jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lu Wang | Ruijuan Xiao | Kai Li
Source: Chinese Physics B (article, 2025-10-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

EDIS: A simulation software for dynamic ion intercalation/deintercalation processes in electrode materials Abstract As the core determinant of lithium-ion battery performance, electrode materials play a crucial role in defining the battery’s capacity, cycling stability, and durability. During charging and discharging, electrode materials undergo complex ion intercalation and deintercalation processes, accompanied by defect formation and structural evolution. However, the microscopic mechanisms underlying processes such as cation disordering, lattice oxygen loss, and stage structure formation are still not fully understood. To address these challenges, we have developed the Electrode Dynamic Ion Intercalation/Deintercalation Simulator (EDIS), a software platform designed to simulate the dynamic processes of ion intercalation and deintercalation in electrode materials. Leveraging high-precision machine learning potentials, EDIS can efficiently model structural evolution and lithium-ion diffusion behavior under various states of charge and discharge, achieving accuracy approaching that of quantum mechanical methods in relevant chemical spaces. The software supports quantitative analysis of how variations in lithium-ion concentration and distribution affect lithium-ion transport properties, enables evaluation of the impact of structural defects, and allows for tracking of both structural evolution and transport characteristics during continuous cycling. EDIS is versatile and can be extended to sodium-ion batteries and related systems. By enabling in-depth analysis of these microscopic processes, EDIS provides a robust theoretical tool for mechanistic studies and the rational design of high-performance electrode materials for next-generation lithium-ion batteries. Electrode ; Intercalation (chemistry) ; Ion ; Lattice (music) ; Software ; Diffusion ; Electrostatics ; Battery (electricity) Copper Interconnects and Reliability
Authors: Liberty L. Mguni | Olga K. Mmelesi | Andrew Ndhlovu | Joshua Gorimbo | Xuefeng Qian | Yali Yao
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Selective Production of Hydrocarbons (C5+) from Co2 Hydrogenation: Exploring Clusters and Machine Learning Production (economics) ; Chemistry ; Economics Machine Learning in Materials Science ; Catalysts for Methane Reforming ; Carbon dioxide utilization in catalysis
Authors: Sanja J. Armaković | Stevan Armaković
Source: Chemosensors (article, 2025-09-19)
Field: Chemistry — Analytical Chemistry

Advanced GC-MS Chemosensing Combined with Atomistic Modeling: A Synergistic Approach for Environmental Water Analysis Gas chromatography–mass spectrometry (GC-MS) plays a crucial role in analyzing complex water samples due to its high sensitivity, selectivity, and robustness. Recent developments have transformed GC-MS into a powerful chemosensor platform, capable of generating detailed chemical fingerprints for targeted or untargeted environmental analysis. This review highlights the integration of GC-MS with atomistic modeling approaches, including quantum chemical calculations and molecular simulations, to enhance the interpretation of mass spectra and support the identification of emerging contaminants and transformation products. These computational tools offer mechanistic insight into fragmentation pathways, molecular reactivity, and pollutant behavior in aqueous environments. Emphasis is placed on recent trends that couple GC-MS with machine learning, advanced sample preparation, and simulation-based spectrum prediction, forming a synergistic analytical framework for advanced water contaminant profiling. The review concludes by addressing current challenges and outlining future perspectives in combining experimental and theoretical tools for intelligent environmental monitoring. Environmental analysis ; Identification (biology) ; Water pollutants ; Molecular dynamics ; Pollutant ; Environmental systems ; Aqueous medium ; Mass spectrometry Analytical chemistry methods development ; Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography
Authors: Nadab Raza | Sheeza Pervaiz | Urooj Haroon
Source: Preprints.org (preprint, 2025-04-28)
Field: Materials Science — Materials Chemistry

Advaces in Hydrogen Energy Storage Technologies: Materials, Mechanisms, and Future Prospects Hydrogen is an environmentally friendly fuel with a high energy density and is a critical component of sustainable energy systems; however, storing hydrogen as an energy carrier is one of the most significant technological challenges. This review has reviewed hydrogen storage technologies, emphasizing physical storage methods, chemical hydrides, and emerging nanomaterials. This review gave special attention to the metals hydride, specifically magnesium hydride (MgH₂), lithium hydride (LiH), sodium alanate, and ammonia borane, while discussing the limitations of their thermodynamics and kinetics in addition to recent advances in the performance of these metal hydrides for hydrogen fuel. These advances in hydrogen storage using metal hydrides will employ advanced porous materials such as metalorganic frameworks (MOFs), covalent organic frameworks (COFs), and carbon nanostructures, showing promising adsorbability. One section of the review also assesses the role of computational screening and machine learning to expedite the discovery of new materials for hydrogen storage. The review synthesizes the most effective ways to design genomic hydrogen storage devices that improve safety, efficiency, and scaling. Hydrogen storage ; Energy storage ; Hydrogen technologies ; Hydrogen fuel ; Hydrogen ; Nanotechnology ; Engineering physics ; Environmental science ; Process engineering ; Computer science ; Materials science ; Chemistry ; Engineering ; Physics ; Hydrogen economy ; Thermodynamics Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems
Authors: Boyang Liang | Wenjia Huo | Xiang Wu | Rongrong Zheng
Source: ACS Omega (article, 2025-10-01)
Field: Materials Science — Polymers and Plastics

Machine Learning-Enabled Parallel Prediction and Co-Design of Polyimides for Tailored Glass Transition, Dielectric, and Bandgap Properties Polyimide (PI) is widely used in aerospace, nuclear industries, microelectronics, and flexible devices due to its exceptional thermal stability, electrical insulation, mechanical strength, and low dielectric loss. However, developing high-performance PIs traditionally requires decades of experimental effort. To accelerate discovery, we integrated machine learning (ML) with molecular dynamics (MD) and quantum mechanics (QM) simulations, demonstrating the efficacy of data-driven approaches for novel PI development. We collected 1499 PI structures from relevant literature, with experimentally measured glass transition temperature (<i>T</i> <sub>g</sub>), dielectric constant (DC), and bandgap width (EG). Subsequently, substructure information was extracted from their SMILES encoding as feature inputs for the model, and 90 ML models were established to describe the various properties of PI. The resulting machine learning model had good predictive performance in identifying key chemical substructures that affect PI performance. SHAP analysis identified critical substructures governing performance, guiding the design of PI with superior dielectric properties, thermal stability, or insulation capabilities compared to those of existing benchmarks. MD and QM simulations validated ML predictions showing excellent agreement, confirming the designed PI's performance and the models' extrapolation reliability. This work establishes an ML-driven approach integrating MD/QM validation to expedite the exploration of innovative polymers, offering a theoretical foundation for experimental synthesis. Extrapolation ; Dielectric ; Substructure ; Polyimide ; Glass transition ; Band gap ; Thermal ; Work (physics) Synthesis and properties of polymers ; Dielectric materials and actuators ; Epoxy Resin Curing Processes
Authors: Natalia Osiecka | Zbigniew Galewski | Marcin Piwowarczyk | Ewa Juszyńska‐Gałązka
Source: The Journal of Physical Chemistry B (article, 2025-09-17)
Field: Materials Science — Materials Chemistry

Deep Learning Analysis of Crystallization Using Polarized Light Microscopy and U-Net Segmentation Understanding the crystallization behavior of materials is essential to controlling their physical properties. In this study, we present an approach that combines polarized light microscopy with deep learning techniques to investigate the crystallization process of liquid-crystalline compound 9BA4. A U-Net convolutional neural network was trained to perform semantic segmentation of microscopy textures, enabling automated identification of crystalline (Cr) and smectic (SmC) phases during nonisothermal cooling performed at multiple cooling rates. The model outputs probability maps, which are binarized to quantify the degree of crystallization over the temperature. The crystallization kinetics were further analyzed by fitting a sigmoidal function to the experimental data, and the inflection point of the fitted curve was used to identify the temperature of maximum crystallization. The data were then fitted to the Ozawa model. The proposed methodology demonstrates the effectiveness of combining traditional optical techniques with neural-network-based image analysis to extract quantitative insights from complex texture evolution during phase transitions. Crystallization ; Microscopy ; Convolutional neural network ; Segmentation ; Sigmoid function ; Optical microscope ; Polarized light microscopy ; Deep learning ; Inflection point Crystallization and Solubility Studies ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Xiaojiang Liang | Haotian Fei | Fengjiao Wu | Jiawei Ma | Zhenyu Wu | Yong Nie
Source: European Journal of Lipid Science and Technology (article, 2025-02-09)
Field: Materials Science — Biomaterials

Valorization of Biodiesel Into Bio‐Based Plasticizers: Optimization of Epoxidized Fatty Acid Isobutyl Ester Production Using Response Surface Methodology and Artificial Neural Network ABSTRACT Biodiesel is a promising green chemical feedstock due to its renewability and sustainability. In this study, bio‐based plasticizers, epoxidized fatty acid isobutyl esters (Ep‐FABEs), were prepared using biodiesel as a feedstock through a combination of transesterification and the formic acid autocatalytic method. The effects of reaction temperature, reaction time, FA/C═C molar ratio, and H 2 O 2 /C═C molar ratio on the conversion to oxirane (RCO) during the epoxidation process were investigated using a central composite design. Both response surface methodology (RSM) and artificial neural network (ANN) models were developed to model and optimize the epoxidation process. Comparative analysis revealed that the ANN model demonstrated superior predictive capabilities, with lower mean squared error (MSE), lower mean absolute error (MAE), and a higher coefficient of determination ( R 2 ) compared to the RSM model. The ANN model predicted an RCO of 92%, which was closely aligned with the experimental value of 91% under optimized conditions (reaction temperature of 66°C, reaction time of 6.7 h, FA/C═C molar ratio of 0.35, and H 2 O 2 /C═C molar ratio of 2.70). Additionally, the physico‐chemical properties of Ep‐FABEs were further analyzed. These findings provide valuable insights into the production of bio‐based plasticizers using biodiesel as a feedstock. Response surface methodology ; Biodiesel ; Plasticizer ; Biodiesel production ; Raw material ; Transesterification ; Central composite design ; Chemistry ; Formic acid ; Materials science ; Organic chemistry ; Chemical engineering ; Chromatography ; Catalysis biodegradable polymer synthesis and properties ; Polymer Science and PVC ; Advanced Polymer Synthesis and Characterization
Authors: Hannah Toru Shay | Gabi Wenzel | Ci Xue | Brett A. McGuire
Source: The Astrophysical Journal (article, 2025-10-24)
Field: Chemistry — Spectroscopy

Bridging Machine Learning and Spectroscopy: A New Analysis for Astrochemical Target Selection Abstract In the last few years, several approaches have used machine learning to reproduce the inventory and column densities of interstellar molecular sources. These models can also provide predictions of other molecules that might make good targets for discovery in a source, along with a predicted column density. A molecular target and column density alone are often not enough to dictate a good target; spectroscopic considerations (e.g., permanent electric dipole moments, line intensities, and line frequencies) need to be accounted for as well. Here we describe a new approach to take these considerations into account, in an attempt to identify the hidden likely candidates among thousands of molecules and associated column densities recommended by machine learning. We leverage machine learning results alongside quantum chemical and astronomical considerations to define a “detectability” metric and then apply this analysis to machine learning–recommended molecules in TMC–1. We find that this approach significantly narrows the field of candidate species but also highlights some of the ongoing issues with results from machine learning models trained on the very small (for machine learning) data sets produced by observations. We discuss these in the context of the need for human intervention, finding that our approach reduces but certainly does not eliminate this requirement. Leverage (statistics) ; Bridging (networking) ; Context (archaeology) ; Metric (unit) ; Dipole ; Quantum Molecular Spectroscopy and Structure ; Mass Spectrometry Techniques and Applications ; Isotope Analysis in Ecology
Authors: Xiang‐Yang Liu | Dongsheng Xiao | Wei‐Hai Fang | Ganglong Cui
Source: Journal of Chemical Theory and Computation (article, 2025-09-04)
Field: Materials Science — Materials Chemistry

Excited-State Wave Functions and Energies Predicted by Machine Learning Based on Graph Neural Networks Accurate and efficient simulation of photoinduced dynamics in materials remains a significant challenge due to the computational cost of excited-state electronic structure calculations and the necessity to account for excitonic effects. In this work, we present a machine learning (ML)-accelerated approach to nonadiabatic molecular dynamics simulations that incorporates excitonic effects by predicting excited-state wave functions via configuration interaction coefficients and excitation energies using a graph neural network (GNN) architecture. The GNN model leverages molecular orbital information from ground-state calculations to construct input graphs, enabling efficient and accurate prediction of relevant excited-state wave functions and energies required for ab initio-based fewest-switches surface hopping simulations. Benchmarking on a zinc phthalocyanine-fullerene (ZnPc-C<sub>6</sub><sub>0</sub>) donor-acceptor system reveals that these ML-predicted properties agree closely with those obtained from linear-response time-dependent density functional theory calculations while boosting the computational efficiency significantly. The ML-accelerated simulations reproduce excited-state dynamics with high fidelity, demonstrating the methodological capability to study complex photodynamical processes in large systems. This work provides a general and scalable framework for efficient excited-state dynamics simulations in materials where excitonic effects play a vital role. Computer science ; Excited state ; Graph ; Artificial neural network ; Wave function ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Physics ; Atomic physics Machine Learning in Materials Science ; Semiconductor Quantum Structures and Devices ; Spectroscopy and Quantum Chemical Studies
Authors: Mrunal M. Patil | Pramod A. Koyale | Sonali P. Sadavar | Anjali R. Shelake | Tukaram D. Dongale | Ananta G. Dhodamani | Santosh S. Sutar | Sagar D. Delekar
Source: Nanoscale (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Facile design of MOF-derived porous CeO<sub>2</sub>/MWCNT nanocomposites for the hydrogen evolution reaction and machine learning-assisted stability forecasting Applying porous electrocatalysts in electrochemical water splitting (ECWS) represents a highly effective approach to enhancing catalytic performance. In this context, MOF-derived metal oxides with controlled morphologies are considered promising candidates for advanced electrochemical studies. This study focuses on the synthesis of cerium oxide (CeO<sub>2</sub>) nanostructures derived from a cerium-based MOF, specifically Ce-1,3,5-benzenetricarboxylic acid (Ce-BTC), through thermal decomposition. To further improve the conductivity and catalytic activity, the CeO<sub>2</sub> nanostructures were modified with varying amounts of multiwalled carbon nanotubes (MWCNTs) <i>via</i> an <i>ex situ</i> sonochemical method, forming CeO<sub>2</sub>/MWCNT nanocomposites (denoted as CCNT NCs). Binder-free electrodes were fabricated by brush coating the NCs onto nickel foam substrates. The electrocatalytic performance was assessed for the hydrogen evolution reaction (HER) in 1 M KOH. Among the synthesised composites, the CCNT-0.1 NCs (containing 0.1 wt% MWCNTs) exhibited the most promising performance, achieving a low overpotential of 87 mV at a current density of -10 mA cm<sup>-2</sup> and a Tafel slope of 96 mV dec<sup>-1</sup>. These results surpass the performance of many previously reported CeO<sub>2</sub>-based HER electrocatalysts. The long-term operational stability of the CCNT-0.1 electrode was also analyzed using a Long Short-Term Memory (LSTM)-based time series model to predict its degradation behavior over extended durations. Overall, this work demonstrates the synergistic advantages of carbonaceous materials and nanostructured metal oxide NCs as efficient and durable electrocatalysts for hydrogen generation. Nanocomposite ; Materials science ; Porosity ; Stability (learning theory) ; Nanotechnology ; Hydrogen ; Chemical engineering ; Computer science ; Composite material ; Engineering ; Machine learning ; Chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Gas Sensing Nanomaterials and Sensors ; Advanced Nanomaterials in Catalysis
Authors: Amina Bibi | Afzaal Hussain | Tariq Munir | M. Z. Yaqoob | Arslan Mahmood | Shoaib Nazir | Yasin Khan
Source: Journal of Materials Science Materials in Medicine (article, 2025-11-21)
Field: Materials Science — Polymers and Plastics

Machine learning-based fabrication of phytogenic NiO nanoparticles for anticancer activity in HepG2 Cell Culture Metal oxide nanomaterials play a central role in biomedical applications due to their unique physicochemical properties. In particular, various treatment methods such as drug delivery, hyperthermia therapy, radiation, and chemotherapy are used for the treatment of carcinoma. Current studies prefer to investigate the anticancer activity of nickel oxide nanoparticles were synthesized using a green synthesis approach. The X-ray diffraction (XRD) analysis was used to investigate the cubic crystalline structure and crystallite size varies from 11.08 nm to 12.88 nm due to increased calcination temperature. The crystallite size has a significant impact on the cytotoxicity and toxicity of nanoparticles; smaller crystal sizes frequently result in higher toxicity, because of their larger surface area to volume ratio. The MTT (Tetrazolium salts) assay was performed to test the cytotoxicity of NiO nanoparticles (NPs) against HepG2 cell line. After that, machine learning was applied to connect the biomedical field with artificial intelligence. It can be seen from the results that the NiO NPs that were calcinated at 600 °C gave the average cell viability <40%. At last, the machine learning approach was used to calculate the cytotoxicity of NiO NPs and decision tree was generated by using Google Colab. A correlation matrix was generated using a machine learning approach, providing insights into the interdependence among all parameters. Cytotoxicity ; Nanomaterials ; Nanoparticle ; Nickel oxide ; Non-blocking I/O ; Crystallite ; Viability assay ; MTT assay Transition Metal Oxide Nanomaterials ; Magnesium Oxide Properties and Applications ; Nanoparticles: synthesis and applications
Authors: Valeria D. Babaylova | Vladislav S. Tuchin | N.S. Petrov | A.V. Kochakov | Anton A. Starovoytov | Igor A. Gladskikh | Daler R. Dadadzhanov
Source: Photonics (article, 2025-06-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

AI-Assisted Plasmonic Coupling Analysis of Spherical Gold Nanoparticles on Substrate A method of electrostatic deposition of CTAB-stabilized gold nanoparticles on a modified APTES and PSS surface was considered. Positively charged gold nanoparticles with a spherical shape were synthesized using a one-step synthesis method with a CTAB surfactant and deposited on a negatively charged modified glass substrate surface with an APTES/PSS layer. Depending on the concentration of the gold nanoparticles, the deposition time, and the modification of the substrate, both isolated nanoparticles with a narrow plasmon peak close to the maximum position in solution, and interacting nanoparticles with varying degrees of plasmonic coupling, were obtained. We also present a deep learning approach for rapid, non-contact estimation of relative plasmon coupling (PC) in gold nanoparticles deposited on substrates using simple camera images. To obtain the training dataset, gold nanoparticles were characterized by the intensity of peaks corresponding to plasmonic coupling in the long-wavelength region of the spectrum. A fully connected neural network was trained to regress PC values from color features, minimizing the mean-squared error. The best model, retrained on the full training set, achieved R2 = 0.83, RMSE = 0.007, MSE = 0.086, and MAE = 0.050 on the test dataset. Colloidal gold ; Nanoparticle ; Plasmon ; Materials science ; Substrate (aquarium) ; Plasmonic nanoparticles ; Coupling (piping) ; Surface plasmon resonance ; Deposition (geology) ; Surface plasmon ; Nanotechnology ; Optoelectronics ; Composite material Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research