jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chenyi Zhou | Rubin Huo | Boyi Situ | Zihan Yan | Yusong Tu | Zhe Zhang
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Unveiling the oxidation mechanisms of octa–penta graphene: a multidimensional exploration from first-principles to machine learning Octa-penta graphene (OPG), a novel carbon allotrope characterized by its distinctive arrangement of pentagonal and octagonal rings, has garnered considerable attention due to its exceptional structure and functional properties. This study systematically investigates the oxidation mechanisms of OPG and elucidates the oxygen migration patterns on the OPG monolayer through first-principles calculations and machine-learning-based molecular dynamics (MLMD) simulations. Specifically, the oxidation processes on OPG-<i>L</i> and OPG-<i>Z</i> involve exothermic chemisorption, where oxygen molecules dissociate at the surfaces, forming stable epoxy groups. Notably, OPG-<i>Z</i> requires higher initial activation energy, reflecting the variable energy demands across different surfaces. The most energetically favorable adsorption sites for an oxygen atom on OPG-<i>L</i> and OPG-<i>Z</i> are the <i>L</i><sub>8-5-2</sub> site and the <i>Z</i><sub>8-5-1</sub> site, respectively, confirmed by their low adsorption energies and optimal bond configurations. Furthermore, the integrated-crystal orbital Hamilton population (ICOHP) and Bader charge analyses provide insights into the physical mechanisms of oxygen atom adsorption. Importantly, we found that oxidation also impacts the electronic properties of OPG, with OPG-<i>L</i> retaining its metallic characteristics post-oxygen adsorption, whereas OPG-<i>Z</i> undergoes a transformation from a metallic to a semiconducting state due to the introduction of oxygen. Oxygen migration on the OPG monolayer involves breaking and reforming of C-O bonds, with varying stability across adsorption sites and limited migration along the basal plane. MLMD simulations corroborate these migration patterns, offering detailed migration trajectories consistent with theoretical predictions. These findings enhance the understanding of oxygen migration dynamics on OPG, facilitate its experimental validations, and highlight its potential as a novel 2D material for applications in batteries, heat-resistant materials, and oxidation-resistant coatings. Graphene ; Chemistry ; Computer science ; Nanotechnology ; Materials science ; Artificial intelligence Graphene research and applications ; Advanced Memory and Neural Computing ; Machine Learning in Materials Science
Authors: Yuanjian Zhang | Jun Cheng | Qinglan Chen | Zhiqiang Zheng | Chengjiang Wei | Tengyue Zou | Weijiang Sun | Yan Huang
Source: Journal of the Science of Food and Agriculture (article, 2025-05-23)
Field: Chemistry — Analytical Chemistry

<scp>Near‐infrared spectroscopy</scp> coupled with Gramian angular field two‐dimensional convolutional neural network for white tea adulteration detection This study introduces the application of the NIR spectral image coding method in tea regression and highlights the advantages of deep learning image processing in the tea industry. © 2025 Society of Chemical Industry. Convolutional neural network ; Gramian matrix ; Near-infrared spectroscopy ; Spectroscopy ; Chemistry ; Field (mathematics) ; Biological system ; Analytical Chemistry (journal) ; Pattern recognition (psychology) ; Mathematics ; Physics ; Artificial intelligence ; Chromatography ; Computer science ; Optics ; Biology ; Eigenvalues and eigenvectors Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Lorenzo Brighel | Gabriella Maria Lucia Scuotto | Giuseppe Antinucci | Roberta Cipullo | Vincenzo Busico
Source: Polymers (article, 2025-06-07)
Field: Materials Science — Materials Chemistry

AI-Aided Crystallization Elution Fractionation (CEF) Assessment of Polyolefin Resins Artificial Intelligence (AI) tools and methods are dramatically innovating the application protocols of most polymer characterization techniques. In this paper, we demonstrate that, with the aid of custom-made and properly trained machine learning algorithms, analytical Crystallization Elution Fractionation (aCEF) can be changed from an ancillary to a standalone approach usable to identify and categorize commercially relevant polyolefin materials without any prior information. The proposed protocols are fully operational for monomaterials, whereas for multimaterials, integration with AI-aided <sup>13</sup>C NMR is a realistic intermediate step. Polyolefin ; Fractionation ; Elution ; USable ; Computer science ; Crystallization ; Materials science ; Process engineering ; Polymer ; Chromatography ; Chemical engineering ; Chemistry ; Nanotechnology ; Engineering ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Polymer crystallization and properties
Authors: A. K. Singh | Archana Nanade | Pratit Bandiwadekar | Pravin D. Patil | Shamraja S. Nadar | Sunny Nanade | Manishkumar S. Tiwari
Source: Journal of the Indian Chemical Society (article, 2025-07-05)
Field: Chemistry — Inorganic Chemistry

Machine learning models for prediction of CO2 adsorption capacity of metal-organic frameworks Chemistry ; Metal-organic framework ; Adsorption ; Metal ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Phase Equilibria and Thermodynamics
Authors: Jian Guan | Zhenhua Dai | Hang Zhou | Zejiang Huang | Xue Lu Wang | Wenlong Zhang | Xiaohong Guan | Mengshuai Liu | Haitao Lv | Shifa Zhong
Source: ACS Applied Materials & Interfaces (article, 2025-10-13)
Field: Materials Science — Materials Chemistry

Design, Synthesis, and Screening of COFs for CO <sub>2</sub> Adsorption by Gaussian Process Effective CO<sub>2</sub> capture requires careful design of Covalent Organic Frameworks (COFs). Current computational approaches often rely solely on simulated properties, neglecting critical chemical and synthetic factors that determine real-world COF performance. We present an integrated computational-experimental framework combining machine learning with experimental validation. Our study analyzes 240 unique COFs (617 samples) with experimentally measured CO<sub>2</sub> adsorption capacities across varied synthesis conditions. Gaussian Process and CatBoost models were developed to predict CO<sub>2</sub> adsorption by simultaneously considering chemical structures, synthesis parameters, and measurement protocols. The GP model demonstrates improved generalization and uncertainty quantification compared to CatBoost. SHAP analysis reveals the model's focus on COF type and synthesis conditions. Using a database of 181 building blocks, we generated 5557 COF structures with synthesis condition recommendations based on experimental similarity. Experimental validation confirmed the predictions for three synthesized COFs, demonstrating the framework's practical utility for COF design and optimization. Adsorption ; Generalization ; Gaussian process ; Gaussian ; Process (computing) ; Focus (optics) ; Covalent bond ; Design of experiments Covalent Organic Framework Applications ; Carbon Dioxide Capture Technologies ; Metal-Organic Frameworks: Synthesis and Applications
Authors: F. Uhlig | Samuel Tovey | Christian Holm
Source: The Journal of Chemical Physics (article, 2025-05-09)
Field: Materials Science — Materials Chemistry

Emergence of accurate atomic energies from machine-learned noble-gas potentials The quantum theory of atoms in molecules gives access to well-defined local atomic energies. Due to their locality, these energies are potentially interesting in fitting atomistic machine learning models as they inform about physically relevant properties. However, computationally, quantum-mechanically accurate local energies are notoriously difficult to obtain for large systems. Here, we show that by employing semiempirical correlations between different components of the total energy, we can obtain well-defined local energies at a moderate cost. We employ this methodology to investigate energetics in noble liquids or argon, krypton, and their mixture. Instead of using these local energies to fit atomistic models, we show how well these local energies are reproduced by machine-learned models trained on the total energies. The results of our investigation suggest that smaller neural networks, trained only on the total energy of an atomistic system, are more likely to reproduce the underlying local energy partitioning faithfully than larger networks. Furthermore, we demonstrate that networks more capable of this energy decomposition are, in turn, capable of transferring to previously unseen systems. Our results are a step toward understanding how much physics can be learned by neural networks and where this can be applied, particularly how a better understanding of physics aids in the transferability of these neural networks. Noble gas ; Krypton ; Locality ; Statistical physics ; Artificial neural network ; Quantum ; Transferability ; Energy (signal processing) ; Computer science ; Physics ; Artificial intelligence ; Argon ; Atomic physics ; Machine learning ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Emily Lın | Yang Zhong | Gang Chen | Sili Deng
Source: npj Computational Materials (article, 2025-07-12)
Field: Chemistry — Inorganic Chemistry

Unified physio-thermodynamic descriptors via learned CO2 adsorption properties in metal-organic frameworks The large design space of metal-organic frameworks (MOFs) has prompted the utilization of deep learning to drive material design. Nonetheless, the prediction of key thermodynamic properties, such as heat of adsorption ( $$\Delta {H}_{{\rm{ads}}}$$ ), remains largely unexplored for CO2 adsorption in MOFs. Herein, we present IsothermNet, a high-throughput graph neural network designed to estimate uptake and $$\Delta {H}_{{\rm{ads}}}$$ over 0–50 bars, enabling high-quality full isotherm reconstruction (PCC: 0.73–0.95 [uptake], 0.76–0.88 [ $$\Delta {H}_{{\rm{ads}}}$$ ]). We further bridged these adsorption properties to uptake behaviors (i.e., isotherm shapes/types) and structural information by performing detailed ablation studies to investigate the relative importance of local and global features in relation to predictive performance. This comparative analysis facilitated the discovery of a (1) physically-interpretable and (2) analytically-derived universal descriptor set capable of illustrating interdependencies between easily-computed, accessible textural information and extrinsic adsorption properties. When used cooperatively with IsothermNet, these descriptors enable efficient material screening, accelerating high-performance MOF discovery for CO2 capture. Metal-organic framework ; Adsorption ; Thermodynamics ; Materials science ; Chemistry ; Physical chemistry ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics
Authors: Wenjie Wang | K. Jia | Yuwen Cheng
Source: Colloids and Surfaces A Physicochemical and Engineering Aspects (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Prediction the effect of oxygen vacancies and single transition metal doping on CO2RR activity of Ta2CO2 MXene based on first principles and machine learning MXenes ; Transition metal ; Oxygen ; Catalysis ; Doping ; Valence (chemistry) ; Electronic structure ; Metal MXene and MAX Phase Materials ; CO2 Reduction Techniques and Catalysts ; Ammonia Synthesis and Nitrogen Reduction
Authors: Min Zhang | Liangyu Liu | Ling Ding | Ningjie Fang | Yinghao Chu
Source: Journal of environmental chemical engineering (article, 2025-07-16)
Field: Materials Science — Materials Chemistry

Modulating surface properties of catalysts to promote the oxidation of multi-component VOCs Catalysis ; Component (thermodynamics) ; Chemical engineering ; Chemistry ; Environmental chemistry ; Materials science ; Organic chemistry ; Engineering Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Diego Ugarte La Torre | Yuji Sugita
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-06-08)
Field: Chemistry — Inorganic Chemistry

CGBack: Diffusion Model for Backmapping Large-Scale and Complex Coarse-Grained Molecular Systems ABSTRACT Molecular dynamics simulations based on coarse-grained (CG) models are used to accelerate conformational dynamics of biomolecules and other chemical systems with reduced computational costs. CG models achieve it by discarding atomic information necessary for downstream structural analysis. Recovering the atomic detail from CG structures, i.e. backmapping, remains a fundamental challenge in multiscale modeling, especially for proteins and complex biomolecular assemblies. Recent machine learning methods have shown promise in reconstructing atomistic details, but most approaches only target simple systems at a small scale. In particular, conventional backmapping pipelines often fail to preserve stereochemistry, induce high-energy configurations, or require extensive minimization. Here we present CGBack, a backmapping framework that employs a denoising diffusion probabilistic model to reconstruct all-atom molecular structures from CG representations. CGBack consists of backmapping and refinement procedures and accurately recovers atomic coordinates across diverse protein systems from small to large scales. We show that CGBack is capable of accurately backmapping both single-chain and multi-chain molecular systems, including densely packed intrinsically disordered proteins in condensates. These results suggest that CGBack can be a powerful tool for multiscale molecular simulation pipelines. We anticipate that CGBack will enable more efficient workflows for protein modelling, as well as for other biomolecules across different CG models. Scale (ratio) ; Diffusion ; Statistical physics ; Computer science ; Geography ; Physics ; Cartography ; Thermodynamics Zeolite Catalysis and Synthesis ; Diffusion Coefficients in Liquids
Authors: Yu Liang | Lu Li | Qi Zhou | Yuqiu Ye | Wei Jiang
Source: Journal of Alloys and Compounds (article, 2025-11-16)
Field: Materials Science — Biomaterials

Hot workability and microstructure development of Mg–8.0 Al–1.0 Yb–0.5 Zn magnesium alloy Dynamic recrystallization ; Hot working ; Nucleation ; Strain rate ; Microstructure ; Isothermal process ; Recrystallization (geology) ; Deformation (meteorology) Magnesium Alloys: Properties and Applications ; Metallurgy and Material Forming ; Metal Forming Simulation Techniques
Authors: Shanza Anzar | Ayesha Sohail | Lubna Sherin | Peter S. Kim
Source: Journal of Nanotechnology (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Computer Methods for the Antimicrobial Efficacy of Metal Nanoparticles Against MDR Microbes The current high mortality rate associated with infectious diseases is an alarming issue worldwide. The emergence of multidrug‐resistant bacterial strains has led to the development of new ailments and the resurgence of old pathogenic infections. To address this issue, metal and metal oxide nanoparticles have emerged as potent nanoantimicrobial agents, offering an alternative to clinically used conventional antibiotics. This study employs support vector machine learning classification algorithms to identify the key attributes of inorganic nanoparticles that influence their antimicrobial efficacy. Key attributes such as type, size, and concentration of nanoparticles, along with transient analysis factors such as exposition time and bactericidal potency evaluation methods, are thoroughly analyzed and documented. The nonlinearity in this time‐dependent data and the complexity of the potent size and concentration of these nano‐antibiotics are simplified through a stepwise machine learning classification process involving encoding and decoding, supported by smart optimization of classifiers. These classifiers help identify the best attributes of nanoparticles to achieve the highest in vitro antimicrobial efficacy of unknown metallic nanoparticles in a robust and cost‐efficient manner. The integration of machine learning techniques in nanomaterial research enhances the understanding and development of smart materials, and thus, the research in this domain can achieve noticeable milestones. Materials science ; Antimicrobial ; Metal ; Nanotechnology ; Microbiology ; Metallurgy ; Biology Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Nanoparticle-Based Drug Delivery
Authors: Reliance Jain | Sandeep Jain | Sheetal Kumar Dewangan | Sumanta Samal | Hansung Lee | Eunhyo Song | Younggeon Lee | Byungmin Ahn
Source: Journal of Magnesium and Alloys (article, 2025-08-01)
Field: Materials Science — Biomaterials

Prediction of alloying element effects on the mechanical behavior of high-pressure die-cast Mg-based alloys • Machine learning models predict mechanical properties of high-pressure die-cast Mg-based alloys, with CatBoost showing the best performance. • CatBoost achieved coefficients of determination of 0.956 for ultimate tensile strength and 0.936 for yield strength. • The approach provides a faster alternative to traditional experimental methods for alloy optimization. Achieving optimal mechanical performance in high-pressure die-cast (HPDC) Mg-based alloys through experimental methods is both costly and time-intensive due to significant variations in composition. This study leverages machine learning (ML) techniques to accelerate the development of high-performance Mg-based alloys. Data on alloy composition and mechanical properties were collected from literature sources, focusing on HPDC Mg-based alloys. Six ML models—extra trees, CatBoost, k-nearest neighbors, random forest, gradient boosting, and decision tree—were trained to predict mechanical behavior. CatBoost yielded the highest prediction accuracy with R 2 scores of 0.95 for ultimate tensile strength (UTS) and 0.92 for yield strength (YS). Further validation using published datasets reaffirmed its reliability, demonstrating R 2 values of 0.956 (UTS) and 0.936 (YS), MAE of 1% and 2.8%, and RMSE of 1% and 3.5%, respectively. Among these, the CatBoost model demonstrated the highest predictive accuracy, outperforming other ML techniques across multiple optimization metrics. Materials science ; Die (integrated circuit) ; Metallurgy ; Alloy ; High pressure ; Die casting ; Composite material ; Engineering physics ; Nanotechnology Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: Wenyang Ding | Jiang Guo | Meng An | Koji Tsuda | Junichiro Shiomi
Source: npj Computational Materials (article, 2025-05-28)
Field: Materials Science — Materials Chemistry

