificial intelligence ; Chemistry ; Cognitive science ; Machine learning ; Psychology ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics