jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kenneth René Simonsen | Mohammad Ostadi | Maciej Zychowski | Simon Pedersen | Mads Valentin Bram
Source: Processes (article, 2026-01-04)
Field: Materials Science — Materials Chemistry

Review of CO2 Corrosion Modeling for Carbon Capture, Utilization and Storage (CCUS) Infrastructure CO2 corrosion remains a critical challenge for the safe and reliable operation of Carbon Capture, Utilization, and Storage (CCUS) infrastructure. This review summarizes CO2 corrosion implications from material selection, exposure time, CO2 phase behavior, flow conditions, and impurities such as H2O, O2, SOx, NOx, and H2S. CO2 corrosion modeling has, since early works by de Waard in 1975, expanded to a wide range of models and software tools, many of which have already been reviewed and compared. This work provides a historical timeline and a comparative summary of models and software tools to assist in selecting models for CCUS applications. Modeling approaches are classified into empirical, semi-empirical, and mechanistic categories, with their assumptions, strengths, and limitations. CO2 corrosion modeling has persistent challenges relating to data quality, data quantity, and parameter interactions, which reduce model accuracy, especially for machine learning approaches. The provided perspective emphasizes that machine learning and hybrid modeling approaches for CO2 corrosion prediction are gaining popularity, and their effectiveness is currently limited by the quality and quantity of available corrosion data. The provided opportunities include recommendations for standardized experimental procedures and hybrid modeling strategies that combine physics-based insights from mechanistic modeling approaches with data-driven machine learning approaches. Corrosion ; Timeline ; Work (physics) ; Carbon steel ; Software ; Learning curve ; Data modeling Corrosion Behavior and Inhibition ; CO2 Sequestration and Geologic Interactions ; Carbon Dioxide Capture Technologies
Authors: A. Nyíri | Michael J. Waters | James M. Rondinelli
Source: ACS Photonics (preprint, 2026-02-21)
Field: Materials Science — Materials Chemistry

Normalized Descriptor for Unbiased Screening of Second-Order Nonlinear Optical Materials Second-order nonlinear optical materials enable frequency doubling of light (second-harmonic generation, SHG), which is essential for optoelectronic applications ranging from materials characterization to quantum technologies. However, comparing SHG performance across materials remains challenging as the second-order nonlinear susceptibility, χ(2), spans several orders of magnitude and strongly depends on the band gap, Eg. To address this, we empirically validate a theoretical upper bound on χ(2) using new databases of ab initio-computed nonlinear optical (NLO) properties. We then formulate a normalized descriptor, d̂, which expresses the NLO response of a material relative to the band gap-dependent physical limit. We show that d̂ exhibits a similar distribution across a wide range of band gap energies. We also demonstrate that the formalism used to compute χ(2) leads to a competition with methodological uncertainty driven by band gap predictions to systematically alter the prediction of the second-order nonlinear response. The universality of d̂ supports its use as a robust, generalizable metric for chemistry-informed machine-learning models and quantification of NLO performance, enabling accelerated materials discovery and optimization across broad application frequencies. Nonlinear system ; Ranging ; Nonlinear optical ; Band gap ; Characterization (materials science) ; Universality (dynamical systems) ; Upper and lower bounds Machine Learning in Materials Science ; 2D Materials and Applications ; Crystal Structures and Properties
Authors: Kapil Dev Mahato
Source: Journal of Photochemistry and Photobiology A Chemistry (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

Predicting power conversion efficiency in donor-acceptor pairs for organic solar cells using machine learning ensemble models Machine learning (ML) has emerged as a powerful approach for accelerating the design of efficient organic solar cells (OSCs) by correlating molecular features with device performance. However, predictive modeling of power conversion efficiency (PCE) remains challenging due to the small size and heterogeneity of available experimental datasets. In this study, we propose a robust and data-efficient ensemble learning framework for accurately predicting PCE in donor-acceptor (D-A) molecular pairs. A dataset of 319 experimentally derived D-A combinations, containing key electronic and molecular descriptors, was employed to develop five regression models: Fine Tree (FT), Medium Tree (MT), Coarse Tree (CT), Bagged Tree (BGT), and Boosted Tree (BST). Among the models examined, the BST ensemble outperformed the others, achieving an R 2 of 88.75%, a minimum MAE of 0.522, and an RMSE of 0.725 for validation, as well as an R 2 of 85.26%, a minimum MAE of 0.549, and an RMSE of 0.734 for testing. The proposed framework integrates SMILES-derived molecular fingerprints with ensemble learning to capture complex, nonlinear interactions between donor and acceptor features, enabling the reliable estimation of efficiency even with limited data. This work highlights that data-driven ensemble approaches can serve as accurate and computationally economical methods for estimating the physical/chemical properties of OSC. The outcomes are expected to enable researchers to conduct rapid screening and develop next-generation OSC materials. • This study demonstrates the ML ensemble models to accurately predict the PCE of OSCs. • The BST ensemble model achieved the best accuracy, with an R 2 of 88.75% for validation and 85.26% for testing. • The outcomes help researchers accelerate the discovery of OSCs in the laboratory. Ensemble learning ; Ensemble forecasting ; Decision tree ; Tree (set theory) ; Mean squared error ; Regression ; Random forest ; Energy conversion efficiency ; Predictive modelling Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Advanced Memory and Neural Computing
Authors: Kunshan Yao | Yexin Xu | Jun Sun | Bing Zhou | Bing Zhang | Xiaojiao Du | Y Zhang
Source: Food Research International (article, 2026-01-20)
Field: Chemistry — Analytical Chemistry

Simultaneous detection and visualization of lipid and protein oxidation in frozen-thawed chicken meat using hyperspectral imaging TBARS ; Lipid oxidation ; Hyperspectral imaging ; Thiobarbituric acid ; Convolutional neural network ; Lipid peroxidation ; Oxidative phosphorylation Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Listeria monocytogenes in Food Safety
Authors: Narashans Alok Sagar | Nitu Rani
Source: Frontiers in Food Science and Technology (article, 2026-01-09)
Field: Materials Science — Biomaterials

Recent trends and innovations in smart and AI-based food packaging: A review Food packaging is crucial for ensuring food safety, maintaining product quality, and reducing food waste. However, traditional packaging systems cannot provide real-time information or predictions about product status. This limitation impacts supply chain visibility and effective shelf-life management. To address these issues, integrating artificial intelligence (AI) with innovative packaging methods, such as smart, active, intelligent, and biodegradable packaging, has become a promising approach. AI-enabled food packaging creates more responsive, accurate, and sustainable systems. It allows for real-time spoilage detection, predicts shelf-life, and improves traceability through machine learning and blockchain technologies. This review discusses recent advancements in AI-integrated smart packaging. It includes AI-driven freshness sensors, data-driven traceability systems, and eco-friendly packaging materials, showcasing their potential to enhance food safety, improve supply chain efficiency, and reduce food waste. Furthermore, it emphasizes future research directions. These focus on next-generation biodegradable smart sensors, advanced machine learning models for predictions, and developing sustainable, scalable packaging systems to strengthen global food security. Traceability ; Food packaging ; Active packaging ; Supply chain ; Product (mathematics) ; Food safety ; Food systems ; Food supply ; Novel food Nanocomposite Films for Food Packaging ; Food Waste Reduction and Sustainability ; Food Supply Chain Traceability
Authors: Georgia Melagraki
Source: Beilstein Journal of Nanotechnology (article, 2026-01-16)
Field: Materials Science — Materials Chemistry

Safe and sustainable by design with ML/AI: A transformative approach to advancing nanotechnology Nanotechnology is revolutionizing different sectors such as medicine, energy, defence, and environmental science by enabling the development of materials and technologies with exceptional precision and efficiency. From advanced drug delivery systems to clean energy solutions, the applications of nanotechnology are diverse and transformative. However, these innovations are accompanied by complex challenges regarding safety and sustainability for both the nanoscale materials themselves and for the products containing them. The growing complexity of engineered nanomaterials calls for proactive strategies to mitigate potential risks while maintaining their functional benefits. The "Safe and Sustainable by Design" (SSbD) concept addresses these challenges by embedding safety measures and sustainability considerations into the earliest stages of material development. Advances in machine learning (ML) and artificial intelligence (AI) have further enhanced the effectiveness of SSbD by providing predictive modelling, risk assessment, decision-making tools, and the ability to computationally screen candidate materials before producing them. This perspective article highlights how ML and AI are driving the evolution of SSbD in nanotechnology, focussing on predictive toxicology, materials informatics, lifecycle analysis, and the pivotal role of digital twins. It also explores current challenges, emerging opportunities, and the path forward for integrating ML/AI-driven SSbD frameworks into regulatory and industrial practices. Transformative learning ; Sustainability ; Impact of nanotechnology ; Societal impact of nanotechnology ; Emerging technologies ; Sustainable development Nanoparticles: synthesis and applications ; Nanotechnology research and applications ; Machine Learning in Materials Science
Authors: Timo N. Schneider | Florence Stoffel | M. Buehler | Karolína Mrzílková | Milad Radiom | Paolo Arosio
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Engineering nanocondensate formation through sequence composition and patterning Abstract Many biological proteins can assemble into dynamic, non-stoichiometric structures known as biomolecular condensates. Although typically observed at micrometer scales in vitro, recent evidence shows that these condensates often appear as nanoscale assemblies both in vitro and in cells. Moreover, biochemical reactions can be more efficiently promoted in nanoscale condensates than in micron-sized droplets, due to mass-transfer limitations and interfacial effects. Therefore, in analogy with colloids, the function of condensate materials can be engineered by tuning their size distribution. However, controlling the size of condensates remains challenging, as the molecular mechanisms that prevent small condensates from coarsening into larger ones are still poorly understood. Here, we developed and applied a computational pipeline that combines high-throughput molecular simulations, machine learning, and mixed-integer linear programming to design phase-separating peptides that form metastable nanocondensates across a broad range of experimental conditions. In addition to experimentally validating these peptides, we elucidate the underlying molecular mechanisms and derive initial design rules. In particular, we show that scaffold net charge combined with sequence blockiness can lead to high phase separation propensity while simultaneously yielding low interfacial tension, thereby slowing ripening. Moreover, these combined properties induce an electrostatics-driven alignment of molecules at the interface, which generates an additional size-dependent coalescence barrier. We further show that these features are shared by biological proteins, providing a possible mechanistic basis for the widespread occurrence of nanocondensates in biological systems. Altogether, our findings shed light on the molecular mechanisms behind nanocondensate formation, and provide a platform to design nanocondensates for several potential applications in bioengineering and biotechnology. Abstract Figure Coalescence (physics) ; Nanoscopic scale ; Sequence (biology) ; Function (biology) ; Directed Molecular Evolution ; Molecular biophysics ; Biomolecule ; Design elements and principles Pickering emulsions and particle stabilization ; Diatoms and Algae Research ; Block Copolymer Self-Assembly
Authors: J. Thorben Frank | Stefan Chmiela | Klaus-Robert Müller | Oliver T. Unke
Source: Nature Machine Intelligence (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

Machine learning global atomic representations with Euclidean fast attention Euclidean geometry ; Euclidean distance ; Quadratic equation ; Representation (politics) ; Mechanism (biology) ; Component (thermodynamics) ; Euclidean group Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Zinuo Wang | Peng Zhou | Tao Zhang | Fuhui Wang
Source: Corrosion Science (article, 2026-01-14)
Field: Materials Science — Metals and Alloys

Bridge for the thermodynamics and kinetics of electrochemical corrosion: Influence of trace fluctuations in alloying element content on the corrosion resistance of low alloy steels Corrosion ; TRACE (psycholinguistics) ; Alloy ; Microstructure ; Trace element ; Bridge (graph theory) ; Kinetics Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Machine Learning in Materials Science
Authors: Snehal L. Kadam | Rahul S. Ingole | Yong Min Kim | Jun Seok Lee | Kwangjun Kim | Minwook Kim | Jong G. Ok
Source: Small Structures (article, 2026-03-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

From Lab to Life: Progress in Supercapacitor Electrode Materials for Real‐Life Electrochemical Energy Storage Applications The growing demand for sustainable, high‐performance energy solutions has intensified the need for advanced electrochemical electrode materials, particularly for real‐life applications, notably electric vehicles, grid stabilization, and portable electronics. Supercapacitors have become essential in this real‐world energy storage landscape due to their exceptional power density, rapid charge–discharge capability, and long operational lifetime. While various electrode materials and their electrochemical performances have been extensively studied, limited attention has been paid to the transition from laboratory‐scale development to real‐world application. This article fills this gap by exploring the latest advancements in supercapacitor electrode materials with a particular focus on their progression from laboratory innovation to practical real‐time energy storage and supply. Emphasizing the crucial role of electrode materials in optimizing electrochemical performance, we provide a thorough analysis of the state‐of‐the‐art supercapacitor technologies in terms of the electrode materials and their advanced processing methods. We further highlight the emerging integrations of machine learning techniques for predictive material discovery and synthesis optimization, and of the environmentally sustainable materials for dissemination of the high‐performance supercapacitors to our society. This article provides a clear roadmap for translating lab‐scale innovations into sustainable, market‐ready supercapacitor technologies, emphasizing the crucial role of advanced electrode materials. Supercapacitor ; Energy storage ; Electrochemical energy storage ; Electrode ; Electrochemistry ; Chemically modified electrode Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Machine Learning in Materials Science
Authors: Yanbang Tang
Source: Radiation Physics and Chemistry (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-driven prediction of gamma-ray shielding performance in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"> <mml:mrow> <mml:mi mathvariant="bold-italic">T</mml:mi> <mml:mi mathvariant="bold-italic">e</mml:mi> <mml:msub> <mml:mi mathvariant="bold-italic">O</mml:mi> <mml:mn mathvariant="bold">2</mml:mn> </mml:msub> <mml:mo linebreak="goodbreak" linebreakstyle="after">−</mml:mo> <mml:mi mathvariant="bold-italic">B</mml:mi> <mml:msub> <mml:mi mathvariant="bold-italic">i</mml:mi> <mml:mn mathvariant="bold">2</mml:mn> </mml:msub> <mml:msub> <mml:mi mathvariant="bold-italic">O</mml:mi> <mml:mn mathvariant="bold">3</mml:mn> </mml:msub> <mml:mo linebreak="goodbreak" linebreakstyle="after">−</mml:mo> <mml:mi mathvariant="bold-italic">Z</mml:mi> <mml:mi mathvariant="bold-italic">n</mml:mi> <mml:mi mathvariant="bold-italic">O</mml:mi> <mml:mo linebreak="goodbreak" linebreakstyle="after">−</mml:mo> <mml:mi mathvariant="bold-italic">B</mml:mi> <mml:mi mathvariant="bold-italic">a</mml:mi> <mml:msub> <mml:mi mathvariant="bold-italic">F</mml:mi> <mml:mn mathvariant="bold">2</mml:mn> </mml:msub> </mml:mrow> </mml:math> glasses Electromagnetic shielding ; Hyperparameter ; Attenuation ; Energy (signal processing) ; Linear regression ; False positive paradox ; Regression ; Mean squared error ; Mass attenuation coefficient Radiation Shielding Materials Analysis ; Nuclear materials and radiation effects ; Radioactivity and Radon Measurements
Authors: Qi Liu | Rui Yan | Zongliang Zhan | Junxuan Li | Zhaoqiang Jin | Liying Huang | Ke Liu | Xiaohai Tian | Shijie Shi | Yunbo Zhang
Source: Microchemical Journal (article, 2026-01-07)
Field: Chemistry — Analytical Chemistry

Rapid assessment of nitrogen content in vegetable leaves using interpretable AI-enhanced NIR spectroscopy: the case of oilseed rape Interpretability ; Partial least squares regression ; Feature selection ; Convolutional neural network ; Feature (linguistics) ; Variable (mathematics) ; Pattern recognition (psychology) ; Artificial neural network ; Monte Carlo method Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Alondra Hernandez Cedillo | Javier Mendez-Lozoya | Coral Hernandez Cedillo | Alfred L. M. Bothwell | Miguel José Yacamán
Source: Nanoscale Advances (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High reliability Ag/Ni/NiO nanowire-based SERS for cancer detection: a study on breast cancer Over the past decade, substantial research has focused on identifying cancer biomarkers using Raman spectroscopy. However, no commercial Raman-based diagnostic tests are currently available. A major barrier is the need to amplify the inherently weak Raman signal, typically achieved through Surface-Enhanced Raman Spectroscopy (SERS). Because SERS relies on nanostructured substrates, its clinical translation has been hindered primarily by poor reproducibility, which undermines data reliability and prevents regulatory approval. In this pilot study, we evaluate an improved SERS substrate based on Ag/Ni/NiO nanowires, previously reported by our group, which significantly enhances sensitivity, specificity, detection limits, and critically substrate reproducibility. We further strengthen analytical performance by incorporating machine learning methods for spectral interpretation. Using this optimized platform, we analyzed SERS spectra from tissue samples of 31 breast cancer patients and compared them with matched healthy controls. Our results demonstrate a clear spectral distinction between cancerous and non-cancerous tissue. More importantly, we showed that SERS combined with machine learning, can differentiate major breast cancer subtypes, including luminal A (LUMA), luminal B (LUMB), HER2-enriched (HER2), and triple-negative breast cancer (TNBC). PCA-LDA modeling yielded exceptional diagnostic metrics, achieving up to 99% accuracy, sensitivity, and specificity across multiple classification tasks. Although this pilot study includes a limited number of samples, the findings demonstrate that improved SERS substrates paired with machine-learning analysis can generate a unique molecular fingerprint of breast cancer and its subtypes. This work opens a pathway for developing assays, potentially using blood or saliva, to enable early detection and subtype identification based on SERS-derived cancer fingerprints. Breast cancer ; Cancer ; Reliability (semiconductor) ; Raman spectroscopy ; Human breast ; Cancer detection ; Substrate (aquarium) Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Junmeng Li | Dezhi Chen | Zhou Fang | Xing Hu | Xiaochuan Chen | Jun Xu | Yi Wang | Sheng Su | Song Hu | Jun Xiang
Source: Journal of Cleaner Production (article, 2026-01-27)
Field: Chemistry — Analytical Chemistry

Rapid prediction of cellulose, hemicellulose, and lignin content in biomass using FTIR based on transfer learning The content of cellulose, hemicellulose and lignin varies significantly from one biomass to another, and this difference can seriously affect the chemical reaction activity of the biomass, so it is important to clarify the content of these three components in the biomass. Compared with traditional wet chemical method, Spectroscopy has been applied to the rapid detection of biomass components because of its advantages of simple sample handling, rapidity, and accuracy detection methods. In this study, FTIR combined with machine learning algorithms was used to achieve the rapid detection of biomass three-component content. Eighty biomass three-component content and FTIR spectral data were obtained and spectral preprocessed. Four machine learning models (PLSR, LS-SVR, RF, ELM) were constructed and analyzed for model performance comparison. The results showed that LS-SVR had the best performance, with an R 2 is 0.983 and an R MSE is 1.74 for predicting cellulose, an R 2 is 0.952 and an R MSE is 6.35 for predicting hemicellulose, and an R 2 is 0.949 and an R MSE is 2.67 for predicting lignin. However, when applied to new biomass datasets, the predictive model yielded an R 2 value below 0.663. To enhance the model's generalization capability, this paper proposes a transfer learning algorithm centered on ε-WLS-SVR. Using only 10 % transfer samples, the ε-WLS-SVR model achieved an R 2 exceeding 0.927 and an R MSE below 7.48, significantly improving its generalization performance. In this study, FTIR spectroscopy combined with ε-WLS-SVR algorithm was proposed to rapidly predict the cellulose, hemicellulose and lignin contents in biomass. It also investigates model transfer optimization, providing an accurate, fast, and green method for detecting biomass composition. • FTIR combined with machine learning accurately predicted the three-component content of biomass. • A transfer learning algorithm centered on ε-WLS-SVR enhanced the model's generalization capability. • T The SHAP algorithm was employed for interpretability analysis of the black-box model. • The R 2 values for predicting the three-component content using the FTIR combined with ε-WLS-SVR model all exceeded 0.927. Hemicellulose ; Lignin ; Biomass (ecology) ; Fourier transform infrared spectroscopy ; Generalization ; Content (measure theory) Spectroscopy and Chemometric Analyses ; Biofuel production and bioconversion ; Advanced Cellulose Research Studies