jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yinan Wang | Yu Fujikata | Lut Wong | Yasuji Muramatsu | Teruyasu Mizoguchi
Source: Ultramicroscopy (article, 2026-02-21)
Field: Materials Science — Materials Chemistry

Systematic correction of core-loss spectra via machine learning: bridging the gap between simulated and experimental spectra Core-loss spectroscopy, including Energy Loss Near Edge Structures (ELNES) and X-ray Absorption Near Edge Structures (XANES), is a vital tool for materials characterization. However, its full potential is often hindered by the limited accuracy of computationally efficient simulations. Specifically, conventional methods like density functional theory with the generalized gradient approximation (DFT-GGA) often fail to reproduce experimental spectra perfectly. The DFT-GGA calculation is known to systematically underestimate energy separations, leading to a compression of spectral features compared to experimental results. To bridge this gap, we developed a machine learning framework utilizing the Random Forest algorithm, based on a dataset of 295 pairs of DFT-GGA and experimental K-edge spectra (C, N, O) from organic molecules. Our model demonstrated high fidelity in correcting the systematic errors of GGA calculations, accurately reproducing experimental spectral line shapes by learning a non-linear transformation. Analysis using artificial spectra reveals that the model has learned to numerically compensate for systematic discrepancies, producing spectral expansions that resemble self-energy corrections. Most significantly, the model exhibits remarkable generalization capabilities. Despite being trained exclusively on organic molecules, it successfully corrects the K-edge spectra of inorganic solid-state materials, for which it was not trained. These findings indicate that our model functions as a computationally efficient "Simulation-to-Experiment" corrector, providing a powerful tool for obtaining high-accuracy theoretical spectra at a fraction of the traditional cost, thereby accelerating high-throughput materials screening and providing reliable theoretical benchmarks to facilitate the interpretation of complex experimental data. Bridging (networking) ; Spectral line ; Spectral shape analysis ; Fidelity ; Generalization ; Density functional theory ; Energy (signal processing) ; Enhanced Data Rates for GSM Evolution Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; CO2 Reduction Techniques and Catalysts
Authors: Xi Chen | Zhouhua Jiang | AO Wang | Shengcheng An
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Physics-Constrained Deep Learning for Predicting Electrical Conductivity of CaF2-Based Multicomponent Melts: A Composition–Microstructure–Property Paradigm Interpretability ; Microstructure ; Conductivity ; Artificial neural network ; Electrical resistivity and conductivity ; Pipeline (software) ; Deep learning ; Arrhenius equation Machine Learning in Materials Science ; Molten salt chemistry and electrochemical processes ; Inorganic Fluorides and Related Compounds
Authors: Yizhe Ding | Jiacheng Shi | Yuqian Lin | Qiya Sun | Yuying Zhao | Xi Wang | Qian Ping | Lin Wang | Yongmei Li
Source: Journal of Hazardous Materials (article, 2026-02-26)
Field: Chemistry — Organic Chemistry

Unraveling the ranitidine removal mechanism in a novel riboflavin-mediated ultraviolet/peracetic acid system: An integrated density functional theory-machine learning framework Radical ; Density functional theory ; Ranitidine ; Conjugated system ; Electron paramagnetic resonance ; Quenching (fluorescence) ; Reactive oxygen species ; Degradation (telecommunications) Free Radicals and Antioxidants ; melanin and skin pigmentation ; Metabolism and Genetic Disorders
Authors: J. Benji Rowlands | Richard J. Lewis | Peter W. A. Howe | Jonathan M. Goodman
Source: ChemRxiv (article, 2026-04-10)
Field: Chemistry — Spectroscopy

CSP5: Large-scale Neural Chemical Shift Prediction from 2.5 Million Experimental NMR Spectra Accurate nuclear magnetic resonance (NMR) chemical shift prediction remains limited by the small size and narrow chemical diversity of atom-assigned training datasets. Here, we address that bottleneck by leveraging the new NMRexp resource with weak supervision, training CASCADE-style graph neural networks with an optimal-assignment loss with datasets of approximately 1.2 million unassigned 13C spectra and 1.4 million unassigned 1H spectra alongside conventional assigned data. This joint training strategy substantially improves performance over assigned-only baselines: on held-out NMRexp data, 13C mean absolute error (MAE) falls from 1.46 to 1.05 ppm and 1H MAE from 0.316 to 0.134 ppm, while 13C performance on a test set from the assigned Exp22K dataset improves from 0.812 to 0.610 ppm. These gains also translate into better structure verification performance, with hard-decoy top-1 retrieval improving from 0.819 to 0.876 for 13C and from 0.589 to 0.801 for 1H. Taken together, these results show that large-scale unassigned literature spectra provide an effective training signal for more accurate and practically useful NMR prediction models. Bottleneck ; Artificial neural network ; Spectral line ; Training set ; Chemical shift ; Test set ; Pattern recognition (psychology) Molecular spectroscopy and chirality ; Computational Drug Discovery Methods ; Advanced NMR Techniques and Applications
Authors: Diana Wahyu Hayati | Jieh-Haur Chen | Mu-Chun Su | Wei-Jen Lin | Ting-Kwei Wang
Source: Journal of Global Governance and Sustainability (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Automated and sustainable coating assessment for H-shaped steel using DNN This study presents the development of a fully automated recognition system leveraging Deep Neural Networks (DNN) to identify and evaluate coating patterns and structural attributes of H-shaped steel components. The system is designed not only to improve the efficiency and accuracy of inspection processes but also to contribute to sustainable construction practices by enabling early defect detection, reducing material waste, and optimizing lifecycle maintenance. The computational pipeline consists of eight key stages: (1) data input, (2) detection area computation, (3) categorization of residual regions, (4) calculation of potential parallel distances, (5) optimal pattern recognition, (6) model-object comparison, (7) output generation for Matplotlib visualization, and (8) 3D plot construction from output coordinates. This pipeline allows for precise, non-destructive evaluation and facilitates real-time integration into automated fabrication or quality control workflows. The system was trained and validated using a robust dataset comprising 115 standard-type and 99 special-type H-shaped steel samples, representing a broad spectrum of commercially used profiles. The proposed model achieved 100% accuracy in identifying critical geometric features such as width, base plate thickness, and wing plate thickness. The overall recognition accuracy exceeded 99.12%, with an average real-world application accuracy of 99.73%, indicating excellent performance and reliability for industrial deployment. By automating the recognition process and enhancing the reliability of coating and structural assessments, this research supports the development of smart, resource-efficient infrastructure, aligning with key goals in sustainable manufacturing and resilient civil engineering. Reliability (semiconductor) ; Pipeline (software) ; Artificial neural network ; Process (computing) ; Key (lock) ; Pipeline transport ; Automation ; Residual Machine Learning in Materials Science ; Infrastructure Maintenance and Monitoring ; Thermography and Photoacoustic Techniques
Authors: Zhuohao Yu | Kai Cheng | Danijela Mišić | Marina Sokóvić | Daniel Figeys | Hongxi Xu | Yang Ye | Jia Liu
Source: TrAC Trends in Analytical Chemistry (article, 2026-04-01)
Field: Chemistry — Spectroscopy

Evidence-Driven m/z Alignment in Mass Spectrometry Imaging: Physics Models, Observed-Data Recalibration, and Deep Learning Calibration ; Representation (politics) ; Deep learning ; Scale (ratio) ; SIGNAL (programming language) ; Mass spectrometry ; Noise (video) ; Quality (philosophy) Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Sora Nakanishi | Shinya Matsutomo | Masahide Shioyama | Narichika Nakamura | Yoshifumi Okamoto
Source: IEEJ Transactions on Fundamentals and Materials (article, 2026-01-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Study on Method of Magnetization Estimation of Permanent Magnet using DNN utilizing Heisenberg Model for Generation of Training Data Permanent magnet (PM), which properties are maintained semi-permanently, is one of the essential magnetic materials for the electromagnetic devices. The performance of those devices substantially depends on the magnetization distribution of the permanent magnets. Therefore, it is essential to clarify the magnetization distribution of PMs in advance. To estimate a physical magnetization distribution in PM, a deep neural network (DNN) based magnetization estimation method using the measurement data of magnetic flux density has been proposed. However, when the estimation of 3-D magnetization distribution is carried out using DNN, the estimation accuracy is not high because an effective method for generating training data has not been proposed. In this paper, an effective procedure for the generation of training data using Heisenberg model, which can realize many distributions of PM magnetization, is proposed. As a result, it is shown that the DNN with the proposed data generation method is able to be effective for the accurate and fast estimation of 3D PM magnetization distribution. Magnetization ; Magnet ; Distribution (mathematics) ; Artificial neural network ; Magnetic flux ; Remanence ; Flux (metallurgy) Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Magnetic Properties of Alloys
Authors: Chris John | Edoardo Cignoni | Lorenzo Cupellini | Benedetta Mennucci
Source: Journal of Chemical Information and Modeling (article, 2026-01-31)
Field: Materials Science — Materials Chemistry

Machine-Learning Framework for Excitation Energies of Chromophores in Polarizable Environments Excited states of embedded chromophores are highly influenced by their interaction with the environment. Herein, we present a machine-learning (ML) framework capable of predicting the different environmental contributions to excitation energies of chromophores in a polarizable embedding. Our ML models are built in a hierarchical structure to capture both the effect of ground-state polarization and the response of the polarizable environment to the electronic transition. With the use of the right descriptors, the models trained on the quantum mechanics/molecular mechanics (QM/MM) calculations in a nonpolarizable environment are able to successfully predict the effects of a polarizable environment on excitation energies. The ML models are applied to three chromophores present in light-harvesting complexes (chlorophyll a, chlorophyll b, and lutein) and are used to reproduce the excitonic structure of a multichromophoric system unseen in the training set to a level of accuracy offered by a polarizable QM/MM calculation, while taking a fraction of its time. Chromophore ; Polarizability ; Excitation ; Excited state ; Polarization (electrochemistry) ; Polarizable continuum model ; Quantum Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Molecular spectroscopy and chirality
Authors: Jie Chi | Yingxi Qin | Lanxiu Ni | Xize Chen | Xingxin Pan | Yuwen Liu | Shuna Wang | Yan-Yan Song | Lei Feng
Source: Chemical Engineering Journal (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

Engineered single nanozyme-based time-encoded virtual sensor array for high-throughput detection of antioxidants Analyte ; Fluorescence ; Sensor array ; SIGNAL (programming language) ; Soft sensor ; Principal component analysis ; Sensitivity (control systems) ; Linear discriminant analysis Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Zhiwei Yin | Victor W. Rosso | F. E. ROBERTS | A.H. Furman | Jason M. Stevens | Taylor A. Watts | Cameron Cook | Anisha Patel | Jun Qiu
Source: Organic Process Research & Development (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

EZClust: A Robust Machine Learning-Based Powder X-Ray Diffraction and Raman Cluster Analysis Model for Efficient High-Throughput Crystallization Polymorph Screening High-throughput crystallization (HTC) polymorph screening is pivotal for exploring the crystal polymorph landscape, but the sheer volume and complexity of powder X-ray diffraction (PXRD) and Raman spectroscopy data present significant data-processing challenges. Traditional approaches, which rely on human interpretation aided by software, are often constrained by limited clustering accuracy. To address these limitations, we developed EZClust, a lightweight machine-learning model designed for rapid PXRD and Raman batch data analysis. A key algorithm in the model is shape-based distance (SBD), which provides robust performance for processing data with distortion and minimal parameter tuning. In this work, we compare EZClust’s performance to existing mainstream commercial software (Jade Pro) and the open-source AutoFIDEL implementation, demonstrating its robustness through cluster analysis of HTC datasets for the model compounds ROY and carbamazepine. Herein, we disclose the core algorithms of EZClust, robust preprocessing coupled with an SBD metric, to streamline cluster analysis for PXRD and Raman datasets in HTC workflows. Raman spectroscopy ; Powder diffraction ; Crystallization ; Robustness (evolution) ; Cluster analysis ; Diffraction ; Cluster (spacecraft) ; Preprocessor X-ray Diffraction in Crystallography ; Enzyme Structure and Function ; Machine Learning in Materials Science
Authors: Krishna Sai Aparna Munjuluri | Monem Moktadir | Hassan Soltani | Srimanta Barui | Sundar V. Atre | Devendra Bajaj | Kunal H. Kate
Source: Powder Technology (article, 2026-03-04)
Field: Materials Science — Ceramics and Composites

Machine learning-assisted determination of critical solids loading in PBT-alumina composites with spherical and non-spherical powder morphologies Torque ; Composite number ; Mixing (physics) ; Void (composites) ; Particle (ecology) ; Polymer ; Flow (mathematics) ; Porosity Advanced ceramic materials synthesis ; Composite Material Mechanics ; Aluminum Alloys Composites Properties
Authors: Chenglin Xing | Jiawen Zhong | Minghui An | Chengyuan Pan | Xuezheng Yue
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Overcoming Spatial Myopia in High-Throughput Metallography via Physics-Informed Frequency-Spatial Synergistic Perception Metallography ; Robustness (evolution) ; Perception ; Decoding methods ; Noise (video) ; Bottleneck ; Deep learning Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Digital Transformation in Industry
Authors: Fatih Uzun
Source: Particuology (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

Hybrid surrogate–physics framework for rapid initialisation of granular packings in discrete element simulations The initialisation of mechanically stable granular packings is a computationally expensive prerequisite for many discrete element method (DEM) simulations, as conventional dynamic settling requires long transient phases dominated by repeated contact resolution and energy dissipation. This work presents a hybrid surrogate–physics framework for rapidly approximating static equilibrium packings while retaining physical admissibility. A particle-wise neural network surrogate is trained to predict approximate equilibrium particle coordinates directly from initial conditions and material properties, using reference solutions generated by an energy-based DEM formulation. Because particle-wise predictions do not explicitly enforce local contact topology, the surrogate output is post-processed through a mandatory overlap-correction stage followed by a short physics-based relaxation to recover mechanically stable configurations. A physics-aware composite loss function incorporating boundary and overlap penalties improves the geometric plausibility of the surrogate predictions. The framework is evaluated on small-scale representative granular packing scenarios as a methodological proof-of-concept for computationally expensive applications such as die filling. It reproduces physically consistent final states while offering a substantial reduction in the computational effort required to reach equilibrium compared to full dynamic settling. Validation using packing structure and coordination number statistics confirms that the hybrid approach recovers both global and local characteristics of mechanically stable granular assemblies. • Hybrid surrogate–physics framework accelerates DEM packing generation. • Neural network predicts particle equilibrium, bypassing dynamic settling. • Physics-based overlap correction ensures rigorous mechanical stability. • The proposed pipeline significantly reduces required computational iterations. • This hybrid method successfully reproduces coordination number statistics. Discrete element method ; Contact dynamics ; Granular material ; Reduction (mathematics) ; Boundary (topology) ; Dynamic relaxation ; Pipeline (software) ; Relaxation (psychology) ; Function (biology) ; Work (physics) Material Dynamics and Properties ; Granular flow and fluidized beds ; Composite Material Mechanics
Authors: TONY NYO | Liling Triyasmono | Uripto Trisno Santoso
Source: Research Square (preprint, 2026-03-09)
Field: Chemistry — Organic Chemistry

In silico QSAR and design of chalcone derivatives for HT-29 colorectal cancer: MLR and ANN approaches Quantitative structure–activity relationship ; In silico ; Overfitting ; Chalcone ; DrugBank ; Interpretability ; Colorectal cancer ; Cardiotoxicity Synthesis and biological activity ; Computational Drug Discovery Methods ; Curcumin's Biomedical Applications
Authors: Gesa-R. Siemann | D. Curcio | A. Mortensen | Charlotte E. Sanders | Yu Zhang | J. S. Rigden | Paulina Majchrzak | Deepnarayan Biswas | Emma Springate | Ratnadwip Singha | Leslie M. Schoop | Philip Hofmann
Source: Physical review. B./Physical review. B (article, 2026-01-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Clustering-enhanced time- and angle-resolved photoemission study of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>LaTe</mml:mi> <mml:mn>3</mml:mn> </mml:msub> </mml:math> : Absence of a photoinduced secondary charge density wave in the electronic structure Optical control offers a compelling route for tailoring material properties on an ultrafast time scale. Ordered states such as charge density waves (CDWs) can be transiently melted by an ultrafast light excitation. This is also the case for the rare-earth tritelluride LaTe$_3$, a prototypical CDW compound. For this material it has recently been reported that the suppression of the primary CDW allows the transient formation of a second CDW, whose wave vector is orthogonal to the primary one. This creates the intriguing scenario where light enables switching between two distinct ordered phases of the material. While the second CDW has so far been observed by structural techniques, it remains an open question how the interplay of the two CDW phases is reflected in the material's electronic structure. We investigate this via time- and angle-resolved photoemission measurements of LaTe$_3$. The complex Fermi contour is probed using a FeSuMa analyzer, which records the photoemission intensity of the entire Fermi contour at once. The dynamics revealed by the FeSuMa analyzer are complemented by measurements using a conventional hemispherical electron analyzer. We combine conventional data analysis with $k$-means clustering, an unsupervised machine learning technique, demonstrating its strong potential for disentangling large datasets. While we do not find any features that cannot be explained by the melting and re-establishment of the primary CDW, distinct dynamics and coherent oscillations are observed in the different branches of the Fermi contour. Charge density wave ; Ultrashort pulse ; Electronic structure ; Fermi Gamma-ray Space Telescope ; Fermi level ; Photoemission spectroscopy ; Electron ; Charge (physics) Organic and Molecular Conductors Research ; 2D Materials and Applications ; Inorganic Chemistry and Materials
Authors: Tianjian Xiao | Zhi Zeng | Haining Ji | Bin Liu | Peng Long | Shan Jin
Source: Applied Thermal Engineering (article, 2026-01-19)
Field: Materials Science — Polymers and Plastics

VO2-based dynamic infrared modulation: properties and applications Vanadium dioxide ; Infrared ; Modulation (music) ; Spacecraft ; Smart material ; Key (lock) ; Thermal ; Adaptability Transition Metal Oxide Nanomaterials ; Gas Sensing Nanomaterials and Sensors ; Pigment Synthesis and Properties
Authors: Gurukarthik Babu Balachandran | Muthu Eshwaran Ramachandran | Hariharasudhan Thangaraj | Manikandan Pandian
Source: Environmental Progress & Sustainable Energy (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

Reducing environmental footprint in power systems through high‐performance epoxy–nanocarbon insulation Abstract Transformers are among the most crucial components of electric distribution and transmission systems, and the grid's stability and dependability are directly impacted by their operation. Polymeric insulation used in transformers and electrical allied areas has recently created an enormous revolution. An epoxy polymer matrix with carbon‐based nano reinforcement is subjected to analyze the possibilities of incorporation as an insulant in dry type transformer. The fabrication process was optimized by varying hardener content and stirring speed using a central composite design integrated with response surface methodology. The developed epoxy/carbon nanocomposites were evaluated for dielectric strength, thermal conductivity, tensile strength, Izod impact resistance, and morphological characteristics to determine their suitability as solid insulation for dry‐type transformer applications. Some algorithm combinations [random forest, neural network, gradient boosting, XGboost gain, and response surface methodology] are examined to determine the most optimal. This study revealed that 6.5% of the sample performed well for all the properties of the study. The optimized sample had a dielectric strength of 140 V/mm, a thermal conductivity of 0.45 W/m K, and an Izod impact of 0.26 (J/mm). RSM was ultimately determined to be a more accurate prediction model than the other algorithms based on the obtained model results. The results indicate that, with an accuracy of 94% for dielectric strength, 98% for tensile strength, 98.4% for Izod impact, and 96% for thermal conductivity, the RSM performs best in forecasting the revolutionizing of the transformer's insulation. Ultimate tensile strength ; Izod impact strength test ; Response surface methodology ; Thermal stability ; Thermal conductivity ; Dielectric ; Transformer ; Composite number High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Thermal properties of materials