jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ruoxia Chen | Mathieu Bauchy | Wei Wang | Yizhou Sun | Xiaojie Tao | Jaime Marian
Source: Scientific Reports (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Using graph neural network and symbolic regression to model disordered systems The key to modeling disordered systems lies in accurately simulating atomic trajectories, typically achieved through molecular dynamic (MD) simulation. The accuracy of MD simulations depends on the precision of the interatomic potential function, which dictates the calculations of atom movements. Traditionally, deriving interatomic potential function relies on extensive prior physical knowledge and high computational cost. This study introduces a novel approach that integrates machine learning with molecular dynamic methods to provide precise interatomic potential energy calculations for disordered systems. Computer science ; Interatomic potential ; Molecular dynamics ; Atom (system on chip) ; Function (biology) ; Graph ; Artificial neural network ; Key (lock) ; Statistical physics ; Theoretical computer science ; Artificial intelligence ; Algorithm ; Machine learning ; Physics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Xin Zhao | Shibin Han | Cheng Chen | Xiaoyi Lv | Jiahe Li | Chen Chen
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2025-11-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep learning-assisted SERS platform for label-free detection of celecoxib in serum using ag@Pt@porous silicon Bragg mirror composite substrate Substrate (aquarium) ; Reproducibility ; Silicon ; Raman spectroscopy ; Crystal violet ; Composite number Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Nanomaterials in Catalysis
Authors: Michael Gyaabeng | Ramadan Ahmed | Cătălin Teodoriu | Deepak Devegowda | Nayem Ahmed
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Metals and Alloys

Modeling the Impact of Hydrogen Embrittlement on the Fracture Toughness of Carbon Steel Using a Machine Learning Approach Hydrogen embrittlement ; Materials science ; Fracture toughness ; Hydrogen ; Toughness ; Embrittlement ; Random forest ; Environmental stress fracture ; Machine learning ; Metallurgy ; Computer science ; Corrosion ; Chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Fatigue and fracture mechanics
Authors: Mao-Ken Hsu | Boyu Huang | Chi‐Hua Yu
Source: Materials & Design (article, 2025-09-22)
Field: Materials Science — Polymers and Plastics

Ai-driven optimization of woven composite via integrated deep and reinforcement learning Graphical abstract of using AI models to directly predict the mechanical properties of woven composites and optimally design structures. • AI models enhance finite element simulations, reducing time and cost while improving accuracy in woven composite design. • Convolutional Neural Networks (CNN) accelerate the prediction of mechanical properties, speeding up the design process. • Deep Q-learning Networks (DQN) optimize mechanical properties, enhancing the efficiency of composite material design. • The reduced model reduces simulation time, and with DQN it enables more efficient and effective product design. Woven carbon fiber composites are increasingly adopted in advanced structural applications due to their exceptional strength-to-weight ratio and tunable design features. However, high-fidelity simulations of their complex woven architecture are computationally intensive. This study presents a hybrid deep learning framework that combines a dual-input Convolutional Neural Network (CNN) for mechanical property prediction with a Deep Q-Network (DQN) for reinforcement learning-based optimization. The CNN achieves R 2 values above 0.96 for elastic deformation, plastic deformation, and strain energy density prediction. Using the DQN, the optimized design achieves a 2.37-fold improvement in strain energy density, increasing from 3590.78 J/m 3 to 8527.85 J/m 3 . Furthermore, by replacing the original woven geometry with a reduced model using stress–strain behavior, simulation time is reduced from 534 min to 2 min, a 267-fold speedup. This approach significantly enhances efficiency in composite design and optimization workflows, enabling rapid exploration of high-performance configurations. Composite number ; Convolutional neural network ; Woven fabric ; Finite element method ; Reinforcement learning ; Artificial neural network ; Reinforcement ; Deep learning Textile materials and evaluations ; Mechanical Behavior of Composites ; Epoxy Resin Curing Processes
Authors: Kannan Subburaj | Nuha Alruwais | Rana Alabdan | Haya Mesfer Alshahrani
Source: Matéria (Rio de Janeiro) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Revolutionizing iron texture analysis: the role of cold reduction and rolling directions through machine learning insights ABSTRACT This study employs machine learning (ML) to analyze the melting and reconsolidation behaviors of iron, emphasizing the influence of cold reduction ratios and rolling sequences. Five samples with varied cold reduction ratios and rolling patterns were examined. Findings indicate that when the cold reduction ratio exceeds 65%, coordinated cold melting minimally impacts crystallographic consistency. Texture formation remains largely unaffected during cold melting and short-duration annealing. However, extended annealing prompts irregular grain growth, altering crystal orientation. Sheets rolled in alignment with their initial condition exhibit consistency patterns similar to conventionally cold-melted pure iron after prolonged annealing. Key parameters influencing material performance were evaluated, revealing annealing temperature as the most significant factor (5.94), followed by cold melting direction order (1.46), while the hanging period during annealing had minimal impact (1.02). ML models were employed to predict Goss angle expansion using cold-rolling and annealing parameters. This approach demonstrates the potential of ML to predict texture evolution in pure iron, offering valuable insights for optimizing industrial cold-rolling practices. Texture (cosmology) ; Reduction (mathematics) ; Artificial intelligence ; Computer science ; Mathematics ; Geometry ; Image (mathematics) Machine Learning in Materials Science ; Hydrocarbon exploration and reservoir analysis ; Image Processing and 3D Reconstruction
Authors: Hossein Sarvi | Sajad Dehdari | Mehdi Maleki | Marzieh Baziari | Yousef Kazemzadeh
Source: Cleaner Engineering and Technology (article, 2025-07-05)
Field: Chemistry — Inorganic Chemistry

Artificial intelligence-based prediction of hydrogen uptake of metal organic frameworks with high volumetric storage density for fuel cell systems The growing release of greenhouse gases from hydrocarbon use has intensified the demand for clean and sustainable energy solutions. Hydrogen, with its high energy output and eco-friendly combustion byproducts, stands out as a promising candidate. However, storing hydrogen, especially for vehicles, poses significant challenges due to the safety risks of high-pressure systems. Metal-organic frameworks (MOFs) offer a potential solution, enabling hydrogen storage at lower pressures through their porous structures. Yet, achieving optimal volumetric storage density while balancing material design and operational conditions remains a critical issue. This study presents an advanced predictive model for hydrogen storage (HS) capacity, leveraging a dataset of 14,544 synthesized MOF porous crystals and 10 key material properties. Machine learning (ML) techniques, including Linear Regression (LR), Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Regression (SVR), LSBoost, and their hybrid versions combined with Particle Swarm Optimization (PSO), were applied to model hydrogen uptake across 18 operational conditions (OCs). The models were rigorously evaluated using metrics such as R 2 , RMSE, MSE, and MAE, demonstrating exceptional accuracy in predicting HS performance. The results underscore the significant impact of thermodynamic factors (pressure and temperature), material density, pore size, and surface characteristics on hydrogen adsorption in MOFs. Hybrid ML models, particularly ANN-PSO and RF-PSO, outperformed traditional methods, delivering more precise and reliable predictions. Additionally, this study introduces a novel approach by averaging multiple training runs and testing various data percentages, ensuring the robustness and consistency of the models. These findings highlight the transformative potential of ML-driven models in optimizing hydrogen storage processes, offering a pathway to safer and more efficient hydrogen fuel cell systems. By integrating AI into hydrogen storage research, this work provides valuable insights and advances the development of sustainable energy technologies, addressing critical challenges in the transition to clean energy. Metal-organic framework ; Hydrogen storage ; Fuel cells ; Materials science ; Hydrogen ; Computer science ; Chemical engineering ; Chemistry ; Engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science ; Fuel Cells and Related Materials
Authors: Yuxinxin Chen | Pavlo O. Dral
Source: ChemRxiv (preprint, 2025-04-11)
Field: Materials Science — Materials Chemistry

AIQM2: Organic Reaction Simulations Beyond DFT Density functional theory (DFT) is the workhorse of reaction simulations but it either suffers from prohibitive cost or insufficient accuracy. In this work, we report AIQM2, the universal AI-enhanced QM Method 2, the first method that enables fast and accurate large-scale organic reaction simulations for practically relevant system sizes and time scales beyond what is possible with DFT. This breakthrough is based on the outstanding speed of AIQM2, orders of magnitude faster than common DFT, while its accuracy in reaction energies, transition state optimizations, and barrier heights is at least at the level of DFT and often approaches the gold-standard coupled cluster accuracy. AIQM2 can be used out of the box without any further retraining. Compared to pure machine learning potentials, AIQM2 possesses high transferability and robustness in simulations without catastrophic breakdowns. We showcase the superiority of AIQM2 compared to traditional DFT by performing an extensive reaction dynamics study overnight and revising the mechanism and product distribution reported in the previous investigation of the bifurcating pericyclic reaction. Computational chemistry ; Chemistry Machine Learning in Materials Science
Authors: Youfu Ma | Yu Jiang | Houlin Su | Jianjun Lei | Wenguang Xiao | Haijun Tian | Yawei Ma | Li Zhu | Ying Liang | Lisheng Wang | Mingqing Yuan | Xu Liu
Source: RSC Advances (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Interpretable machine learning models for predicting the antitumor effects of metal and metal oxide nanomaterials Understanding the toxic behavior of metal and metal oxide nanoparticles (M/MOx NPs) is essential for effective tumor diagnosis and treatment, yet generalizing findings remains challenging due to limited data, sampling variability, unreported complexities, low model accuracy, and a lack of interpretability. To address these issues and minimize extensive experimentation, we combined quantum chemistry calculations with published toxicity data to develop a machine learning model achieving over 90% accuracy in cross-validation. Utilizing 39 descriptors extracted from 152 articles, our dataset comprises 2765 instances covering various nanoparticle types, detection methods, and cell types. We enhanced data representation with the Jaccard similarity coefficient and employed Feature Importance and Shapley Additive Explanations (SHAP) to identify key factors influencing cytotoxicity, such as concentration, exposure time, zeta potential, diameter, COSMO area (CA), coating, testing methods, cell types, metal electronegativity, HOMO energy, and molecular weight. Additionally, we analyzed the interactions among these features and their influence on predictions, synthesized novel metal oxide nanoparticles, and assessed their physicochemical properties and anti-tumor toxicity. Cytotoxicity experiments with newly synthesized nanoparticles further validated the model's accuracy and generalizability, revealing hidden relationships and enabling predictions for previously unseen samples. This approach supports preliminary computer-aided screenings, significantly reducing the need for labor-intensive experimentation. Nanomaterials ; Metal ; Oxide ; Materials science ; Nanotechnology ; Artificial intelligence ; Computer science ; Machine learning ; Metallurgy Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Nanoparticle-Based Drug Delivery
Authors: Qian Li | Wang Xu | Wenjun Wang | Da Li | Haiqiang Wang | Jianmeng Chen | Dongzhi Chen
Source: Journal of Hazardous Materials Advances (article, 2025-06-01)
Field: Chemistry — Inorganic Chemistry

Machine learning-driven screening and performance prediction of metal-organic frameworks for photocatalytic removal of malodorous methyl mercaptan • Machine learning was firstly applied to predict MOF’s CH₃SH photocatalytic removal. • A dataset comprising over 100 samples and 13 structural descriptors was constructed. • Five key features of MOFs influencing CH₃SH photocatalytic removal were identified. • XGBoost and Random Forest models demonstrated superior predictive accuracy. • The synergy between metal centers and organic linkers greatly impact CH₃SH removal. Photocatalytic oxidation technology is applicable for the purification of low-concentration malodorous gases. Metal organic frameworks (MOFs) have been widely recognized as a kind of ideal photocatalytic star materials. However, the vast diversity and structural complexity of MOFs pose great challenges for the efficient identification of high-performance candidates via conventional experimental screening methods. In this study, a data-driven machine learning approach was firstly employed to accurately predict the CH₃SH photocatalytic removal of MOFs materials. A dataset comprising 180 samples and 13 structural descriptors with broad sample distribution was independently constructed by integrating literature-sourced structural features with experimentally measured performance data. Metal centers, organic linkers, post-synthetic modifications, specific surface area, and pore volume were identified as the 5 key factors successively influencing CH₃SH removal of MOFs. XGBoost and Random Forest demonstrated the highest predictive accuracy (R² = 0.97 and 0.96, respectively) and strong robustness, in comparison with Neural Network, k-Nearest Neighbors, and Linear Models, well-suited for capturing the nonlinear relationships between MOF features and CH₃SH removal performance. Partial dependence analysis indicated that the most influential features were the Cu metal center and the 1-H-pyrazole-4-carboxylic acid linker. Synergistic interaction analysis further revealed that the coupling of specific metal centers and organic linkers impact significantly on CH₃SH removal, and MOFs containing certain metal centers could achieve excellent activities. The work can provide a novel and effective strategy for the rational design and development of high-performance MOFs materials for CH₃SH abatement. Photocatalysis ; Metal-organic framework ; Metal ; Chemistry ; Materials science ; Chemical engineering ; Organic chemistry ; Catalysis ; Engineering ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques ; Gas Sensing Nanomaterials and Sensors
Authors: Arun S. Mujumdar | Amirhossein Barzigar | Seyed Mostafa Hosseinalipour | Amirhossein Edalatpour
Source: Drying Technology (article, 2025-09-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Critical review of drying processes for electrode materials: Bridging fundamentals and advanced manufacturing for energy storage devices Drying electrode materials is a critical yet complex step in the fabrication of lithium-ion and emerging energy storage devices, directly impacting their structural integrity, electrochemical performance, and manufacturing scalability. This review provides a comprehensive analysis of the physical and chemical fundamentals governing electrode drying, including heat and mass transfer, binder migration, and stress-induced defect formation. Various drying technologies such as convective, infrared, microwave, vacuum, freeze-drying, and hybrid approaches, are systematically evaluated in terms of drying mechanisms, energy efficiency, industrial readiness, and their influence on electrode microstructure. The review highlights how advanced modeling and simulation frameworks, ranging from analytical and continuum models to machine learning-driven digital twins, are being leveraged to optimize drying profiles and predict defect formation. The complex process–structure–performance relationships that link drying conditions to electrode porosity, adhesion, and electrochemical performance are critically assessed. Key industrial considerations, including energy consumption, solvent recovery, real-time process control, and environmental compliance, are discussed with emphasis on the challenges of scaling up to high-speed continuous production lines. Finally, emerging trends such as solvent-free electrode fabrication, smart drying controls, integration of green solvents, and circular economy strategies are explored. The article concludes by identifying critical research gaps and proposing a roadmap to guide future innovations in sustainable and intelligent electrode drying for next-generation battery technologies. Bridging (networking) ; Energy storage ; Electrode ; Electrochemical energy storage ; Energy density Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Electrical and Thermal Properties of Materials
Authors: Diptarka Hait | Jan D. Estrada Pabón | Martin Stoehr | Todd J. Martı́nez
Source: Journal of Chemical Theory and Computation (article, 2025-11-05)
Field: Materials Science — Materials Chemistry

Locating Ab Initio Transition States via Geodesic Construction on Machine-Learned Potential Energy Surfaces Efficient and reliable identification and optimization of transition state structures is a longstanding challenge in computational chemistry. Popular chain-of-states methods require hundreds if not thousands of <i>ab initio</i> calculations to generate initial guesses for local quasi-Newton optimizers, with persistent risk of collapse to an alternative stationary point on the potential energy surface (PES). Here, we show that high-quality guess structures for transition state optimization can be obtained by constructing the geodesic path between reactant and product structures on the PES generated by machine learning potentials (MLPs). We present an algorithm for optimization of such geodesic paths, as well as the associated codebase. We demonstrate effectiveness of this approach using the recent eSEN-sm-cons MLP. On average, the highest-energy point along these MLP geodesics requires 30% fewer quasi-Newton optimization steps to converge to the transition state compared to guesses from the fully <i>ab initio</i> freezing string method. Our approach therefore completely eliminates the need for <i>ab initio</i> calculations for the generation of transition state guesses and considerably speeds up subsequent structural optimization. Geodesic construction on ML PES thus promises to be a useful approach for efficient computational elucidation of complex chemical reaction networks. Geodesic ; Potential energy surface ; Ab initio ; Transition state ; State (computer science) ; Point (geometry) ; Potential energy ; String (physics) ; Optimization problem Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Matt Raymond | Paolo Elvati | Jacob C. Saldinger | Jonathan Lin | Xuetao Shi | Angela Violi
Source: Plasma Sources Science and Technology (article, 2025-02-27)
Field: Materials Science — Materials Chemistry

Machine learning models for Si nanoparticle growth in nonthermal plasma Abstract Nanoparticles formed in nonthermal plasmas (NTPs) can have unique properties and applications. However, modeling their growth in these environments presents significant challenges due to the non-equilibrium nature of NTPs, making them computationally expensive to describe. In this work, we address the challenges associated with accelerating the estimation of parameters needed for these models. Specifically, we explore how different machine learning models can be tailored to improve prediction outcomes. We apply these methods to reactive classical molecular dynamics data, which capture the processes associated with colliding silane fragments in NTPs. These reactions exemplify processes where qualitative trends are clear, but their quantification is challenging, hard to generalize, and requires time-consuming simulations. Our results demonstrate that good prediction performance can be achieved when appropriate loss functions are implemented and correct invariances are imposed. While the diversity of molecules used in the training set is critical for accurate prediction, our findings indicate that only a fraction (15%–25%) of the energy and temperature sampling is required to achieve high levels of accuracy. This suggests a substantial reduction in computational effort is possible for similar systems. Nanoparticle ; Plasma ; Nanotechnology ; Materials science ; Chemistry ; Engineering physics ; Physics ; Nuclear physics Silicon Nanostructures and Photoluminescence
Authors: Ravikumar Jayabal
Source: Journal of Energy Storage (article, 2025-09-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Waste derived graphene metal oxide composites for advanced supercapacitors: A review on modern energy storage Supercapacitor ; Electrochemical energy storage ; Graphene ; Energy storage ; Software deployment ; Raw material ; Characterization (materials science) ; Sustainable development ; Sustainable energy Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Nanomaterials for catalytic reactions
Authors: Zahra Karimzadeh
Source: Bioimpacts (article, 2025-11-22)
Field: Chemistry — Inorganic Chemistry

Recent advances and future prospects of metal organic frameworks (MOF)-based biosensors As a wide-ranging category of nanostructured materials, metal-organic-frameworks (MOFs) display distinctive characteristics, including uniformly ordered porosity, exceptional stability, and extensive tunability. These attributes enable the strategic design of MOFs in advanced biosensing platforms, including electrochemical and fluorescent biosensors. This editorial discusses the latest developments in MOF-based biosensors, emphasizing structural and surface functionalization strategies, enzyme immobilization, and signal amplification approaches that enhance analytical sensitivity and selectivity. Particular focus is placed on the MOF hybrid nanocomposites and micro/nano-sensing architectures designed to achieve precise control over activity-structure relationships. Moreover, current challenges in accomplishing scalable, biocompatible, and reproducible synthesis as well as in balancing stability with diffusion efficiency are examined. Finally, emerging trends combining computational modeling, advanced characterization, and machine-learning (ML)-guided design are highlighted as pathways toward next-generation analytical and point-of-care sensors with improved performance and broader practical applicability. Biosensor ; Metal-organic framework ; Surface modification ; Design elements and principles ; Sensitivity (control systems) Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors