jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ali M. A. Daban | Siti Armiza Mohd Aris | Mohd Syahid Mohd Anuar
Source:  (article, 2025-10-27)
Field: Materials Science — Materials Chemistry

Steel Classifications Based on Sparks Patterns Using Convolutional Neural Network ResNet Pre-Trained Models Steel plays a vital role in numerous industries due to its strength, versatility, and wide-ranging applications. However, its production is highly energy-intensive and a significant contributor to global CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions, raising environmental concerns. Recycling steel offers a more sustainable alternative, yet accurately classifying different steel types remains a persistent challenge, especially in resource-limited settings. This study introduces an automated method for steel classification based on the traditional spark test, utilizing pre-trained Convolutional Neural Networks (CNNs). Specifically, ResNet34 and ResNet101 models were trained on spark test images to evaluate their classification performance. ResNet34 achieved a notable accuracy of 95.0%, outperforming the previously used ResNet50 model, which recorded 92.6%. In contrast, ResNet101 significantly underperformed with an accuracy of just 20.6%. These results demonstrate the practical potential of deep learning models, particularly ResNet34, to enhance the efficiency of steel recycling processes, ensure higher material quality, and contribute to environmental sustainability through automated, precise classification. Convolutional neural network ; SPARK (programming language) ; Artificial neural network ; Residual neural network ; Production (economics) ; Deep learning Machine Learning in Materials Science ; Mineral Processing and Grinding ; Iron and Steelmaking Processes
Authors: Zhengyi Zhu | Xue Chen | Hao Yang | Yashuai Han | Lin Zheng | Xiaolong Zhang
Source:  (article, 2025-04-18)
Field: Materials Science — Materials Chemistry

Water leakage detection for HVDC converter hall based on laser illumination and deep learning Water leakage in HVDC converter halls, often caused by material degradation due to environmental factors or improper installation, poses serious threats such as short circuits and system failures, which can result in significant operational disruptions in DC transmission. Conventional detection techniques mainly target large-scale water seepage or accumulation, but these methods tend to be slow and less effective in the intricate and sensitive conditions found in converter halls. In this paper, we propose a new leakage detection approach that uses laser illumination to make water droplets more visible, combined with a deep learning framework that integrates a frame-difference-based multi-modal feature convolutional neural network (FDFM-CNN). The laser illumination serves to highlight the precise locations of the water droplets, facilitating their capture in images. Meanwhile, the CNN model processes video frames over time, filtering out background noise and concentrating on the areas where droplets are located. By adding numerical features such as connected regions and distribution patterns, this method substantially improves the accuracy and responsiveness of leakage detection in complex environments. The results from experiments confirm that this approach surpasses existing methods, providing an efficient and scalable solution for monitoring leakage in HVDC converter halls. Leakage (economics) ; HVDC converter ; Materials science ; Computer science ; Electrical engineering ; Electronic engineering ; Optoelectronics ; Engineering ; Voltage High voltage insulation and dielectric phenomena ; High-Voltage Power Transmission Systems ; Power Transformer Diagnostics and Insulation
Authors: Weihsueh A. Chiu | Alexandra C. Cordova | Thomas J. McDonald | James N. Dodds | Ivan Rusyn | Rui Zhu | Han-Hsuan D Tsai | Dillon Lloyd | Fred A. Wright | Galen Newman | Alina T. Roman‐Hubers
Source: UNC Libraries (article, 2025-07-26)
Field: Chemistry — Spectroscopy

Application of Ion Mobility Spectrometry–Mass Spectrometry for Compositional Characterization and Fingerprinting of a Library of Diverse Crude Oil Samples Exposure characterization of crude oils, especially in time-sensitive circumstances such as spills and disasters, is a well-known analytical chemistry challenge. Gas chromatography-mass spectrometry is commonly used for "fingerprinting" and origin tracing in oil spills; however, this method is both time-consuming and lacks the resolving power to separate co-eluting compounds. Recent advances in methodologies to analyze petroleum substances using high-resolution analytical techniques have demonstrated both improved resolving power and higher throughput. One such method, ion mobility spectrometry-mass spectrometry (IMS-MS), is especially promising because it is both rapid and high-throughput, with the ability to discern among highly homologous hydrocarbon molecules. Previous applications of IMS-MS to crude oil analyses included a limited number of samples and did not provide detailed characterization of chemical constituents. We analyzed a diverse library of 195 crude oil samples using IMS-MS and applied a computational workflow to assign molecular formulas to individual features. The oils were from 12 groups based on geographical and geological origins: non-US (1 group), US onshore (3), and US Gulf of Mexico offshore (8). We hypothesized that information acquired through IMS-MS data would provide a more confident grouping and yield additional fingerprint information. Chemical composition data from IMS-MS was used for unsupervised hierarchical clustering, as well as machine learning-based supervised analysis to predict geographic and source rock categories for each sample; the latter also yielded several novel prospective biomarkers for fingerprinting of crude oils. We found that IMS-MS data have complementary advantages for fingerprinting and characterization of diverse crude oils and that proposed polycyclic aromatic hydrocarbon biomarkers can be used for rapid exposure characterization.&nbsp; Ion-mobility spectrometry ; Crude oil ; Characterization (materials science) ; Petroleum ; Tracing ; Mass spectrometry ; Workflow ; Diamondoid Mass Spectrometry Techniques and Applications ; Petroleum Processing and Analysis ; Analytical chemistry methods development
Authors: Anna Katharina Picha (22233654) | Marcus Wieder (3685414) | Stefan Boresch (604024)
Source: Figshare (article, 2025-09-11)
Field: Materials Science — Materials Chemistry

Transferable NeuralNetwork Potentials and CondensedPhase Properties Transferable neural network potentials (NNP) are undergoing rapid development. Many practical applications of NNPs focus on single molecules; e.g., using NNPs as a fast replacement for quantum chemical methods for dihedral angle scans in force field development. Similarly, the reference data on which most transferable NNPs have been trained are single molecule properties. As NNPs are beginning to be used to simulate more complex systems, such as solute–solvent simulations, the question arises whether the current generation of transferable NNPs is accurate enough to reproduce condensed phase properties, which in most cases are outside the training domain of the models. Here we present a first analysis of how well two transferable NNPs (ANI-2x, MACE-OFF23(S/M)) perform in reproducing properties such as density, heat of vaporization, heat capacity, and isothermal compressibility of several pure liquids (water, methanol, acetone, benzene, <i>n</i>-hexane at room temperature, and <i>N</i>-methylacetamide at 100 C). In addition, we examine selected radial distribution functions and the self-diffusion constant. We find specific weaknesses for each of the models, and seemingly small flaws lead to poor performance when applied to condensed phase simulations. The varied outcomes observed with the machine learning potentials suggest that, currently, selecting an architecture or model for all-NNP simulations of real-world applications requires careful consideration and testing. Phase (matter) ; Artificial neural network ; Work (physics) ; Focus (optics) ; Domain (mathematical analysis) ; Field (mathematics) ; Isothermal process Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Physical and Chemical Molecular Interactions
Authors: Vadim Korolev | Alexey Andreevich Sorokin | Yuri Kuratov
Source: Journal of Chemical Information and Modeling (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

gSelformer-MV: Multiview, Subgraph-Augmented Group SELFIES Transformer for Molecular Property Prediction Data-driven approaches are essential for relating properties to the chemical structure. Atom-focused views of individual compounds are common in molecular representation learning: graph neural networks and chemical language models, the two main algorithm classes, take atomic-level graphs and atom-wise token sequences as input, respectively. However, directly integrating information about functional groups into advanced architectures remains nearly unexplored. To fill this gap, we introduce gSelformer-MV, a transformer that operates on multiple views of Group SELFIES (a SELFIES variant augmented with tokens for functional groups) that enables representation at both the atomic and substructure levels. Unlike prior Group SELFIES approaches that produce a single string per molecule, gSelformer-MV constructs multiple subgraph-partitioned Group SELFIES views and uses them jointly during training and inference. We show that gSelformer-MV is superior in terms of accuracy and explainability to the models trained exclusively on SELFIES strings. Moreover, gSelformer-MV achieves state-of-the-art performance on several regression benchmarks; further gains are obtained when restricting to high-confidence predictions. These results indicate that subgraph augmentation is a simple and effective route for advancing string-based molecular property prediction. Substructure ; Property (philosophy) ; Molecular graph ; Transformer ; Graph ; Security token ; Representation (politics) ; Training set Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Peng Ai (331334) | Shuwei Tang (3298953) | Shulin Bai (9415043) | Da Wan (3837829) | Xiaodong Li (181281) | Wanrong Guo (4220863) | Tuo Zheng (18592851) | Hao Wang (39217) | Pengfei Zhang (32384)
Source: Figshare (article, 2025-05-19)
Field: Materials Science — Materials Chemistry

Synergistically Tuning Thermoelectric Properties ofBaCdPF via Strain Engineering The layered BaCdPF compound with the ZrSiCuAs-type structure emerges as a promising thermoelectric (TE) material due to the excellent electronic transport properties under the <i>p</i>-type doping circumstance. However, the fundamental mechanisms that regulate its thermal and electronic transport under strain engineering remain largely unexplored, hindering its practical applications. In current work, the crystal structure, thermal transport, and electronic transport properties of layered BaCdPF under strain engineering are systematically investigated by first-principles calculations in combination with a machine-learning interatomic potential approach. The BaCdPF maintains mechanical robustness, high dynamic and thermal stabilities across a wide range of strain conditions. The coexistence of band degeneracy and anisotropic band dispersions (heavy and light bands) at the valence band maximum plays a critical role in enhancing electronic transport properties. Under tensile strain, the chemical bond softening and four-phonon scattering favors two-dimensional (2D) phonon transport characteristics, yielding a low lattice thermal conductivity of 1.08 W m<sup>–1</sup>K<sup>–1</sup> at 300 K under a −4% tensile strain. Conversely, compressive strain significantly enhances the carrier mobility, promoting three-dimensional (3D) electronic transport. The current work not only provides effective strategies for optimizing the TE performance of layered BaCdPF but also sheds light on the distinct interplay of 3D charge and 2D phonon transport under strain engineering, which offers broader implications for the design of strain-modulated TE materials. Strain engineering ; Thermoelectric effect ; Phonon ; Thermal conductivity ; Thermoelectric materials ; Doping ; Band gap ; Electronic band structure ; Phonon scattering Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; MXene and MAX Phase Materials
Authors: Yu Su | Kongxiang Wang | Xiang Guan | Yumao Wu | Hong Zhang | Fengxian Xie | Junhao Chu
Source: Advanced Energy Materials (article, 2025-12-12)
Field: Materials Science — Materials Chemistry

Machine Learning Descriptors for Mapping Structure‐Property‐Performance Relationships of Perovskite Solar Cells ABSTRACT In recent years, machine learning (ML) has emerged as a versatile tool for accelerating the development of perovskite solar cells (PSCs). A key challenge, however, lies in the scarcity of researchers possessing deep expertise in both material science and artificial intelligence. Pivotal to bridging this gap is ML descriptors, mediating between the empirical language of materials and the numerical inputs for ML algorithms. By translating domain knowledge into computationally tractable forms, the descriptors significantly enhance the model interpretability and empower researchers to uncover the underlying physical mechanisms governing behavior of PSCs. Therefore, it is crucial to overview the efforts translating the structure, property of perovskite materials and performance of PSCs into numerical descriptors compatible with ML models. This review summarized (1) the encoding of crystal structure in perovskites; (2) the quantification of microstructures in perovskite films; (3) the stability assessment of perovskite materials and devices. By synthesizing progress in these aspects, this work lays a solid foundation for constructing a universal model to elucidate the structure‐property‐performance relationships in PSCs, especially in forward prediction and backward inference. Interpretability ; Perovskite (structure) ; Bridging (networking) ; Stability (learning theory) ; Domain (mathematical analysis) ; Property (philosophy) ; Deep learning ; Key (lock) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Inorganic Chemistry and Materials
Authors: Mangalassery, Neethu Mohan | Singh, Abhishek Kumar
Source: arXiv (Cornell University) (preprint, 2025-12-08)
Field: Materials Science — Materials Chemistry

Edge-Aware Graph Attention Model for Structural Optimization of High Entropy Carbides Predicting relaxed atomic structures of chemically complex materials remains a major computational challenge, particularly for high-entropy systems where traditional first-principles methods become prohibitively expensive. We introduce the edge-aware graph attention model, a physics-informed graph neural network tailored for predicting relaxed atomic structures of high-entropy systems. the edge-aware graph attention model employs chemically and geometrically informed descriptors that capture both atomic properties and local structural environments. To effectively capture atomic interactions, our model integrates a multi-head self-attention mechanism that adaptively weighs neighbouring atoms using both node and edge features. This edge-aware attention framework learn complex chemical and structural relationships independent of global orientation or position. We trained and evaluated the edge-aware GAT model on a dataset of carbide systems, spanning binary to high-entropy carbide compositions, and demonstrated its accuracy, convergence efficiency, and transferability. The architecture is lightweight, with a very low computational footprint, making it highly suitable for large-scale materials screening. By providing invariance to rigid-body transformations and leveraging domain-informed attention mechanisms, our model delivers a fast, scalable, and cost-effective alternative to DFT, enabling accelerated discovery and screening of entropy-stabilised materials. Graph ; Binary number ; Carbide ; Artificial neural network ; Complex system ; Entropy (arrow of time) ; Convergence (economics) Machine Learning in Materials Science ; High Entropy Alloys Studies ; Advanced Graph Neural Networks
Authors: Mohammad Reza Attar | Ali Davoodi
Source: Corrosion and Materials Degradation (article, 2025-11-13)
Field: Materials Science — Metals and Alloys

A Review on Advanced AFM and SKPFM Data Analytics for Quantitative Nanoscale Corrosion Characterization Corrosion is a complex, surface-initiated process that demands nanoscale, real-time characterization to understand its initiation and propagation. Atomic force microscopy (AFM) and scanning Kelvin probe force microscopy (SKPFM) have emerged as powerful tools in corrosion science, enabling high-resolution imaging and electrochemical mapping under realistic conditions. This review, inspired by pioneering work at KTH by Professors Christofer Leygraf and Jinshan Pan, highlights advanced analytical strategies that extend the capabilities of AFM and SKPFM beyond traditional line-profile analysis. Techniques such as power spectral density (PSD) analysis, multimodal Gaussian histogram fitting, statistical roughness quantification, and deconvolution methods are discussed in the context of case studies on aluminum alloys, stainless steels, magnesium alloys, biomedical implants, and protective coatings. By integrating in situ imaging, electrochemical mapping, and statistical data processing, these approaches provide deeper insights into localized corrosion, micro-galvanic coupling, and surface reactivity. Future directions include coupling AFM-based methods with high-speed imaging, machine learning, and spectro-electrochemical techniques to accelerate the development of corrosion-resistant materials and enable probabilistic diagnostics of corrosion initiation susceptibility. Characterization (materials science) ; Context (archaeology) ; Deconvolution ; Atomic force microscopy ; Kelvin probe force microscope ; Scanning probe microscopy ; Microscopy ; Nanoscopic scale Hydrogen embrittlement and corrosion behaviors in metals ; Force Microscopy Techniques and Applications ; Corrosion Behavior and Inhibition
Authors: Shogo Saito (21569481) | Riona Oka (21569484) | Tomoya Ikuta (21569487) | Yuya Abe (14161228) | Yonghyun Choi (7533677) | Takumi Ohashi (1659586) | Kevin Critchley (1735162) | Jonghoon Choi (1275948) | Stephen D. Evans (1297983) | Masayoshi Tanaka (454481) | Mina Okochi (521982)
Source: Figshare (article, 2025-06-19)
Field: Materials Science — Biomaterials

Integration of PeptideArray Screening and MachineLearning for the Discovery of Peptides Directing Triangular Gold NanoplateFormation Peptide-mediated one-pot synthesis of gold nanoparticles (AuNPs) with diverse morphologies has been explored using tetrachloroauric acid ions at room temperature. Among these, triangular gold nanoplates (AuNPLs) are particularly notable for their unique optical properties and potential applications in photothermal therapy. In this study, we integrated peptide array screening with machine learning to identify peptides and evaluate their AuNP synthesis potential. By analyzing the color characteristics of AuNPs synthesized by each peptide on the array, we trained machine learning models to predict biomineralization efficiency based on peptide sequences and the resulting AuNP optical characteristics. This approach led to the discovery of four peptide sequences, among which the BR2–3 peptide (RWGGGIGGWN) facilitated the formation of uniform triangular AuNPLs with a yield of 33%. While the overall synthesis efficiency remained comparable to previously identified peptides, our findings broaden the scope of peptide sequences that can direct controlled AuNPL formation, offering possibilities for tailored AuNP synthesis. Over 95% of these nanoparticles (triangular and decahedral) fell within the 10–100 nm size range, making them highly suitable for drug delivery systems (DDS) and other biomedical applications. AuNPs synthesized with BR2–3 exhibited higher cellular uptake compared to those synthesized with other peptides, suggesting enhanced internalization potential. This study demonstrates the feasibility of peptide array-based, machine learning-assisted peptide screening for the biomineralization of triangular AuNPLs, emphasizing their applicability in biosensing and biomedicine. Peptide ; Biomineralization ; Colloidal gold ; Nanoparticle ; Photothermal therapy ; Biosensor Diatoms and Algae Research ; Supramolecular Self-Assembly in Materials ; Advanced Nanomaterials in Catalysis
Authors: Yushu Cheng (5783054) | Huichun Zhang (1445707)
Source: Figshare (article, 2025-07-05)
Field: Materials Science — Materials Chemistry

L2D: A VersatileLiterature-to-Data set Pipeline forComplex, User-Specific Data Extraction in Environmental Research Large language models (LLMs) have introduced a new paradigm for automated information extraction, significantly reducing manual effort. While many LLM-based pipelines perform well in fields like materials and medical research, their application in environmental domains remains limited. Document-level LLM frameworks offer new opportunities to address the unique challenges of environmental literature, where relevant information is often scattered and requires selective, multifeature extraction informed by domain context. This study introduces L2D, a structured multistep prompting pipeline that dynamically integrates domain constraints and an LLM-based self-validation process. L2D employs contextual prompting strategies to extract only relevant experimental details (e.g., chemicals undergoing biodegradation, not medium components), while validation mitigates hallucinations and misinterpretations. We benchmarked L2D against two LLM pipelines (ChatExtract and PropertyExtractor) and a deep learning model (ChemREL). While ChemREL achieved only 0.53 F1 on our chemical recognition taskreflecting the limitations of task-specific deep learning models in nuanced contextsL2D achieved 93% accuracy in extracting eight biodegradation-related features from 79 articles, screened from an initial pool of 500 in under 30 min. Without requiring additional model training, L2D demonstrates superior precision, adaptability, and scalability, making it a practical, low-code tool for structured data set generation in environmental research. Pipeline (software) ; Domain (mathematical analysis) ; Pipeline transport ; Set (abstract data type) ; Information extraction ; Data set ; Deep learning ; Data modeling Machine Learning in Materials Science ; History and advancements in chemistry ; Computational Drug Discovery Methods