jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wenjun Lu | Jiaxue Li | Jie Sun | Jia Bi
Source:  (article, 2025-11-14)
Field: Materials Science — Materials Chemistry

Research Progress on Data Mining-Based Corrosion Prediction Models for Metallic Materials Research into the corrosion of metallic materials is a discipline heavily reliant upon data. The investigation of corrosion patterns and mechanisms, the development of protective technologies, and the formulation of testing standards all depend upon corrosion data collected under various environmental conditions. This paper introduces data mining methods such as curve fitting, multiple linear regression analysis, grey system theory, artificial neural network, decision tree, and cluster analysis, along with their application in predicting the corrosion of metallic materials. It summarises and analyses the characteristics and application limitations of these different methods. Traditional single models typically exhibit limited accuracy and are prone to overfitting. Presently, most corrosion prediction models for metallic materials possess strict applicability boundaries. Consequently, the integrated application of multiple algorithms proves advantageous for broadening model utilisation limits and enhancing the precision of analytical or simulation outcomes. Corrosion ; Predictive modelling ; Artificial neural network ; Experimental data ; Mathematical model ; Linear regression Machine Learning in Materials Science ; Grey System Theory Applications ; Structural Integrity and Reliability Analysis
Authors: Grazielle Oliveira | Pedro Henrique Wegner | P. Carvalho | Fernando Augusto Pedersen Voll | Agnes de Paula Scheer | Rafael de P. Soares | Fabiane Oliveira Farias
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Enhanced Cosmo-Sac for Accurate Solubility Predictions Solubility ; Machine learning ; Artificial intelligence ; Computer science ; Chemistry ; Organic chemistry Crystallization and Solubility Studies ; Injection Molding Process and Properties ; Machine Learning in Materials Science
Authors: Fabian Berger (8813759) | Angelos Michaelides (1295379)
Source: Figshare (article, 2025-10-03)
Field: Materials Science — Materials Chemistry

When Are Dopant <i>d</i>‑States Free-Atom-Like?Periodic Trends and Confinement Effects in Single-Atom Alloys The free-atom-like character of dopant <i>d</i>-states is a defining feature of single-atom alloys (SAAs), contributing to their exceptional selectivity and activity in heterogeneous catalysis. Based on reliable density functional theory (DFT) calculations for the full series of 4<i>d</i> transition metal (TM) dopants embedded in various hosts, we provide a unified perspective on when dopants exhibit this characteristic and how it can be tuned. Only late TM dopants exhibit the narrow, free-atom-like <i>d</i>-bands typically associated with SAAs, whereas early TM dopants display significantly broader <i>d</i>-bands that approach the width of host metal <i>d</i>-bands. This variation is not driven by increased dopant–host <i>d</i>-state mixing, which remains minimal across the series. Instead, we attribute the observed periodic trend to differences in the spatial extent of the localized dopant states and their overlap with surrounding host atoms, as well as to avoided hybridization associated with <i>d</i>-state filling. We further corroborate that dopant confinement, quantified by the number and proximity of surrounding host atoms, is as a key factor: more confined dopants exhibit broader <i>d</i>-bands, whereas less confined dopants feature narrower <i>d</i>-bands. Reduced dopant confinement also stabilizes high-spin configurations, enhancing spin polarization for certain 4<i>d</i> elements. Together, these findings offer fundamental insights into the origins of the unique electronic structure of SAAs. Building on these findings, we establish design principles for tuning dopant <i>d</i>-band shape and spin and illustrate how such modifications impact catalytic selectivity. The developed guidelines are also encapsulated in a machine learning model that predicts <i>d</i>-band widths, facilitating the rational design of SAAs. Dopant ; Doping ; Population ; Density functional theory ; Transition metal ; Spin (aerodynamics) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Fabiola Eugelio | Marcello Mascini | Elettra Marone | Federico Fanti | Sara Palmieri | Manuel Sergi | Michele Del Carlo | Darío Compagnone
Source: Journal of Agricultural and Food Chemistry (article, 2025-09-05)
Field: Chemistry — Analytical Chemistry

Phenolic Profile as a Powerful Machine Learning Tool for Identification, Traceability, and Quality Control of Olive Cultivars This study investigates the phenolic and fatty acid profiles of olives from four <i>Olea europaea</i> cultivars (Arbequina, Arbosana, Frantene, and Koroneiki), widely grown in the Mediterranean region and collected at different ripening stages in Italy. The aim was to assess the potential of olive chemical profiles as markers for cultivar classification using machine learning algorithms, including Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM). Results showed that phenolic profiling achieved significantly higher classification accuracy than fatty acids across all models. Using phenolic data, RF and SVM achieved 98% accuracy, while for fatty acids, the best-performing model was NB, reaching just 65%. Importantly, minor phenolic compounds such as chlorogenic acid, ferulic acid, and apigenin were crucial for classification. These findings demonstrate that machine learning combined with phenolic profiling can improve cultivar identification, traceability, and quality control in the olive sector. Traceability ; Cultivar ; Quality (philosophy) ; Identification (biology) ; Biotechnology ; Food quality ; Food science ; Biology ; Agronomy ; Botany ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Advanced Chemical Sensor Technologies
Authors: Jian‐Ping Zou | Zhanghao Zhouyin | Shishir Kumar Pandey | Qiangqiang Gu
Source: Chinese Physics B (article, 2025-10-22)
Field: Materials Science — Materials Chemistry

Review of machine learning tight-binding models: Route to accurate and scalable electronic simulations Abstract The rapid advancement of machine learning based tight-binding Hamiltonian (MLTB) methods has opened new avenues for efficient and accurate electronic structure simulations, particularly in large-scale systems and long-time scenarios. This review begins with a concise overview of traditional tight-binding (TB) models, including both (semi-)empirical and first-principles approaches, establishing the foundation for understanding MLTB developments. We then present a systematic classification of existing MLTB methodologies, grouped into two major categories: direct prediction of TB Hamiltonian elements and inference of empirical parameters. A comparative analysis with other ML-based electronic structure models is also provided, highlighting the advancement of MLTB approaches. Finally, we explore the emerging MLTB application ecosystem, highlighting how the integration of MLTB models with a diverse suite of post-processing tools from linear-scaling solvers to quantum transport frameworks and molecular dynamics interfaces is essential for tackling complex scientific problems across different domains. The continued advancement of this integrated paradigm promises to accelerate materials discovery and open new frontiers in the predictive simulation of complex quantum phenomena. Suite ; Scalability ; Inference ; Quantum ; Open research ; Deep learning Machine Learning in Materials Science ; Scientific Computing and Data Management ; Neural Networks and Applications
Authors: Ting Su (640554) | Chao Rong (2032945) | Yabin Yan (3606812) | Fuzhen Xuan (5859824)
Source: Figshare (article, 2025-08-18)
Field: Materials Science — Materials Chemistry

Tensile MechanicalProperties and Edge Defect-DrivenDegradation in Bilayer Graphene Bilayer graphene attracts significant attention due to its unique electronic structure and excellent physical properties, with its mechanical performance being crucial for understanding deformation mechanisms and assessing application reliability. The mechanical properties of bilayer graphene measured by atomic force microscopy currently exhibit considerable scatter and show clear deviations from theoretical predictions. In situ tensile testing is widely regarded as a more reliable and authoritative approach for evaluating the mechanical properties of two-dimensional materials. Accordingly, the Young’s modulus of bilayer graphene is measured to be 873.80 ± 12.68 GPa using a push-to-pull device inside a scanning electron microscope, which is close to the theoretical value. Moreover, the integration of bilayer graphene into device architectures requires micro/nanoscale patterning and shaping, which inevitably introduces edge defects. However, it remains experimentally challenging to precisely control the concentration of these edge defects. To address these limitations, a combined approach of molecular dynamics simulations and machine learning was employed to systematically uncover the effects of edge defects on the mechanical behavior of bilayer graphene. This study provides a theoretical foundation for a deeper understanding and optimization of the mechanical behavior of bilayer graphene, thereby laying important groundwork for its application in microelectronic devices. Bilayer ; Graphene ; Bilayer graphene ; Enhanced Data Rates for GSM Evolution ; Nanoindentation ; Ultimate tensile strength ; Microelectronics Graphene research and applications ; 2D Materials and Applications ; Nanowire Synthesis and Applications
Authors: Vinay Awasthi | А. В. Иванов
Source: Preprints.org (preprint, 2025-01-21)
Field: Materials Science — Materials Chemistry

Modeling Charged Atoms for Atomistic Dynamics, using Equivariant GNNs Graph Neural Networks have been quiet successful in modeling inter-particle interactions at quantum level for datasets carrying force-fields and atomic charge, detecting efficacy of a drug compounds, preventing further harmful chemical reactions such as drugs targeting cancerous cells, preventing tumor growth (protein ligand interactions). There are various other applications of this approach where one can model inter graph interactions, among nodes belonging to different graphs, using cutoff-radius based sphere as receptive field such as recommendation system taking into account of user&amp;rsquo;s interactions with influencers on other social networks, interactions among various sets of swarm drones, spread of viruses among multiple species of birds, based on their interactions across flight paths etc... Dataset such as SPICE [3], have information about cutoff-radius embed in them. Selecting right cutoff-radius is key parameter for getting reasonable outcome. Next is AGGR function as authors have notices that charge should not be approximated using SUM but MAX to get current bond-length measure at atomic level. For other domains, prior domain knowledge should guide these decisions. We present a e3nn, nequip, Allegro based GNN with slight modification for SPICE dataset carrying charge, to detect right bond length even with short training of 100 epochs. Ideas presented here can be applied to other node interactions belonging to various graphs. Here we show how we detected ammonium ion N-H bond length, where one N-H bond out of four carried charge. We have a Jupyter notebook for readers to try our approach in less than ten minutes. Equivariant map ; Dynamics (music) ; Statistical physics ; Physics ; Mathematics ; Pure mathematics Machine Learning in Materials Science ; Nuclear Physics and Applications ; Advanced Physical and Chemical Molecular Interactions
Authors: Q. Ge | Weiping Zhu | Hongjiu Hu
Source: Materials Today Communications (article, 2025-03-13)
Field: Materials Science — Materials Chemistry

An inflatable bilayer graphene: Enhancement of impact resistance and structural efficiency Materials science ; Inflatable ; Graphene ; Bilayer ; Bilayer graphene ; Nanotechnology ; Impact resistance ; Composite material ; Engineering physics ; Membrane ; Mechanical engineering Graphene research and applications ; Graphene and Nanomaterials Applications ; Carbon Nanotubes in Composites
Authors: Chao Li | Wenrui Cao | Meiyan Meng | Qiao Huang | Kui Fu | Yong Xu
Source:  (article, 2025-08-29)
Field: Materials Science — Biomaterials

Research on Silkworm Cocoon Image Recognition Based on YOLOv7 Algorithm The silkworm cocoon industry, a vital component of China's traditional agriculture, not only provides the primary raw material for silk production but also plays important roles in the food, pharmaceutical, and chemical industries. This study uses image recognition research with the YOLOv7 algorithm to improve the efficiency of cocoon screening processes and, as a result, the productivity of silk reeling operations and silk product quality. Cocoon images were collected in the laboratory and compiled into a dataset of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{2, 0 8 0}$</tex> images. Each image contains different-shaped cocoons. Annotation tools were used to label four categories of cocoons: prime cocoons, double cocoons, yellow-spotted cocoons, and deformed cocoons. The YOLOv7 object detection network from deep learning was used for model training and validation. The results show that the YOLOv7 model accurately recognizes cocoon images. The trained model has high recognition accuracy for prime cocoons and yellowspotted cocoons, but low recognition accuracy for double cocoons and deformed cocoons. This study can serve as a foundation for future research into intelligent cocoon recognition and the development of screening equipment. Image (mathematics) ; Pattern recognition (psychology) ; SILK ; Prime (order theory) ; Deep learning ; Annotation ; Component (thermodynamics) ; Cognitive neuroscience of visual object recognition ; Image processing Silk-based biomaterials and applications ; Silkworms and Sericulture Research ; Date Palm Research Studies
Authors: Krishna Sai Koushik Achanta | Demetrius Finley | Zeinab Hajali Fard | Luke T. Roling | George A. Kraus | Jean‐Philippe Tessonnier
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Rational Design of Indole Thiosemicarbazone Inhibitors for Enhanced Mild Steel Corrosion Inhibition Corrosion inhibitors play a critical role in mitigating mild steel deterioration, a pervasive challenge in various industries. Aromatic molecules containing heteroatoms (N, O, S), such as pyrroles, pyridines, imidazoles, triazoles, thiazoles, and quinazolinones, have demonstrated notable inhibition efficiency against mild steel corrosion. The incorporation of functional side groups further enhances their performance. However, most studies rely on empirical, trial-and-error approaches, which are time-consuming and resource-intensive, limiting the systematic development of optimized inhibitors. In this study, we present a rational design strategy for corrosion inhibitors, utilizing indole-3-carboxaldehyde derivatives as a molecular platform. Indole-3-carboxaldehyde is a naturally occurring compound in human and animal digestive systems, formed from the bacterial degradation of L-tryptophan, a prevalent dietary amino acid. Instead of employing conventional heuristic methods, we systematically derivatized the indole structure to generate a library of structurally related molecules to establish structure-activity relationships (SAR) that can enhance inhibitor performance predictability. To further enhance predictive efficiency, we generated virtual molecular structures and quantum chemical parameters such as highest occupied molecular orbital energy (E HOMO ), lowest unoccupied molecular orbital energy (E LUMO ), energy gap (ΔE), dipole moment, and electron transfer number (ΔN) were computed for both synthesized and virtual molecules. These parameters provide insight into electronic properties and inhibition efficiency for prediction models, facilitating the determination of promising inhibitors. By generating structured datasets, this study lays the foundation for machine learning (ML)-guided models to predict inhibitor performance, offering a data-driven strategy to accelerate the identification of highly effective corrosion inhibitors. Overall, this study introduces a novel methodology for designing and evaluating corrosion inhibitors, leveraging a bio-based molecule. Our findings contribute to the growing efforts to replace traditional empirical methods with a systematic, predictive framework that enhances efficiency, and advances the understanding of corrosion inhibition mechanisms. Rational design ; Corrosion inhibitor ; Molecule ; Virtual screening ; Indole test ; Corrosion ; HOMO/LUMO ; Drug design ; Heteroatom Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Computational Drug Discovery Methods
Authors: Sungyong Shim | Tuyen Tran | Dae Hyun Choi | Duksun Han
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Spectroscopy

Self-Absorption Correction in Libs-Based Lithium Isotope Analysis with a Modified 1d U-Net Lithium (medication) ; Isotope ; Net (polyhedron) ; Absorption (acoustics) ; Materials science ; Environmental science ; Analytical Chemistry (journal) ; Chemistry ; Environmental chemistry ; Physics ; Nuclear physics ; Mathematics ; Internal medicine ; Composite material ; Medicine Mass Spectrometry Techniques and Applications ; Radioactive contamination and transfer ; Isotope Analysis in Ecology
Authors: Sepehr Hejazi (20378406) | Ryan Godin (21366313) | Vito Jurasic (21366316) | Nigel F. Reuel (1278930)
Source: Figshare (article, 2025-05-15)
Field: Materials Science — Biomaterials

Single-WalledCarbon Nanotube Probes for ProteaseCharacterization Directly in Cell-Free Expression Reactions Proteins can be rapidly prototyped with cell-free expression (CFE) but in most cases there is a lack of probes or assays to measure their function directly in the cell lysate, thereby limiting the throughput of these screens. Increased throughput is needed to build standardized, sequence to function data sets to feed machine learning guided protein optimization. Herein, we describe the use of fluorescent single-walled carbon nanotubes (SWCNT) as effective probes for measuring protease activity directly in cell-free lysate. Substrate proteins were conjugated to carboxymethyl cellulose-wrapped SWCNT, yielding stable and sensitive probes for protease detection with a detection limit of 6.4 ng/mL for bacterial protease from Streptomyces griseus. These probes successfully measured subtilisin activity in unpurified CFE reactions, surpassing commercial assays. Furthermore, they enabled continuous monitoring of activity during synthesis of subtilisin in both purified and lysate-based CFE systems without compromising protein expression. Surface passivation techniques, such as preincubation with cell lysate and supplement components, reduced the initial signal loss and improved probe signal stability in the complex cell lysate environment. These modular probes can be used, as described, for high-throughput screening and optimization of proteases and, with the change of conjugated substrate, a wider range of other hydrolases. Subtilisin ; Proteases ; Protease ; Lysis ; Conjugated system ; Cleave ; Carbon nanotube Diatoms and Algae Research ; Carbon Nanotubes in Composites ; Advanced Biosensing Techniques and Applications
Authors: R. Osborn | Stephan Rosenkranz | Matthew Krogstad
Source: Structural Dynamics (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

New Computational Approaches to the Analysis of Single Crystal Diffuse Scattering The power of single crystal diffuse scattering in probing the role of inhomogeneity in material properties has long been recognized by the crystallography community [1]. The method is sensitive to three-dimensional structural correlations over length scales of 5 to 200Å or more, from local relaxations around point defects to nanoscale short-range order. However, in the past, experimental and computational challenges have hindered its widespread adoption as a tool for characterizing disordered materials. With recent advances in both neutron and x-ray instrumentation, it is now routinely possible to measure large volumes of reciprocal space, containing hundreds and often thousands of Brillouin zones, on time scales ranging from a few minutes with synchrotron x-rays to a few hours with neutrons. Such speeds enable diffuse scattering data to be collected as a function of temperature and composition, allowing the evolution of structural fluctuations to be tracked across entire phase diagrams in a matter of days. I will highlight a number of studies where new computational approaches enabled by recent experimental progress have been developed to reveal new insights into the role of structural inhomogeneity, particularly in correlated electron materials [2]. For example, unsupervised machine learning has been used to cluster data voxels according to their common temperature dependences, characterizing both order parameters and fluctuations such as Goldstone Modes and Bragg Glass correlations [3, 4], while 3D-ΔPDF analysis has provided new ways of determining the character and length scale of the structural response to electronic phase transitions, such as charge- density-waves and metal-insulator transitions [5, 6]. Finally, I will also discuss the computational framework that we have developed in order to facilitate data analysis in real-time during measurements [7]. Neutron scattering ; Reciprocal lattice ; Scattering ; Brillouin zone ; Cluster (spacecraft) ; Phase (matter) ; Synchrotron radiation ; Length scale ; Phase transition X-ray Diffraction in Crystallography ; Glass properties and applications ; Multiferroics and related materials
Authors: Aneta Furmanova | Viktor Hruška | Aleksandra Bajicová
Source:  (article, 2025-12-25)
Field: Materials Science — Materials Chemistry

Machine learning for modelling band structure properties Although there are various analytical approaches and numerical methods for solving sonic crystal problems, analytical expressions for modelling the band structure properties are limited to a few special cases.The access to a numerical model offers a solid foundation for data-driven discovery.In our approach, we employed the Webster equation for the unit cell and Floquet-Bloch theory for periodic structures, with the waveguide parametrized by cubic splines.To extract analytical formulae linking the waveguide geometry to the corresponding dispersion relation, we applied methods of physics-informed machine learning, such as coordinate transformation and symbolic regression.These results provide a deeper understanding of the underlying principles and serve as an efficient alternative to computationally demanding numerical optimization.Moving toward a Schrdinger-like equation and parametrization by Gaussian curvature allows for a more multiphysical approach, yet it also presents challenges related to the feasibility limits of the geometry. Feature (linguistics) ; Noise (video) ; Stability (learning theory) ; Artificial neural network ; Support vector machine Machine Learning in Materials Science ; Computational Physics and Python Applications ; Advanced Power Amplifier Design