Human–AI collaboration for modeling heat conduction in nanostructures Materials informatics, which combines data science and artificial intelligence (AI), has garnered significant attention owing to its ability to accelerate material development. However, human involvement is often limited to the initiation and oversight of machine learning processes and rarely includes roles that capitalize on human intuition or domain expertise. In this study, taking the problem of heat conduction in a two-dimensional nanostructure as a case study, an integrated human-AI collaboration framework is designed and used to construct a model to predict the thermal conductivity. This approach is used to determine the parameters that govern phonon transmission over frequencies and incidence angles. The self-learning entropic population annealing technique, which combines entropic sampling with a surrogate machine learning model, generates a global dataset that can be interpreted by a human. This allows humans to develop parameters with physical interpretations, which can guide nanostructural design for specific properties. Thermal conduction ; Nanostructure ; Materials science ; Nanotechnology ; Computer science Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Kamran-ul-Haq Khan | Imran Siddiqui
Source: Scientific Reports (article, 2025-07-01)
Field: Materials Science — Biomaterials

A predictive model for electrospun based Polyvinyl alcohol (PVA) nanofibers diameter using an artificial neural network The aim of this work is to develop a predictive model for electrospun based polyvinyl alcohol (PVA) nanofiber diameter. Artificial Neural Network is employed to analyze the key variables such as applied electric field, the polymer concentrations, the rate of injection and nozzle collector distance, involves in the process of electrospinning affecting the diameter of PVA nanofiber. The most suitable network architecture was determined by considering and examining different topologies in artificial neural networks (ANNs) that composed of single and double hidden layers with different numbers of nodes for each layer. Strong prediction capabilities of the developed model are observed in the analysis of the parameters influencing the diameter of spun fibers for ANN configuration of 5-9-1. The findings revealed that PVA fiber diameters' predicted in our work and the already existing experimental data are significantly correlated with a regression that is around 0.973. Also, a low value of average absolute error i.e., 0.06 is obtained after the evaluation of the developed model. Polyvinyl alcohol ; Nanofiber ; Electrospinning ; Materials science ; Artificial neural network ; Biomedical engineering ; Composite material ; Computer science ; Polymer ; Artificial intelligence ; Medicine Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: Fan Cheng | Maodong Li | Sihao Yuan | Zhi‐Ping Xie | Dechin Chen | Yi Yang | Yi Qin Gao
Source: Journal of Chemical Theory and Computation (article, 2025-07-30)
Field: Materials Science — Materials Chemistry

Performing Path Integral Molecular Dynamics Using an Artificial Intelligence-Enhanced Molecular Simulation Framework This study employed an artificial intelligence-enhanced molecular simulation framework to enable efficient path integral molecular dynamics (PIMD) simulations. Owing to its modular architecture and high-throughput capabilities, the framework effectively mitigates the computational complexity and resource-intensive limitations associated with conventional PIMD approaches. By integrating machine learning force fields (MLFFs) into the framework, we rigorously tested its performance through two representative cases: a small-molecule reaction system (double-proton transfer in the formic acid dimer) and a bulk-phase transition system (water-ice phase transformation). Computational results demonstrate that the proposed framework achieves accelerated PIMD simulations while preserving the quantum mechanical accuracy. These findings show that nuclear quantum effects can be captured for complex molecular systems using relatively low computational cost. Molecular dynamics ; Computer science ; Path (computing) ; Data science ; Artificial intelligence ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods