jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: R. S. P. Singh
Source:  (article, 2025-11-27)
Field: Materials Science — Materials Chemistry

Generative AI for 3D Molecular Structure Prediction Using Diffusion Models It is essential to be able to create unbiased biological sub-comparative routes to study, to gain an empirical understanding of them. This study explains the exploration of a framework that uses generative artificial intelligence and diffusion-based learning. When drawing graph-based representations having geometric limitations the structures and atom to atom interactions must be valid. Enhancement in molecular stability accuracy achieved on the experimental assessment relative to customary generative approaches. These discoveries work against drugs design and discoveries using AI-driven methods are advantageous. Diffusion ; Pattern recognition (psychology) ; Generative grammar ; Generative model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Model Reduction and Neural Networks
Authors: Owais Ahmad | Vishal Panwar | Kaushik Das | R. Mukherjee | Somnath Bhowmick
Source: Physica Scripta (article, 2025-06-25)
Field: Materials Science — Materials Chemistry

Scope of generative artificial intelligence in microstructural studies: a case study Abstract The generative adversarial network (GAN) is one of the most widely used deep generative models for synthesizing high-quality images with the same statistics as the training set. Finite element method (FEM) based property prediction often relies on synthetically generated microstructures. The phase-field model is a computational method of generating realistic microstructures considering the underlying thermodynamics and kinetics of the material. Due to the expensive nature of the simulations, it is not always feasible to use phase-field for synthetic microstructure generation. In this work, we train a GAN with microstructures generated from the phase-field simulations. Mechanical properties calculated using the finite element method on synthetic and actual phase field microstructures show excellent agreement. Since the GAN model generates thousands of images within seconds, it has the potential to improve the quality of synthetic microstructures needed for FEM calculations or any other applications requiring a large number of realistic synthetic images at minimal computational cost. Scope (computer science) ; Generative grammar ; Artificial intelligence ; Computer science ; Materials science Machine Learning in Materials Science
Authors: Boris N. Slautin | Utkarsh Pratiush | Doru C. Lupascu | Maxim Ziatdinov | Sergei V. Kalinin
Source: ArXiv.org (preprint, 2025-03-04)
Field: Materials Science — Materials Chemistry

Integrating Predictive and Generative Capabilities by Latent Space Design via the DKL-VAE Model We introduce a Deep Kernel Learning Variational Autoencoder (VAE-DKL) framework that integrates the generative power of a Variational Autoencoder (VAE) with the predictive nature of Deep Kernel Learning (DKL). The VAE learns a latent representation of high-dimensional data, enabling the generation of novel structures, while DKL refines this latent space by structuring it in alignment with target properties through Gaussian Process (GP) regression. This approach preserves the generative capabilities of the VAE while enhancing its latent space for GP-based property prediction. We evaluate the framework on two datasets: a structured card dataset with predefined variational factors and the QM9 molecular dataset, where enthalpy serves as the target function for optimization. The model demonstrates high-precision property prediction and enables the generation of novel out-of-training subset structures with desired characteristics. The VAE-DKL framework offers a promising approach for high-throughput material discovery and molecular design, balancing structured latent space organization with generative flexibility. Autoencoder ; Kernel (algebra) ; Generative grammar ; Generative model ; Representation (politics) ; Space (punctuation) ; Property (philosophy) ; Gaussian process Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Yanmin Zhu | Loza F. Tadesse
Source: Matter (article, 2025-10-15)
Field: Materials Science — Materials Chemistry

SpectroGen: A physically informed generative artificial intelligence for accelerated cross-modality spectroscopic materials characterization Autoencoder ; Characterization (materials science) ; Key (lock) ; Relation (database) ; Generative grammar ; Generative model ; Component (thermodynamics) ; Pattern recognition (psychology) Machine Learning in Materials Science ; Nuclear Physics and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Archi Katiyar | Vishal B. Upare | A Ajayraj | J. Amala | Anjana P. Anantharaman
Source: Catalysis Letters (article, 2025-12-02)
Field: Chemistry — Inorganic Chemistry

Sustainable Synthesis of Metal (Ce, Fe, and Cu)–Organic Framework as Multi-functional Catalysts for Water and Air Pollution Control Catalysis ; Carbon monoxide ; Adsorption ; Metal-organic framework ; Nanomaterials ; Heterogeneous catalysis ; Mesoporous material ; Trimesic acid ; Scanning electron microscope ; Redox Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science ; Covalent Organic Framework Applications
Authors: J. Y. Li | Yin Wang | Ji Qi | Hu Liu | Xuhong Liu | Mei‐Yan Wang | Jing Lv | Shouying Huang | Xinbin Ma
Source: Industrial & Engineering Chemistry Research (article, 2025-06-09)
Field: Chemistry — Inorganic Chemistry

Effect of Ordered Mesoporous Alumina on Mass Transfer to Enhance Dimethyl Ether Carbonylation of Shaped Mordenite Catalysts Driven by the increasing global energy demand, developing efficient catalytic processes for sustainable ethanol production from dimethyl ether (DME) carbonylation has gained significant attention. To optimize the performance of shaped catalysts for DME carbonylation, we systematically investigated the role of ordered mesoporous alumina as a binder component. Structural characterization revealed that increasing the proportion of mesoporous alumina in the binder systematically reduced the microporosity while simultaneously enhancing the mesoporous surface area. Acidic property analyses demonstrated that despite the decrease in microporosity, the total acid density remained relatively constant. Notably, catalytic performance evaluations illustrated that the catalyst with 20% mesoporous alumina exhibited significantly enhanced catalytic activity due to improved pore connectivity and mass transfer properties. DME diffusion studies, analyzed through standard and time-fractional diffusion models, further confirmed that mesoporous alumina incorporation significantly improved molecular diffusion, resulting in decreased activation energies for diffusion. These findings provide valuable insights for the future rational design of shaped catalysts, demonstrating that carefully adjusting the proportion of ordered mesoporous alumina effectively balances the catalyst porosity and mass transfer properties to maximize catalytic performance. Dimethyl ether ; Mordenite ; Mesoporous material ; Catalysis ; Mass transfer ; Chemical engineering ; Zeolite ; Carbonylation ; Materials science ; Chemistry ; Organic chemistry ; Chromatography ; Carbon monoxide Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Qingsong Yang | Binglan Wu | Xuwei Liu | Bo Chen | Wei Li | Gen Long | Xin Chen | Mingjun Xiao
Source: Materials Futures (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

DiffNMR: diffusion models for nuclear magnetic resonance spectra elucidation Abstract Nuclear magnetic resonance (NMR) spectroscopy is a key method for molecular structure elucidation. However, interpreting NMR spectra to deduce molecular structures remains challenging due to the complexity of spectral data and the vastness of the chemical space. Here we introduce DiffNMR, a novel end-to-end framework that leverages a conditional discrete diffusion model for de novo molecular structure elucidation from NMR spectra. DiffNMR refines molecular graphs iteratively through a diffusion-based generative process, ensuring global consistency and mitigating error accumulation inherent in autoregressive methods. The framework integrates a two-stage pretraining strategy that aligns spectral and molecular representations via a diffusion autoencoder and contrastive learning. It also incorporates retrieval initialization and similarity filtering during inference. Our experimental results demonstrate that DiffNMR achieves competitive performance for NMR-based structure elucidation, especially outperforming autoregressive models in domain generalization and robustness, thereby offering an efficient and robust solution for automated molecular analysis. Initialization ; Autoencoder ; Autoregressive model ; Consistency (knowledge bases) ; Nuclear magnetic resonance spectroscopy ; Spectral line ; Diffusion ; Similarity (geometry) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Sandip Patil | Sarala Adhau | Xma R. Pote | Vaibhaw R. Doifode | V. S. Rajguru | Tejas R. Patil | Aayushi Jaiswal | Nilesh Shelke | Haytham F. Isleem
Source: Multiscale and Multidisciplinary Modeling Experiments and Design (article, 2025-07-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

An integrated artificial intelligence-driven approach to multi-criteria optimization of nano-materials for high-capacity electric vehicles supercapacitors Supercapacitor ; Nano- ; Computer science ; Engineering ; Capacitance ; Chemical engineering ; Physics Supercapacitor Materials and Fabrication ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry ; Manufacturing Process and Optimization
Authors: Mehrdad Khakbiz | Milad Chagami | Fatemeh Salahi | P. Delshad Khatibi
Source: Journal of Molecular Structure (article, 2025-10-25)
Field: Chemistry — Inorganic Chemistry

Synthesis and release studies of ZIF-8 nanoparticles for ascorbic acid delivery in cancer therapy • ZIF-8 nanoparticles can efficiently intercalate AA molecules within their sodalite structure. • The average particle size increased due to the intercalation of AA, from 90 nm to 160 nm. • AA@ZIF-8 can release AA within two days when used as a drug delivery vehicle at pH 7.4, demonstrating potential for use as a pH-sensitive system in cancer therapy. Metal-Organic Frameworks (MOFs) have recently been used for a wide variety of applications, such as designing nanoparticle-based drug delivery systems. The Zeolite Imidazole Framework (ZIF-8) is a subgroup of these materials. In the current study, ZIF-8 nanoparticles (NPs) were synthesized using the dissolving method in a methanol-based solution, while an in-situ entrapping method was employed to intercalate Ascorbic acid (AA) within the frameworks. Field emission scanning electron microscopy (FESEM) images of ZIF-8 NPs, before and after AA intercalation, showed the particles to be nearly hexagonal with sharp edges. Dynamic light scattering (DLS) measurements revealed that the size range of the as-synthesized NPs was from 90 nm to 160 nm, before and after intercalation, respectively. The X-ray diffraction experiment confirmed that the NPs were formed with perfect crystallinity and retained structural integrity after AA loading. UV-absorbance and FTIR measurements verified the presence of AA molecules within the ZIF-8 NPs. Furthermore, the AA release was investigated by UV-visible spectroscopy at both pH values of 7.4 and 5. Results demonstrated a faster drug release at pH 5, such that approximately the entire amount of AA was released within 10 hours. The release kinetics were mathematically simulated using three conventional models: the first-order, second-order, and parabolic diffusion models. The fitted data indicated that the second-order equation was the most accurate in predicting the rate of AA release over time, with a correlation coefficient (R²) greater than 0.99. Additionally, the first-order and diffusion models could only predict the initial hours of AA discharge from the ZIF-8 NPs effectively. Ascorbic acid ; Nanoparticle ; Intercalation (chemistry) ; Drug delivery ; Crystallinity ; Fourier transform infrared spectroscopy ; Dynamic light scattering ; Molecule Metal-Organic Frameworks: Synthesis and Applications ; Vitamin C and Antioxidants Research ; Nanoplatforms for cancer theranostics
Authors: Yiwei Zhao | Wenbo Liu | Jie Qiu | Jing Li | Di Yun
Source: Physical Review Materials (article, 2025-03-06)
Field: Materials Science — Materials Chemistry

Modeling oxygen diffusion in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>UO</mml:mi><mml:mrow><mml:mn>2</mml:mn><mml:mo>±</mml:mo><mml:mi>x</mml:mi></mml:mrow></mml:msub></mml:math>, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>PuO</mml:mi><mml:mrow><mml:mn>2</mml:mn><mml:mo>±</mml:mo><mml:mi>x</mml:mi></mml:mrow></mml:msub></mml:math>, and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mi mathvariant="normal">U</mml:mi><mml:mo>,</mml:mo><mml:mi>Pu</mml:mi><mml:mo>)</mml:mo><mml:mi mathvariant="normal">O</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mo>±</mml:mo><mml:mi>x</mml:mi></mml:mrow></mml:msub></mml:math> systems This paper combines molecular dynamics, kinetic Monte Carlo, and density functional theory (DFT) methods to calculate the diffusion coefficients of oxygen vacancies and interstitials based on the migration of point defects on the lattice in ${\mathrm{UO}}_{2\ifmmode\pm\else\textpm\fi{}x},\phantom{\rule{0.16em}{0ex}}{\mathrm{PuO}}_{2\ifmmode\pm\else\textpm\fi{}x}$, and (U, Pu) ${\mathrm{O}}_{2\ifmmode\pm\else\textpm\fi{}x}$ systems. The valence and electronic structures of oxygen defects in the ${\mathrm{MO}}_{2\ifmmode\pm\else\textpm\fi{}x}$ system are considered, the configurations of oxygen vacancies and interstitials were identified using the DFT and lattice dynamics methods, and the influence of cation valence change on oxygen diffusion were analyzed quantitatively. The chemical and self-diffusion coefficients in the different stoichiometric ${\mathrm{MO}}_{2\ifmmode\pm\else\textpm\fi{}x}$ systems were obtained on the basis of the defect chemistry theory. In addition, the oxygen diffusion model of the ${\mathrm{MO}}_{2\ifmmode\pm\else\textpm\fi{}x}$ system, considering the effects of cation valence change due to local charge conservation effect, is constructed at the mechanistic level, and the relationships between the diffusion coefficients and the O/M ratio, temperature, Pu concentration, and oxygen partial pressure were analyzed. The findings of this paper provide reference parameters for macrobehavioral analyses of mixed-oxide and ${\mathrm{UO}}_{2}$ fuels, such as constituent redistribution and fuel restructuring. The method in this paper also provides a fresh perspective for the simulation and calculation of oxygen point defect behavior in oxide systems with variable valence cations. Diffusion ; Materials science ; Physics ; Thermodynamics Nuclear Materials and Properties ; Radioactive element chemistry and processing ; Nuclear reactor physics and engineering
Authors: Jubaira Mammoo | P. Malin Bruntha
Source: Journal of Circuits Systems and Computers (article, 2025-03-01)
Field: Chemistry — Analytical Chemistry

Efficient Siamese Pyramid Attention Network with Fusion Autoencoder and Hypercube Gan for Detection of Pesticide Residue Excessive pesticide use in agriculture boosts productivity but raises health risks, complicating pesticide residue detection in fruits and vegetables through hyperspectral imaging. Hence, a novel “Siamese Pyramid Attention with Fusion Autoencoder and Hypercube GAN” is introduced. In the existing segmentation process, it poses challenges due to spectral similarities between pesticide residues and healthy fruits and vegetables. Thus, a novel “Siamese Dual Path Network with Feature Pyramid Attention (DPN-FPA)” is employed, to enhance segmentation by reducing errors by distinguishing residues effectively. Furthermore, temporal dynamics in pesticide residue analysis led to chemical composition variations, necessitating improved unmixing techniques for accurate feature extraction. So, a novel “Sparse Capsule Spatial-Spectral Fusion Autoencoder (SC-SSFA)” is introduced, which seamlessly incorporates spatial and spectral data, enhancing accuracy in pesticide residue detection. Finally, detection faces trade-offs between higher spatial and higher spectral resolutions. Therefore, a novel “Hypercube Conditional Generative Adavector Network (HCGAN)” is implemented, which optimally balances spatial and spectral resolutions using a conditional generative adversarial network architecture thereby enhancing pesticide residue detection in fruits and vegetables. The proposed method demonstrates excellence in segmentation, feature extraction, and detection by achieving a high accuracy of 0.99, precision of 0.98, recall of 0.98, and low mean square error (MSE) of 2.4%. Residue (chemistry) ; Hypercube ; Autoencoder ; Fusion ; Pyramid (geometry) ; Computer science ; Hyperspectral imaging ; Artificial intelligence ; Biological system ; Pattern recognition (psychology) ; Environmental science ; Computer vision ; Mathematics ; Artificial neural network ; Parallel computing ; Chemistry ; Biology Spectroscopy and Chemometric Analyses
Authors: Monia Runge Nielsen | Tianyu Li | Rajat Sainju | Seth March | Chunxiang Zhu | Pu‐Xian Gao | Steven L. Suib | Yuanyuan Zhu
Source: ACS Catalysis (article, 2025-10-03)
Field: Materials Science — Materials Chemistry

<i>Operando</i> Elucidation of Filamentous Carbon Gasification Kinetics and Mechanisms on a Spent Ni/CeO<sub>2</sub> Catalyst Coking is the leading cause of catalyst deactivation in many important hydrocarbon conversion technologies. Understanding regeneration mechanisms is critical for developing effective carbon removal strategies that improve catalyst longevity and reduce operational costs. Here, we present a spatially resolved operando investigation of the regeneration of a spent Ni/CeO2 catalyst under industrially relevant air-like conditions, using in-situ environmental transmission electron microscopy (ETEM) combined with semantic segmentation. By deconvoluting competing gasification events for filamentous carbon removal, we found three distinct gasification modes─while fast catalytic gasification was expected, less steady noncatalytic combustion and cooperative gasification were also present and even more prevalent. Microstructure-informed kinetics directly linked the maximum gasification rates to axial filament consumption through either Ni/carbon contact or filament breakage, emphasizing the pivotal role of edge-plane carbon sites across all gasification pathways. Moreover, our operando characterization uncovered a Ni(−Cx)-limited carbon diffusion mechanism, which challenges the conventional carbon bulk diffusion model typically assumed for catalytic gasification. Furthermore, adverse processes such as Ni/carbon contact disruption and gasification-induced catalyst sintering were also identified. Collectively, these findings provide mechanistic insights into carbon gasification processes, highlighting critical pathways and potential pitfalls that can guide the optimization of catalyst regeneration strategies. Catalysis ; Carbon fibers ; Kinetics ; Methanation ; Combustion ; Diffusion ; Hydrocarbon ; Biomass (ecology) Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Thermochemical Biomass Conversion Processes
Authors: Fumiya Noritake
Source: Computational and Theoretical Chemistry (article, 2025-03-19)
Field: Materials Science — Ceramics and Composites

Two bond-exchange modes in sodium silicate liquids: Slide and oscillation modes Chemistry ; Oscillation (cell signaling) ; Silicate ; Sodium silicate ; Sodium ; Chemical physics ; Chemical engineering ; Organic chemistry Glass properties and applications ; Photorefractive and Nonlinear Optics ; Photonic Crystals and Applications
Authors: Hugo Leonardo André Genier | Juliano Elvis de Oliveira | Sheila Cristina Costa do Santos | Jacqueline Camolese de Araujo | Filippe Elias de Freitas Soares
Source: Next Materials (article, 2025-03-04)
Field: Materials Science — Biomaterials

Controlled release of papain encapsulated in chitosan hydrogels and study of nematicidal action on Panagrellus sp. Alternatives to the use of chemical products traditionally used to combat agricultural pests and diseases have been a concern for society. Thus, based to sustainable agriculture, the use of enzymes has grown as an alternative to the control with traditional. However, some limitations prevent greater effectiveness of action of these important biological catalysts, such as variations in pH and temperature, the presence of inhibitory ions, among others. In this study, papain, a plant enzyme, was incorporated into chitosan hydrogels by cross-linking with glutaraldehyde at different concentrations (0; 0,25 %; 0,5 %; 0,75 %; 1 %). The results of scanning electron microscopy showed a difference in the porous morphology of the different treatments compared to chitosan without crosslinking and the FTIR analysis suggests crosslink formation in the hydrogels. The enzymatic activity of free papain was reduced by approximately 57 % of the initial value. On the outher hand, the results indicate that the activity of the enzyme released from some hydrogels increased and the diffusion of papain probably occurred in the solution from the surface of the formed chitosan hydrogels, represented by a parabolic diffusion model. The mortality of Panagrellus sp., a model nematode, was evaluated and the treatments with hydrogels demonstrated high mortality of Panagrellus sp. above 80 % after 5 days. The hydrogels containing papain were effective in the mortality of the nematodes evaluated and constituted a slow release system, sustainable environmentally, as it has the potential to reduce the number of applications needed to control pests and diseases in agriculture. Chitosan ; Self-healing hydrogels ; Papain ; Chemistry ; Controlled release ; Polymer chemistry ; Materials science ; Biochemistry ; Nanotechnology ; Enzyme Nanocomposite Films for Food Packaging ; Polymer-Based Agricultural Enhancements ; Pineapple and bromelain studies
Authors: Zhangyu Lai | Yilin Lu | Xinyang Li | J. C. Lin | Yi Qu | Liujuan Cao | Ming Li | Rongrong Ji
Source: ArXiv.org (preprint, 2025-03-10)
Field: Materials Science — Materials Chemistry

AnomalyPainter: Vision-Language-Diffusion Synergy for Zero-Shot Realistic and Diverse Industrial Anomaly Synthesis While existing anomaly synthesis methods have made remarkable progress, achieving both realism and diversity in synthesis remains a major obstacle. To address this, we propose AnomalyPainter, a zero-shot framework that breaks the diversity-realism trade-off dilemma through synergizing Vision Language Large Model (VLLM), Latent Diffusion Model (LDM), and our newly introduced texture library Tex-9K. Tex-9K is a professional texture library containing 75 categories and 8,792 texture assets crafted for diverse anomaly synthesis. Leveraging VLLM's general knowledge, reasonable anomaly text descriptions are generated for each industrial object and matched with relevant diverse textures from Tex-9K. These textures then guide the LDM via ControlNet to paint on normal images. Furthermore, we introduce Texture-Aware Latent Init to stabilize the natural-image-trained ControlNet for industrial images. Extensive experiments show that AnomalyPainter outperforms existing methods in realism, diversity, and generalization, achieving superior downstream performance. Texture synthesis ; Anomaly (physics) ; Texture (cosmology) ; Anomaly detection ; Object (grammar) ; Sequence (biology) Machine Learning in Materials Science