jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qiuji Wang | Dequan Wu | Kun Zhou | Haiyan Gong | Suyu Jin | Jinke Wang | Zhibin Chen | Lingwei Ma | Dawei Zhang
Source: npj Materials Degradation (article, 2026-01-10)
Field: Materials Science — Materials Chemistry

Quantification and prediction of carbon steel surface corrosion based on machine vision Corrosion detection plays a critical role in assessing material degradation and predicting performance changes to ensure equipment safety. Traditional manual inspection methods are highly subjective, while instrumental analysis tends to be destructive and costly. To address these limitations, this study proposes a machine vision-based method for detecting and predicting the composition of corrosion products on carbon steel subjected to corrosion after different durations. The corrosion products were analyzed using Raman spectroscopy. A generative adversarial network (GAN) was employed to expand the corrosion image dataset, and a convolutional neural network (CNN) was used for automatic corrosion classification. The K-means algorithm was then applied to segment different corrosion product regions. The total proportion of each corrosion product on the carbon steel surface was calculated by correlating the rusted area with the product distribution, and the corrosion evolution was analyzed using interpolation. Experimental results confirm that this method accurately quantifies corrosion product proportions, offering a novel strategy for intelligent corrosion detection. Corrosion ; Carbon steel ; Convolutional neural network ; Artificial neural network ; Product (mathematics) ; Generative adversarial network Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Structural Integrity and Reliability Analysis
Authors: Zodinpuia Ralte | Ramesh Kumar | Mukhtiyar Singh
Source: Modelling and Simulation in Materials Science and Engineering (article, 2026-02-27)
Field: Materials Science — Materials Chemistry

Elemental frequency-based supervised classification approach for the search of novel topological materials Abstract The machine learning (ML) based approaches efficiently solve the goal of searching the best materials candidate for the targeted properties. The search for topological materials (TMs) using traditional first-principles and symmetry-based methods often require lots of computing power or is limited by the crystalline symmetries. In this study, we present frequency-based statistical descriptors for ML-driven TM’s classification that is independent of crystallographic symmetry of wave functions. This approach predicts the topological nature of a material based on its chemical formula. With a balanced dataset of 3880 materials, we have achieved classification accuracies of 82% with the support vector machine model and 83% with the random forest model, where both models have trained on common frequency based features. We have verified the performances of the models using 5-fold cross-validation approach. Moreover, we have validated the models on a dataset of unseen binary compounds and have efficiently identified 22 common materials using both the models. Next, we used the first-principles approach to provide preliminary evidence for the topological candidates via band inversions at time-reversal invariant momenta points. Therefore, we have demonstrated that the implications of frequency-based descriptors is a practical and less complex way to find novel TMs with certain physical post-processing filters. This approach lays the groundwork for scalable, data-driven topological property screening of complex materials. Binary number ; Topology (electrical circuits) ; Topological data analysis ; Random forest ; Support vector machine ; Property (philosophy) ; Symmetry (geometry) Machine Learning in Materials Science ; Topological Materials and Phenomena ; Topological and Geometric Data Analysis
Authors: Haoye Qin | Wenjing Lv | Zhe Zhang | Zijin Yang | Jue Li | Mengyao Li | Bo Li | Ji Zhou | Romain Fleury | Patrice Genevet | Qinghua Song | Cheng-Wei Qiu
Source: Nature Reviews Materials (article, 2026-02-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Photonic exceptional points in engineered materials and their emerging applications Photonics ; Wavefront ; Topology (electrical circuits) ; Scattering ; Polarization (electrochemistry) ; Cloaking ; Dielectric ; Photonic metamaterial Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Plasmonic and Surface Plasmon Research
Authors: Mahdi Kchaou | Francesco Contino | Diederik Coppitters
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

&lt;p&gt;Exploring Design Flexibility in Near-Optimal E-Molecules Import Pathways Using Modelling to Generate Alternatives and Interpretable Machine Learning&lt;/p&gt; Flexibility (engineering) ; Key (lock) ; Set (abstract data type) ; Design methods ; Constraint (computer-aided design) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Seung-Ho Choi | Yeon-Joo Kim | Jaetae Seo | Dongjin Lee | Seon-Jin Choi
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Spectroscopy

Metal-Dependent Sensitivity Modulation in Chemiresistive Anion Sensing with meso-Squaramide-Substituted Metalloporphyrin Selectors Squaramide ; Analyte ; Deprotonation ; Porphyrin ; Ion ; Biosensor ; Chemical sensor Molecular Sensors and Ion Detection ; Analytical Chemistry and Sensors ; Supramolecular Chemistry and Complexes
Authors: Sakshi Goel | Arti Kashyap
Source: Journal of Magnetism and Magnetic Materials (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Machine learning assisted high-throughput study of M4X3Tx MXenes MXenes ; Antiferromagnetism ; Ferromagnetism ; Density functional theory ; Lattice (music) ; Ising model ; Ground state MXene and MAX Phase Materials ; Heusler alloys: electronic and magnetic properties ; 2D Materials and Applications
Authors: M.I.A. Barbosa | A. Margarida Teixeira | M. José Sousa | Pedro Ribeiro | Clara Sousa | Pedro Miguel Rodrigues
Source: Processes (article, 2026-03-18)
Field: Chemistry — Analytical Chemistry

FTIR-Based Machine Learning Identification of Virgin and Recycled Polyester for Textile Recycling in Industry 4.0 Advances in Industry 4.0 manufacturing have accelerated the adoption of machine learning (ML) for automated classification. Polyester (PES), a widely used synthetic fiber, competes with natural fibers like cotton and other synthetics, highlighting the need for continuous research and improvement. In the textile sector, distinguishing recycled polyester (rPES) from virgin polyester (vPES) remains challenging due to overlapping chemical signatures and material variability. A combination of Fourier transform infrared (FTIR) spectroscopy and ML has not been explored for this purpose. In this study, we evaluated ML models to discriminate three PES fiber types (45 vPES, 65 rPES, and 55 mixed PES) using 165 FTIR spectra across four spectral regions, R1, R2, R3, and R4, as well as their combined representation. Six ML approaches were tested on data reduced with fast independent component analysis (FastICA) (1–30 components) using an 80/20 train–test dataset split. The Decision Tree classifier achieved the highest Accuracy in four of the five spectral evaluations, with classification accuracies ranging from 66.67% to 77.78% for region R4, which also had a balanced classification profile with an area-under-the-curve (AUC) value of 0.81. Notably, despite the moderate overall Accuracy, the model achieved 100% discrimination of rPES when distinguishing it from both mixed and vPES. Mixed fibers remained the most difficult to classify, highlighting the need for improved feature representation. Polyester ; Textile ; Classifier (UML) ; Decision tree ; Fourier transform ; Pattern recognition (psychology) ; Feature (linguistics) ; Fourier transform infrared spectroscopy ; Textile industry Spectroscopy and Chemometric Analyses ; Textile materials and evaluations ; Microplastics and Plastic Pollution
Authors: X. Niu | Z. H. Zhang | Xiaoyu Wu | Yan Liu | Y Cui | Jianwen Jiang
Source: Chemical Science (article, 2026-01-01)
Field: Chemistry — Inorganic Chemistry

Machine learning guided discovery of water stable metal–organic frameworks for photocatalytic hydrogen production With remarkably tunable porosity and modular chemistry, metal-organic frameworks (MOFs) present a versatile platform for photocatalytic hydrogen (H<sub>2</sub>) production. However, identifying high-performing and water stable MOFs from the vast design space is challenging. In this study, we develop a hierarchical screening strategy to accelerate the discovery of photocatalytically active MOFs with robust water stability. First, machine learning (ML) classifiers are trained on experimental H<sub>2</sub> production data to predict photocatalytic performance, achieving high accuracy and excellent transferability. Then, starting from 11660 structures in the CoRE-MOF database, 1731 are shortlisted to be photocatalytically active. Detailed structure-performance analyses reveal that linker flexibility and aliphatic character positively correlate with H<sub>2</sub> evolution activity, while excessive aromaticity and rigidity are detrimental. Finally, a water stability classifier is applied to further identify 419 MOFs to be simultaneously photocatalytically promising and water stable. The ML-guided strategy provides a quantitative and interpretable path toward the discovery of new MOFs as photocatalysts, and it would facilitate future experimental exploration for efficient photocatalytic H<sub>2</sub> production. Photocatalysis ; Modular design ; Hydrogen production ; Flexibility (engineering) ; Linker ; Classifier (UML) Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques ; Covalent Organic Framework Applications
Authors: Mohd Azfar Shaida | Laiba Saleem | Syed Ali Waqas Ahmad | Saad Shamim Ansari | Syed Muhammad Ibrahim
Source: Environmental Science and Pollution Research (article, 2026-04-06)
Field: Chemistry — Inorganic Chemistry

Data-driven explainable artificial intelligence models for predicting CO2 uptake in metal–organic frameworks Interpretability ; Artificial neural network ; AdaBoost ; Random forest ; Test set ; Generalization ; Gradient boosting ; Boosting (machine learning) ; Set (abstract data type) Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Covalent Organic Framework Applications
Authors: Zhiwei Hou | Yihan Song | Aochi Liu | Li Sun | Chenyang Gao | Xinda Cai | Yujiao Xie | Tianxiang Chen | Aiguo Wu | Jiaqi Lin
Source: Thermochimica Acta (article, 2026-01-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Surface-enhanced Raman spectroscopy for Alzheimer’s Disease biomarkers detection: Advances, challenges, and future perspectives Biomarker ; Substrate (aquarium) ; Surface-enhanced Raman spectroscopy ; Raman spectroscopy ; Biomarker discovery ; Diagnostic biomarker ; SIGNAL (programming language) Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Nanomaterials in Catalysis
Authors: Jing Ran | Qi Zhang | Hui Xu | Xueyuan Bai | Wei Zhang
Source: Journal of Food Composition and Analysis (article, 2026-03-14)
Field: Chemistry — Analytical Chemistry

Nondestructive detection of ganoderic acids based on short-wave infrared hyperspectral and machine learning Ganoderma lucidum is widely used in the medicine and healthcare industries as a valuable medicinal herb, which is usually artificially grown. Its quality is influenced by various factors, and traditional evaluation methods for this herb are complex. Herein, a combination of short-wave infrared hyperspectral imaging and machine learning methods was explored to develop a prediction model for different ganoderic acids contained in Ganoderma lucidum. First, we established Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM) and Decision Tree (DT) models using the original spectral data as baseline models; ELM showed the best prediction. Next, we preprocessed spectral data using different preprocessing methods and selected characteristic wavelengths via Competitive Adaptive Reweighted Sampling (CARS), Successive Projections Algorithm (SPA) and Uninformative Variables Elimination (UVE). The optimal results demonstrated that the MSC–CARS–ELM model achieved the best performance for predicting ganoderic acids A, B, and C2, with prediction correlation coefficients (R²P) of 0.91, 0.88, and 0.91, respectively. This finding confirms that preprocessing combined with feature wavelength selection significantly enhances prediction accuracy. Further analysis revealed that while the BPNN and ELM models generally benefited from these techniques, the DT model showed limited improvement. Hence, short-wave infrared hyperspectral imaging can be used for the accurate, fast, and non-destructive detection of ganoderic acids in Ganoderma lucidum. However, this study still exhibits several limitations. Consequently, future research should expand the sample size, incorporate external validation sets, and explore additional model parameter configurations. Keywards： ganoderic acids, machine learning, hyperspectral imaging, wavelength selection, preprocessing methods • Integrating CARS wavelength selection with ELM creates a highly accurate and efficient model for quantifying ganoderic acids. • Advanced preprocessing and feature selection dramatically streamline the spectral data, focusing on the most relevant chemical information. • The research validates a practical, non-destructive approach that aligns with the needs of industrial quality control for traditional medicines. • The developed methodology establishes a transferable blueprint for ensuring the quality and authenticity of a wide range of medicinal plants. Hyperspectral imaging ; Pattern recognition (psychology) ; Infrared ; Training set ; Near-infrared spectroscopy Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Molecular spectroscopy and chirality
Authors: Yaping Wen | Yipu Zhang | Haibo Ma
Source: ChemRxiv (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

Navigating high-dimensional fabrication-parameter space in organic photovoltaics device optimization by a multi-tier machine learning framework Organic photovoltaics (OPV) have achieved significant advances over recent decades, driven by synergistic innovations in molecular design and device engineering. However, precise morphological control within bulk-heterojunction active layers remains a crucial barrier to commercial viability, primarily due to the high-dimensional parameter spaces and complex interdependencies among processing variables. To overcome this challenge, we established a standardized materials-processing-performance database integrating donor/acceptor pairs, nine key active layer processing parameters, and device efficiencies. This database, curated from over a decade of experimental results, resolves critical data heterogeneity issues and provides the field's most comprehensive optimization resource. Leveraging this resource, we developed a novel three-tiered machine learning framework employing gradient boosting regression trees to progressively decode active layer processing complexities. Our strategy initiates with single-parameter models for targeted optimization, advances through stage-combined models revealing intra-process synergies (e.g., solvent-additive interplay), and culminates in a global optimization tier. Remarkably, this final tier achieves unprecedented performance, demonstrating >0.9 overall Pearson correlations, and >80% success rates in identifying optimal nine-dimensional configurations. Experimental validation on 78 novel systems confirms exceptional generalization, yielding >75% accuracy in predicting either optimal or secondary parameters across eight active layer processing conditions. This work establishes a robust framework for navigating processing complexity in high-dimensional spaces, enabling accelerated optimization of OPV photoactive layers and providing a transferable data-driven paradigm for rational process design in emerging photovoltaic technologies. Boosting (machine learning) ; Organic solar cell ; Photovoltaics ; Gradient boosting ; Photovoltaic system ; Process (computing) ; Layer (electronics) ; Key (lock) Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Block Copolymer Self-Assembly
Authors: Enriko Kroon | Ricardo Moreira Borges | Rômulo Pereira de Jesus | Stefan Kuhn
Source: Research Ideas and Outcomes (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Interpreting 2D-NMR spectra using Grad-CAM It has been shown that it is possible to train a (simple) neural network to classify nuclear magnetic resonance spectra by a substructures either being part of the chemical structure measured or not. We now explore the interpretability of such models using techniques from explainable AI, specifically Grad-CAM. We show that those techniques do not give ideal results in the context of NMR, which would be able to identify individual peaks. On the other hand, they enable a better interpretation of the results than those metrics just based on "right or wrong". We can also confirm the result from our previous work, that the trained network performs well for pure compounds, but its generalisability to mixtures is questionable, a limitation that could only be assumed in the original study. Interpretability ; Interpretation (philosophy) ; Context (archaeology) ; Ideal (ethics) ; Artificial neural network Machine Learning in Materials Science ; Molecular spectroscopy and chirality ; Advanced NMR Techniques and Applications
Authors: Zhan Li | Hao Ren
Source:  (article, 2026-01-30)
Field: Materials Science — Materials Chemistry

A Variational Autoencoder Framework for Generating Molecular Structures from Raman Spectra of Vibrational Probes Vibrational probe spectroscopy is an effective technique for investigating complex condensed-phase systems. Changes in the vibrational spectra of probe molecules can be used to sensitively monitor variations in key properties of complex systems. However, inversely resolving the local environment of probe molecules from vibrational spectra remains a challenging task. Generative models are capable of learning latent patterns in data and generating new structures, showing great potential for inverse structure determination from spectroscopic data. In this work, ethyl acetate is employed as a vibrational probe. A variational autoencoder (VAE) is used to learn the latent representation of the probe molecule and its local chemical environment, enabling encoding and decoding between molecular structures and latent space. In addition, an artificial neural network is trained to learn the relationship between vibrational spectra and latent variables, forming a spectrum-to-latent-to-structure spectrum–structure generative model (SSG). The model achieves root mean square errors (RMSEs) of 0.058 Å and 0.311 Å for structure prediction on the training and test sets, respectively, which are smaller than the hydrogen atomic radius (0.529 Å), demonstrating the strong generative capability of the model. Furthermore, discriminators with different strengths are investigated to fine-tune the generated structures. The results show that an appropriately designed discriminator enables fast and accurate generation of correct molecular structures Autoencoder ; Discriminator ; Spectral line ; Representation (politics) ; Raman spectroscopy ; Generative model ; Artificial neural network ; Molecular vibration ; Pattern recognition (psychology) Machine Learning in Materials Science ; Molecular spectroscopy and chirality ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Xinyi Chen | Jinglan Zhang | Xu Ye | Mingyu Cheng | Yun Jiang | H. R. Zhang | Zengyao Wang | Bin Ai
Source: Sensors and Actuators B Chemical (article, 2026-03-15)
Field: Chemistry — Organic Chemistry

Three-dimensional asymmetric chiral coupling nanocones for dimensionality-augmented machine learning in robust quantitative enantiodiscrimination Enantiomer ; Analyte ; Chirality (physics) ; Robustness (evolution) ; Circular dichroism ; Coupling (piping) ; Stereocenter ; Chiral stationary phase Synthesis and Properties of Aromatic Compounds ; Advanced Memory and Neural Computing ; Supramolecular Self-Assembly in Materials
Authors: Antonio Sanna | Tiago F. T. Cerqueira | Ekin Dogus Cubuk | Ion Errea | Yue-Wen Fang
Source: Communications Physics (article, 2026-02-27)
Field: Materials Science — Materials Chemistry

Search for thermodynamically stable ambient-pressure superconducting hydrides in the GNoME database Hydrides are considered to be one of the most promising families of compounds for achieving high temperature superconductivity. However, there are very few experimental reports of ambient-pressure hydride superconductivity, and the superconducting critical temperatures (<i>T</i> <sub>c</sub>) are typically less than 10 K. At the same time several hydrides have been predicted to exhibit superconductivity around 100 K at ambient pressure but in thermodynamically unfavorable phases. In this work we aim at assessing the superconducting properties of thermodynamically stable hydride superconductors at room pressure by investigating the GNoME material database, which has been recently released and includes thousands of hydrides thermodynamically stable at 0K. To scan this large material space we have adopted a multi stage approach which combines machine learning for a fast initial evaluation and cutting edge ab initio methods to obtain a reliable estimation of <i>T</i> <sub>c</sub>. Ultimately we have identified 25 cubic hydrides with <i>T</i> <sub>c</sub> above 4.2 K and reach a maximum <i>T</i> <sub>c</sub> of 17 K. While these critical temperatures are modest in comparison to some recent predictions, the systems where they are found, being stable, are likely to be experimentally accessible and of potential technological relevance. Superconductivity ; Hydride ; Ab initio ; Work (physics) ; Ab initio quantum chemistry methods ; Room-temperature superconductor ; High-temperature superconductivity Hydrogen Storage and Materials ; Inorganic Chemistry and Materials ; Machine Learning in Materials Science