jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vinh‐Binh Truong | Analía Fernández Herrero | Anna Andrle | Victor Soltwisch | Philipp Hönicke
Source: Advanced Materials Interfaces (article, 2025-04-29)
Field: Materials Science — Materials Chemistry

Machine Learning‐Accelerated Reconstruction of Periodic Nanostructures with X‐ray Fluorescence Spectroscopy Methods Abstract With advancements in the semiconductor industry, the complexity of three‐dimensional (3D) nanostructures becomes higher with continuously decreasing feature sizes. In order to monitor the processing steps, it is crucial to accurately determine the critical dimensions and composition of these nanostructures. Early detection of production malfunctions is crucial for reasonable production yields, as even minor imperfections can heavily impede device performance. Grazing X‐ray fluorescence spectroscopy methods are non‐destructive and element‐specific methods with high sensitivity for nanostructured surfaces. They enable the quantification and localization of elements within the sample, with information depths sufficient also for buried features. Such measurements can be utilized to reconstruct the shape and size of nanostructure features, with simulated data based on finite element method (FEM) Maxwell calculations. However, the computation time of FEM calculations poses a challenge (especially in the X‐ray energy regime), prolonging the reconstruction process and making it impractical for the characterization of multiple samples. To address this issue, a neural network‐based approach is adopted to replace the time‐consuming FEM‐based forward calculations. In this study, the feasibility of utilizing neural networks for nanostructure reconstruction is demonstrated on various nanostructures. The discrimination limit for different model parameters is assessed and compared against conventional FEM calculation results. Materials science ; Nanostructure ; Spectroscopy ; Fluorescence ; X-ray fluorescence ; Fluorescence spectroscopy ; Nanotechnology ; X-ray spectroscopy ; Optics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Mahmoud M.A. Nassar | Khalid I. Alzebdeh | Mahmoud Alsafy | Mohammad Jawaid
Source: Results in Engineering (article, 2025-11-21)
Field: Materials Science — Polymers and Plastics

Optimization and predictive modelling of chemically crosslinked recycled PE/PP blends for enhanced mechanical properties • Chemically crosslinked recycled PP/PE blends achieved superior performance. • Optimal blend: 25% PE, 3% DCP, and 10% AcAm via Taguchi–GRA optimization. • ANN accurately predicted mechanical and physical blend properties. • Integrated experimental-statistical approach enhances sustainable polymer design. Recycling plastic has become a prevalent practice for reducing waste in the environment, but producing high-performance recycled polymer blends is still a major technical problem. In order to improve the mechanical performance of recycled High-Density Polyethylene (PE) and Polypropylene (PP) blends, this work offers an integrated experimental and predictive modeling method. In order to enhance the interfacial adhesion of polymer blends through chemical crosslinking, maleic anhydride (MAH) and Acrylamide (AcAm) were employed as coupling agents. Optimization of the of PE content, Dicumyl Peroxide (DCP) as an imitator, and AcAm was accomplished using the Taguchi experimental design, while the Analytic Hierarchy Process (AHP) and Grey Relational Analysis (GRA) were employed to assign response weights and identify the optimal formulation. The blend containing 25 wt.% PE, 3 wt.% DCP, and 10 wt.% AcAm demonstrated the best overall performance, achieving 60% higher tensile strength and 40% higher flexural strength compared to the untreated blend. It is found that the addition of coupling agents also increased thermal stability, but viscosity changes were negligible. Additionally, a strong correlation with experimental results was found when mechanical and physical properties were accurately predicted by an Artificial Neural Network (ANN) model (R = 0.952). A viable and sustainable framework for creating high-performance recycled polymer blends appropriate for a variety of industrial applications is offered by the combination of statistical optimization and ANN prediction. Polypropylene ; Ultimate tensile strength ; Polymer ; Polymer blend ; Maleic anhydride ; Izod impact strength test ; Taguchi methods ; Flexural strength ; Response surface methodology Polymer crystallization and properties ; Polymer Nanocomposites and Properties ; Polymer composites and self-healing
Authors: Xiaohui Guo | Zhen Yang | Yuwei Wang | Zhiguo Qu | Yun Lu | Y. K. Lou | Meiling Chen | Xiaoliang Ren
Source: Materials Chemistry and Physics (article, 2025-08-13)
Field: Materials Science — Materials Chemistry

Lasso regression with molecular descriptors predicts natural product adsorption onto graphene oxide-derived adsorbents Graphene ; Adsorption ; Oxide ; Lasso (programming language) ; Natural product ; Materials science ; Regression ; Product (mathematics) ; Nanotechnology ; Chemistry ; Mathematics ; Computer science ; Statistics ; Organic chemistry ; Metallurgy Machine Learning in Materials Science ; Adsorption and biosorption for pollutant removal ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Binod Barai | Vikash Kumar | Pratik Das | Subhasish Sarkar | Piyali Basak | Buddhadeb Oraon | Tapendu Mandal
Source: Biomaterials Advances (article, 2025-08-22)
Field: Materials Science — Surfaces, Coatings and Films

A hybrid multi-objective optimization framework for designing superhydrophobic coatings on magnesium alloys for biomedical applications Materials science ; Magnesium ; Nanotechnology ; Metallurgy ; Computer science Surface Modification and Superhydrophobicity ; Tribology and Lubrication Engineering ; Aerogels and thermal insulation
Authors: Lin Tan | Yue Wang | Hailiang Zhang | Jinyu Sun | Qiong Yang | Xiao Yang | Zhimin Zhang | Hongmei Lü
Source: Analytical Chemistry (article, 2025-12-12)
Field: Chemistry — Analytical Chemistry

DeepMIR: A Hybrid Convolutional Neural Network-Transformer Framework for Accurate Identification of Target Components from Mid-Infrared Spectra of Mixtures Accurate identification of components from mid-infrared (MIR) spectra of mixtures remains a significant challenge in analytical chemistry due to severe spectral overlap and instrumental variability. To address this, we present DeepMIR, a deep learning framework designed for targeted component identification in complex mixtures using user-defined spectral libraries. DeepMIR uniquely integrates a convolutional neural network for extracting high-fidelity local spectral features and a transformer encoder to capture global, long-range dependencies across the spectrum. This hybrid architecture enables robust identification of target components even when reference and mixture spectra are acquired using different techniques, such as transmission and attenuated total reflectance. The model is trained and validated on a data set of over 67,000 synthetically augmented spectral pairs, achieving an accuracy of 99.5% on the test set, with statistical significance confirmed by <i>t</i> test analysis (<i>p</i> < 0.05). Extensive real-world validation demonstrates high accuracy across diverse scenarios, 94.8% for complex liquid solvents, 93.9% for solid pigment mixtures relevant to cultural heritage, and 99.1% for commercial blended textiles. Dedicated limit-of-detection studies establish reliable detection thresholds of 20% v/v in liquids and 10% w/w in solids. To ensure accessibility, DeepMIR has been deployed as an open-access web server, providing a powerful and practical tool for the scientific community that significantly outperforms traditional library search methods in both accuracy and reliability. Convolutional neural network ; Pattern recognition (psychology) ; Identification (biology) ; Artificial neural network ; Encoder ; Component (thermodynamics) ; Test set ; Spectral signature Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Water Quality Monitoring and Analysis
Authors: Qiao Zhang | S Q Yan | Jing Wang | Dingguo Xu
Source: Crystal Research and Technology (article, 2025-07-21)
Field: Materials Science — Materials Chemistry

Advances and Challenges in Computational Simulation of Calcium Phosphate Nucleation Abstract Calcium phosphate‐based bioceramic materials have shown tremendous potential in biomedical applications, particularly in bone repair and replacement, due to their excellent biocompatibility and osteoconductivity. However, since nucleation events occur at the atomic scale, traditional experimental methods face significant limitations in exploring the nucleation and growth mechanisms of calcium phosphate. The introduction of multi‐scale computational simulation techniques can provide comprehensive understanding the nucleation mechanism and properties of calcium phosphate. This review systematically summarizes recent progress in the computational simulation of calcium phosphate nucleation and analyzes the current challenges in the field. Finally, this study further proposes that the integration of advanced computational methods, machine learning techniques, and experimental validation will enable a comprehensive understanding of the nucleation process of calcium phosphate, bridging the gap between microscopic mechanisms and macroscopic properties. Nucleation ; Calcium ; Phosphate ; Materials science ; Chemistry ; Computer science ; Metallurgy ; Biochemistry ; Organic chemistry Crystallization and Solubility Studies ; Calcium Carbonate Crystallization and Inhibition ; Enzyme Structure and Function
Authors: Chunguang Shen | Wangzhong Mu | Chenchong Wang | Jun Hui Yu | Wei Xu | Peter Hedström
Source: Journal of Materials Research and Technology (article, 2025-11-01)
Field: Materials Science — Metals and Alloys

Integrated machine learning approach for predicting low-temperature embrittlement in duplex stainless steels via ferrite hardening The low-temperature embrittlement of duplex stainless steels significantly restricts their broader application in critical environments. Despite extensive research, a reliable predictive model for this phenomenon remains unavailable. In this study, we present a data-driven framework that integrates two machine learning (ML) models to predict the evolution of ferrite hardness and steel toughness during thermal ageing. We assembled two experimental datasets from both the open literature and in-house experiments. The ferrite hardness dataset includes chemical composition and ageing conditions, while the steel toughness dataset incorporates all features from the ferrite dataset, along with ferrite grain size and ferrite fraction. A systematic selection of input features and ML algorithms was conducted to optimize model performance. The integrated framework is based on random forest regression, where ML1 predicts changes in ferrite hardness, and ML2 estimates variations in steel toughness using the predicted ferrite hardness from ML1 as an input feature. This linkage reflects the metallurgical understanding that ferrite hardness serves as a key indicator of low-temperature embrittlement in duplex stainless steel. The trained models achieved high predictive accuracy, with R 2 values exceeding 0.97 for both ferrite hardness and steel toughness across multiple stainless steel grades. Furthermore, the models demonstrated strong generalizability when applied to unseen alloys and new ageing conditions. To assess model interpretability, feature importance analysis was performed to evaluate the influence of individual input variables, and the results were interpreted through the lens of physical metallurgy, offering insights into the underlying mechanisms of embrittlement. • Integrated machine learning model low-temperature embrittlement in stainless steel • Achieves R 2 > 0.97 for ferrite hardness and steel toughness predictions • Framework generalizes well to unseen alloys and ageing conditions • Feature importance reveals key drivers of low-temperature embrittlement Ferrite (magnet) ; Embrittlement ; Toughness ; Duplex (building) ; Hardening (computing) ; Grain size Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Microstructure and Mechanical Properties of Steels
Authors: A.A. Shah | Po Kin Leung | Wei Xing
Source: npj Computational Materials (article, 2025-03-02)
Field: Materials Science — Materials Chemistry

Rapid high-fidelity quantum simulations using multi-step nonlinear autoregression and graph embeddings Abstract The design and high-throughput screening of materials using machine-learning assisted quantum-mechanical simulations typically requires the existence of a very large data set, often generated from simulations at a high level of theory or fidelity. A single simulation at high fidelity can take on the order of days for a complex molecule. Thus, although machine learning surrogate simulations seem promising at first glance, generation of the training data can defeat the original purpose. For this reason, the use of machine learning to screen or design materials remains elusive for many important applications. In this paper we introduce a new multi-fidelity approach based on a dual graph embedding to extract features that are placed inside a nonlinear multi-step autoregressive model. Experiments on five benchmark problems, with 14 different quantities and 27 different levels of theory, demonstrate the generalizability and high accuracy of the approach. It typically requires a few 10s to a few 1000’s of high-fidelity training points, which is several orders of magnitude lower than direct ML methods, and can be up to two orders of magnitude lower than other multi-fidelity methods. Furthermore, we develop a new benchmark data set for 860 benzoquinone molecules with up to 14 atoms, containing energy, HOMO, LUMO and dipole moment values at four levels of theory, up to coupled cluster with singles and doubles. Nonlinear system ; Fidelity ; Autoregressive model ; Computer science ; Graph ; High fidelity ; Quantum ; Statistical physics ; Theoretical computer science ; Mathematics ; Econometrics ; Physics ; Telecommunications ; Quantum mechanics Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Spectroscopy and Quantum Chemical Studies
Authors: Patricia R. S. Wenceslau | Antônio S. N. Aguiar | Vitor S. Duarte | Leonardo R. Almeida | Chris H. J. Franco | Gilberto L. B. Aquino | Jaqueline E. Queiroz | Adriano O. Maldaner | Hamilton B. Napolitano
Source: ACS Omega (article, 2025-07-31)
Field: Chemistry — Organic Chemistry

Comprehensive Analysis of Pyrazoline Analogs: Exploring Their Antioxidant Potential as Biofuel Additives Global dependence on fossil fuels raises environmental and energy concerns, encouraging the search for renewable sources, such as biodiesel. However, their limited oxidative stability compromises their commercial viability. This study investigates two pyrazoline analogues (CNF and CNO) as potential antioxidant additives for biodiesel using a combined molecular modeling and machine learning approach. Supramolecular analysis revealed that C-H···O and C-H···π interactions influence molecular stability. Chemical reactivity descriptors indicated that the methoxy group (-OCH<sub>3</sub>) in CNO enhances the electron-withdrawing effect, making it more susceptible to oxidation. Kinetic evaluation predicted that both compounds have intermediate oxidation rates when exposed to hydroxyl radicals, with a performance comparable to widely used commercial antioxidants. These results suggest that pyrazoline analogues are promising candidates to mitigate the oxidative degradation of biodiesel, contributing to the greater efficiency and sustainability of biofuels. Antioxidant ; Biofuel ; Pyrazoline ; Biochemical engineering ; Chemistry ; Biotechnology ; Engineering ; Organic chemistry ; Biology Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Tongwei Han | Suncheng Zhang | Xiaoyan Zhang | Fabrizio Scarpa
Source: Diamond and Related Materials (article, 2025-10-21)
Field: Materials Science — Materials Chemistry

Data-driven inverse design of graphene Kirigami with negative Poisson's ratio using machine learning and genetic algorithms Graphene ; Inverse ; Perforation ; Support vector machine ; Genetic algorithm ; Boosting (machine learning) ; Curse of dimensionality ; Feature (linguistics) Graphene research and applications ; Topology Optimization in Engineering ; Additive Manufacturing and 3D Printing Technologies
Authors: Zhixing Xu | Weiguo Xie | Daiqian Xie
Source: ArXiv.org (preprint, 2025-09-18)
Field: Materials Science — Materials Chemistry

Spectral/Spatial Tensor Atomic Cluster Expansion with Universal Embeddings in Cartesian Space Equivariant atomistic machine learning models have largely been built on spherical-tensor representations, where explicit angular-momentum coupling introduces substantial complexity and systematic extensions beyond energies and forces remain challenging, often requires problem-specific architectural choices. Here we introduce the Tensor Atomic Cluster Expansion (TACE), which unifies scalar and tensorial modeling in Cartesian and space by decomposing local environments into irreducible Cartesian tensors (ICT) constructing a controlled many-body hierarchy with atomic cluster expansion (ACE). In addition to performing ACE in the frequency domain, we propose an efficient Clebsch-Gordan-free alternative in the spatial domain. TACE provides universal invariant (e.g., fidelity tags and charges) and equivariant (e.g., external electric fields and non-collinear magnetic moments) embeddings and predicted tensorial observables are handled on equal footing and enabling explicit control at inference. We demonstrate the accuracy, stability, and efficiency across finite molecules and extended materials, including in-domain and out-of-domain benchmarks, spectra, Hessian, external-field responses, charged systems, and multi-fidelity/head training. We further show its robustness on nonequilibrium/reactive datasets and controlled scaling when extending to large foundation model datasets. Tensor (intrinsic definition) ; Cartesian coordinate system ; Embedding ; Scalar (mathematics) ; Space (punctuation) ; Field (mathematics) ; Hierarchy ; Vector space Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Quantum many-body systems
Authors: Weijie Li | Yajie Li | Maxim Avdeev | Siqi Shi
Source: Chinese Physics Letters (article, 2025-10-24)
Field: Materials Science — Materials Chemistry

Dimensionality-Decomposition Based Deep Learning Approach for Non-Equilibrium Electric Double Layer Modeling Abstract The electric double layer (EDL), formed by charge adsorption at the electrolyte–electrode interface, constitutes the microenvironment governing electrochemical reactions. However, due to scale mismatch between the EDL thickness and electrode topography, solving the two-dimensional (2D) nonhomogeneous Poisson–Nernst–Planck (N-PNP) equations remains computationally intractable. This limitation hinders understanding of fundamental phenomena such as curvature-driven instabilities in 2D EDL. Here, we propose a dimensionality-decomposition strategy embedding a fully connected neural network (FCNN) to solve 2D N-PNP equations, in which the FCNN is trained on key electrochemical parameters by reducing the electrostatic boundary into multiple equivalent 1D representations. Through a representative case of LiPF 6 reduction on lithium metal half-cell, nucleus size is unexpectedly found to have an important influence on dendrite morphology and tip kinetics. This work paves the way for bridging nanoscale and macroscale simulations with expandability to 2D situations of other 1D EDL models. Bridging (networking) ; Nanoscopic scale ; Electrode ; Embedding ; Deep learning ; Multiscale modeling ; Electrochemistry ; Artificial neural network ; Resistive touchscreen ; Dendrite (mathematics) Machine Learning in Materials Science
Authors: Wenxuan Li | Xiaonan Zhang | Hao Guo | Lingchuan Li | Lifeng Ding | Qingyuan Yang
Source: Advanced Science (article, 2025-09-19)
Field: Chemistry — Inorganic Chemistry

Inverse Design of Metal‐Organic Frameworks for CH <sub>4</sub> /N <sub>2</sub> Separation Enabled by Coupled Machine Learning and Genetic Algorithms Metal-organic frameworks (MOFs) have emerged as promising candidates for gas separation, yet the vastness of their structural design space renders experimental screening prohibitively time- and resource-intensive. Recent advances in machine learning (ML) technology offer powerful alternatives for accelerating MOF discovery through data-driven prediction. In this work, a high-accuracy ML model with a Tangent Adaptive Genetic Algorithm (TAGA) is integrated to enable inverse design of MOFs for CH<sub>4</sub>/N<sub>2</sub> separation. The ML model, trained on structural features including topology, metal/organic secondary building units, and functional groups, is embedded within the TAGA framework to efficiently navigate the high-dimensional chemical space. Analysis of the evolutionary trajectory reveals that MOFs featuring the fsc topology and ligands such as pyrene, anthracene, and naphthalene consistently exhibit superior CH<sub>4</sub>/N<sub>2</sub> selectivity. Based on these high-performance genotypes, a series of MOF structures are constructed, among which the top-performing candidate achieves an IAST selectivity of 15.92 and a CH<sub>4</sub> uptake of 2.47 mmol g<sup>-1</sup>. This study highlights a paradigm shift from trial-and-error screening toward goal-directed materials design, offering a generalizable pathway for developing next-generation separation materials. Inverse ; Genetic algorithm ; Tangent space ; Chemical space ; Space (punctuation) ; Series (stratigraphy) Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Covalent Organic Framework Applications
Authors: Gang Meng | Yaolong Zhang | Bin Jiang | Hua Guo
Source: Annual Review of Physical Chemistry (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

Dynamics of Surface Processes: Impact of Adiabatic and Nonadiabatic Energy Dissipation Dynamics of molecular interactions with solid surfaces, such as scattering, adsorption/desorption, diffusion, and reaction, are affected by energy dissipation at surfaces. Recent progress in experimental studies of surface dynamics has stimulated intense interest in theoretical investigation of microscopic mechanisms and pathways of energy transfer. This review summarizes recent developments in modeling such processes, emphasizing new understandings of electronically adiabatic and nonadiabatic energy dissipation mechanisms and dynamics in representative systems, using various theoretical methods. In particular, machine learning has been leveraged to represent high-dimensional adiabatic potential energy surfaces, electronic friction tensors, and effective multielectron diabatic Hamiltonians. When integrated with mixed quantum-classical dynamics methods, such as molecular dynamics with electronic friction and independent electron surface hopping, these first-principles-based simulations provided unprecedented insights into the roles played by adiabatic and nonadiabatic energy dissipation channels in surface dynamics and in-depth interpretation of experimental observations. Diabatic ; Adiabatic process ; Dissipation ; