jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shan Gao | Yuting Ji | Tianyu Zhao | Haiming Song | Dehua Huang | Arif Rashid | Hanxue Hou | Wentao Wang
Source: Journal of Future Foods (article, 2025-09-01)
Field: Materials Science — Biomaterials

Novel starch/agar smart bioplastic incorporated with dual-encapsulated anthocyanin for shrimp freshness monitoring • Dual-encapsulation enhanced the color stability and reversibility of smart films. • Chitosan hydrochloride ≥160 kDa resulted in films' better comprehensive properties. • ROC analysis confirmed the reliability of ΔE* AP to indicate shrimp edible quality. • The bioplastics had satisfied color response for shrimp freshness monitoring. • Artificial neural network was established between films' color and shrimp quality. A dual-encapsulation strategy for anthocyanins (as the active core) was proposed, using chitosan hydrochloride and carboxymethyl chitosan (80,160, 240 kDa) as the external shell and β-cyclodextrin as the internal shell. The highest encapsulation efficiency (63.65%) was achieved in the complex constructed with carboxymethyl chitosan of 240 kDa, while that of 160 kDa enabled the complexes with superior stability against light, heat, and oxidation. Furthermore, smart bioplastics were constructed, where starch/agar networks well-entrapped the complexes by establishing new intermolecular interactions. Although the molecular weight of carboxymethyl chitosan insignificantly affected the bioplastics' capacity to indicate shrimp freshness, all obtained bioplastics showed good color response to different pH conditions, excellent color stability at 25°C for 21 d and reversibility. Color difference values of the bioplastics were confirmed as a reliable indicator of the edible quality of shrimp, and an artificial neural network model was successfully established between the color information ( L *, a *, b *, color difference) and the related quality parameters (total volatile basic nitrogen, pH) of shrimp. Findings supported the application of such bioresource-based smart bioplastics in the seafood packaging field. Bioplastic ; Shrimp ; Chitosan ; Active packaging ; Food packaging ; Anthocyanin Nanocomposite Films for Food Packaging ; Food and Agricultural Sciences ; Coconut Research and Applications
Authors: Yūsuke Nanba | Michihisa Koyama
Source: Journal of Computational Chemistry (article, 2025-03-21)
Field: Materials Science — Materials Chemistry

Physical Significance of Descriptors to Predict the Band Center of High‐Entropy Nanoalloys The band center of d orbitals (d-band center) has been widely used as an effective descriptor for analyzing material properties. However, in high-entropy nanoalloys, the diverse atomic environments present challenges in systematically exploring all possible combinations. Due to computational resource limitations, generating a sufficient number of samples is infeasible. Consequently, the d-band center should be treated as a response variable in machine-learning models. We calculated the d-band center for individual atoms and applied supervised learning techniques to identify key factors influencing its behavior. While several factors were identified, their physical significance in predicting d-band centers remained unclear. To address this issue, we incorporated various interatomic distance terms as descriptors, along with element-based coordination numbers (ECN). The resulting model closely resembled the overlap integral of the Slater-type orbital, and the regression coefficients of the ECN exhibited sensitivity to the effective principal quantum number and nuclear charge. Understanding the physical significance of these descriptors is crucial for improving property predictions and facilitating data collection on novel materials. Atomic orbital ; Computer science ; Entropy (arrow of time) ; Quantum chemical ; Machine learning ; Artificial intelligence ; Statistical physics ; Physics ; Quantum mechanics ; Electron ; Molecule Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Advanced Thermoelectric Materials and Devices
Authors: Fangyuan Zheng | Hongjun Xiang | Lanlan Zhong | Xueling Wang | Xiaohan Zhang | Qingwen Su | Chunmei Tang | Ling Meng | Lei Du | Feng Jiao | Yoshitaka Aoki | Baoyin Yuan | Ning Wang | Siyu Ye
Source: Small (review, 2025-05-29)
Field: Materials Science — Materials Chemistry

Optimization of Performance at Air Electrode Side for Protonic Solid Oxide Cells: Advances and Machine Learning Guided Perspectives Protonic solid oxide cell (P-SOC) is a novel type of solid oxide cell for hydrogen production and power generation. P-SOCs have garnered significant attention due to their advantages, such as the elimination of precious metals and high conversion efficiency. However, the commercialization of P-SOCs is currently hindered by suboptimal electrochemical performance, particularly at the air electrode side, where challenges in catalytic activity and ionic/electronic conductivity persist. Recently, strategies for designing advanced triple-conducting oxides, exsolution, and optimizing the air electrode-electrolyte interfaces have been proposed to improve the electrochemical reactive area, kinetics, and durability of air electrodes. Thereinto, machine learning (ML) techniques have emerged as powerful tools, playing a crucial role in the above topics. Despite these advancements, a comprehensive review synthesizing these innovative strategies and ML-guided advances and perspectives has been lacking in literature. This review comprehensively makes a summary of these methods and discusses their effects on cell performance. Importantly, the ML-guided perspectives and challenges in accelerating the optimization of these strategies and P-SOCs are proposed here. This paper not only offers valuable insights for understanding and optimizing performances at the air electrode side but also provides a roadmap for the rational design of superior air electrodes of P-SOCs. Electrode ; Materials science ; Nanotechnology ; Oxide ; Chemical engineering ; Chemistry ; Engineering ; Metallurgy Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Magnetic and transport properties of perovskites and related materials
Authors: Sridatri Nandy | K. V. Jovan Jose
Source: Journal of Chemical Theory and Computation (article, 2025-01-15)
Field: Materials Science — Materials Chemistry

Directed Electrostatics Strategy Integrated as a Graph Neural Network Approach for Accelerated Cluster Structure Prediction We present a directed electrostatics strategy integrated as a graph neural network (DESIGNN) approach for predicting stable nanocluster structures on their potential energy surfaces (PESs). The DESIGNN approach is a graph neural network (GNN)-based model for building structures of large atomic clusters with specific sizes and point-group symmetry. This model assists in the structure building of atomic metal clusters by predicting molecular electrostatic potential (MESP) topography minima on their structural evolution paths. The DESIGNN approach is benchmarked on the prototype Mg<sub><i>n</i></sub> clusters with <i>n</i> < 150. The predicted MESP topography minima of Mg<sub><i>n</i></sub> clusters, <i>n</i> < 70, fairly agrees with the whole-molecule MESP topography calculations. Moreover, the ground-state structures of Mg<sub><i>n</i></sub> (<i>n</i> = 4-32) clusters generated through the DESIGNN approach corroborate well with the global minimum structures reported in the literature. Furthermore, this approach could generate novel symmetric isomers of medium to large Mg<sub><i>n</i></sub> clusters in the size regime, <i>n</i> < 150, by constraining the point-group symmetry of the parent clusters. The parent growth potential (GP) of a cluster gives a measure of its parent cluster to accommodate more atoms and characterize the structures on the DESIGNN-guided path. The GP of a cluster can also be interpreted as a measure of the cooperative interaction relative to its parent cluster. Along the highest GP paths, the DESIGNN approach is further employed to generate stable Mg<sub><i>n</i></sub> nanoclusters with <i>n</i> = 228, 236, 257, 260. Therefore, the DESIGNN approach holds great promise in accelerating the structure search and prediction of large metal clusters guided through MESP topography. Computer science ; Cluster (spacecraft) ; Artificial neural network ; Electrostatics ; Artificial intelligence ; Graph ; Machine learning ; Data mining ; Data science ; Theoretical computer science ; Chemistry ; Computer network Machine Learning in Materials Science ; Nanocluster Synthesis and Applications ; Computational Drug Discovery Methods
Authors: Azadeh Mokari | Simone Eiserloh | Oleg Ryabchykov | Ute Neugebauer | Thomas Bocklitz
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2025-06-27)
Field: Chemistry — Analytical Chemistry

A comparative study of robustness to noise and interpretability in U-Net-based denoising of Raman spectra Raman spectroscopy is a valuable analytical technique for molecular characterization, but its practical application is often restricted by low signal-to-noise ratio (SNR), especially at short integration times. These limitations are critical in time-sensitive applications where rapid spectral acquisition is required. Deep learning, in particular, U-Net-based models are becoming routine tools for denoising the spectra. However, such models are typically applied as black boxes that are only evaluated using performance metrics. In this study, we investigate how training strategies using spectra acquired at different integration times and thus varying noise levels, affect model generalization. Specifically, we compare two models trained with the same U-Net architecture: a Single-Condition (SC) model trained on spectra acquired with a single integration time, and a Multi-Condition (MC) model trained on a dataset combining multiple integration times. Besides the quantitative evaluation of the models using Root Mean Squared Error (RMSE) and Pearson Correlation Coefficient (PCC), we apply interpretability techniques to gain deeper insight into how the models process spectral data. Saliency maps and Jacobian matrix analysis enable us to visualize which spectral regions the models focus on during denoising. This study highlights that training with diverse integration times significantly improves model generalization and denoising robustness. Moreover, our use of interpretability techniques reveals how different training strategies influence model focus and decision-making, offering a novel perspective on designing explainable and noise-resilient deep learning models for Raman spectroscopy. Chemistry ; Interpretability ; Raman spectroscopy ; Robustness (evolution) ; Noise reduction ; Spectral line ; Biological system ; Artificial intelligence ; Biochemistry ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Jeongnam Kim | Youngki Kim | Youhee Cho
Source: Polymers (article, 2025-03-20)
Field: Materials Science — Polymers and Plastics

Estimation of Synthetic Rubber Lifespan Based on Ozone Accelerated Aging Tests This study investigates the impact of ozone exposure on the hardness of synthetic rubber specimens (a blend of NR (natural rubber) and CR (chloroprene rubber)) through accelerated aging tests. Using a linear regression model, the research predicts the lifespan of rubber under real-world conditions and demonstrates how established experimental methods can yield novel insights when applied to synthetic rubber. The results show that ozone exposure significantly increases hardness within the first 10 days, stabilizing after day 12. Through analysis, this study calculates acceleration factors based on ozone concentration and temperature, estimating the practical lifespan of synthetic rubber under actual conditions to be approximately 25.76 years. These findings provide valuable indicators for evaluating the durability of synthetic rubber materials and predicting the longevity of rubber products in industrial applications. Furthermore, the research emphasizes the potential for improving lifespan prediction accuracy by incorporating non-linear models or machine learning approaches. Chloroprene ; Natural rubber ; Synthetic rubber ; Accelerated aging ; Ozone ; Materials science ; Durability ; Yield (engineering) ; Biological system ; Linear regression ; Environmental science ; Biochemical engineering ; Process engineering ; Composite material ; Mathematics ; Statistics ; Engineering ; Chemistry ; Biology Polymer Nanocomposites and Properties ; Polymer crystallization and properties ; Environmental Impact and Sustainability
Authors: Johannes Schörghuber | Nina Bučková | Esther Heid | Georg K. H. Madsen
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Chemistry — Electrochemistry

From flat to stepped: active learning frameworks for investigating local structure at copper–water interfaces Understanding processes at solid-liquid interfaces at the atomic level is important for applications such as electrocatalysis. Here we explore the effects of different step densities on the structure of interfacial water at the copper-water interface. Utilizing spatially resolved uncertainties, we develop an active learning framework and train a machine-learning force field (MLFF) based on dispersion-corrected density functional theory data. Using molecular dynamics simulations, we investigate structural properties of water molecules in the contact layer, including density profiles, angular distributions, and 2D pair correlation functions. In accordance with previous studies, we observe the formation of two sublayers within the contact layer at the Cu(111) surface, whereas the structure on surfaces with a high step density is dominated by the undercoordinated ridge atoms. By systematically decreasing the step density, we identify the cross-over to when the behavior observed at the flat surface can be locally recovered. Using dimensionality reduction, we identify four distinct types of Cu environments at the interfaces, providing insights into analyzing less idealized surfaces with MLFFs. Copper ; Active learning (machine learning) ; Materials science ; Nanotechnology ; Computer science ; Metallurgy ; Artificial intelligence Electrochemical Analysis and Applications
Authors: Zhaohui Li | Shuo Yang | Delian Liu
Source: Engineering Applications of Artificial Intelligence (article, 2025-04-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Automatic inverse design of second-order differential metasurfaces based on reinforcement learning Second-order differential metasurfaces, as two-dimensional artificial nanostructures, hold great potential for various applications in optical computing, image processing, microscopy imaging, etc. They have the capability to perform second-order mathematical operations on incident optical fields. In general, the design of second-order differential optical processing metasurfaces needs to consider various constraints, including incident optical fields, material properties, and application requirements. This task is challenging and time-consuming, typically requiring manual intervention. Therefore, a reinforcement learning-based artificial intelligence algorithm is proposed for the automatic design of second-order differential metasurfaces. It can rapidly find the desired result by leveraging the powerful self-learning ability of reinforcement learning. This paper first introduces the basic principles of designing second-order differential optical processing metasurfaces. Then, it presents a new automatic design method for second-order differential metasurfaces based on reinforcement learning. Finally, a design of a second-order differential metasurface is presented. Simulation and experimental results validate the effectiveness of the proposed method. The proposed method establishes a basic framework for applying reinforcement learning to the automatic design of photonic devices and outlines the various constraint factors that need to be coupled and considered during the design process. A surrogate model based on neural networks is presented to fit the transmission calculated by finite difference time domain software. A new reward formula is given to sufficiently consider the transmission efficiency of whole devices. The proposed method effectively leverages the self-learning capability of reinforcement learning, circumventing challenges related to manual intervention, computational complexity, and time-consuming tasks in conventional methods. • Reinforcement learning is applied to metasurfaces inverse design. • Neural network based transmission calculation surrogate model is presented. • New reward formula considering transmission efficiency is given. Computer science ; Reinforcement learning ; Inverse ; Differential (mechanical device) ; Order (exchange) ; Artificial intelligence ; Physics ; Mathematics Metamaterials and Metasurfaces Applications ; Neural Networks and Reservoir Computing ; Aeroelasticity and Vibration Control
Authors: A. Yağmur Gören | Zeqing Bao | Jesús Lozano | Christine Allen
Source: Scientific Data (article, 2025-07-10)
Field: Materials Science — Biomaterials

A formulation dataset of poly(lactide-co-glycolide) nanoparticles for small molecule delivery Poly(lactide-co-glycolide) (PLGA) nanoparticles are promising drug delivery systems, widely recognized for their ability to overcome various limitations associated with conventional formulations. However, designing and optimizing such formulations is a complex and non-trivial process that heavily relies on a lengthy, iterative approach, often involving trial and error. To address the limitations of traditional approaches, formulation scientists are increasingly incorporating artificial intelligence, particularly machine learning, to rationalize and accelerate the process. Despite decades of intensive research into PLGA nanoparticles, a notable shortage remains in the availability of comprehensive open-source datasets essential for driving this accelerated development process forward. Here, we present a literature-curated dataset of 433 PLGA nanoparticle formulations encompassing 65 small molecules. The dataset aims to bridge existing data gaps and provide a comprehensive resource for research on nanoparticle formulations. Lactide ; Nanoparticle ; Molecule ; PLGA ; Chemistry ; Chemical engineering ; Nanotechnology ; Materials science ; Combinatorial chemistry ; Organic chemistry ; Polymer ; Engineering ; Copolymer Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Graphene and Nanomaterials Applications
Authors: Wahyu Aji Eko Prabowo | Muhamad Akrom | Supriadi Rustad | T. Sutojo | Hermawan Kresno Dipojono | Ryo Maezono | Febdian Rusydi
Source: Results in Surfaces and Interfaces (article, 2025-04-01)
Field: Chemistry — Inorganic Chemistry

Predicting CO2 adsorption in metal-organic frameworks: Integrating machine learning with virtual sample generation This study introduces a machine learning (ML) strategy that uses kernel density estimation (KDE) to generate virtual samples, significantly enhancing the prediction of CO 2 adsorption capacities in metal-organic frameworks (MOFs). Utilizing a dataset of 339 entries with features such as surface areas (S BET and S L ), pore volume (V T ), pressure (P), and temperature (T), we compared four ML models: Random Forest (RF), Gradient Boosting Decision Trees (GBDTs), Light Gradient Boosting Machines (LGBMs), and eXtreme Gradient Boost (XGBOOST). The incorporation of virtual samples led to notable improvements in model precision. For instance, correlation coefficients (R 2 ) improved, with RF increasing from 0.988 to 0.997, and root mean square error (RMSE) values decreased from 0.642 to 0.360. To demonstrate the model's generalizability, we applied it to the MOF-74 series, a new set of MOF materials not included in the training dataset. The predictions closely matched experimental and simulation results, validating our methodology as a reliable and efficient tool for evaluating and developing new MOF materials. This approach effectively bridges the gap between theoretical and experimental research in predicting CO 2 uptake. Adsorption ; Metal-organic framework ; Sample (material) ; Materials science ; Artificial intelligence ; Computer science ; Chemistry ; Chromatography ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Machine Learning in Materials Science
Authors: Zhen Jia Lim | Petar Žuvela | Šime Ukić | Mirjana Novak Stankov | Tomislav Bolanča | Mario Lovrić | Ming Wah Wong | Bogusław Buszewski
Source: ACS Omega (article, 2025-02-04)
Field: Chemistry — Spectroscopy

Gradient Retention Time Modeling in Ion Chromatography through Ensemble Machine Learning-Powered Quantitative Structure-Retention Relationships Quantitative structure-retention relationships (QSRRs) have been a popular modeling approach in ion chromatography to predict retention time from molecular structures. It is often coupled with solvent strength models to extend it to other isocratic chromatographic conditions. While this approach has achieved reasonable success, potential inconsistencies from the solvent strength model may propagate to the QSRR models, thereby amplifying their errors. In this work, we aim to incorporate information on the isocratic conditions directly into the QSRR model to reduce error propagation and build global models. Four machine learning approaches that can account for both global and local sources of variability in chromatographic retention, random forest regression, gradient boosting regression (GBR), extreme gradient boosting (xgBoost), and adaptive boosting (AdaBoost), were evaluated and compared. The partial least-squares model was built as a baseline to compare against. GBR and xgBoost have shown superior predictive ability among the evaluated models with root-mean-square errors (RMSEs) of isocratic retention of 0.025 (+0.009, -0.006) and 0.025 (+0.008, -0.006), respectively. Developed QSRR models were further incorporated into the isocratic-to-gradient model to predict gradient retention. GBR and xgBoost QSRR models have outperformed the other models with RMSEs of gradient retention of 0.358 (+0.199, -0.107) and 0.385 (+0.387, -0.139) min, respectively. Such an approach demonstrates the benefits of incorporating the eluent composition into prediction models, with the potential to extend to other chromatographic techniques. Retention time ; Chromatography ; Ion chromatography ; Ensemble learning ; Computer science ; Chemistry ; Artificial intelligence Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies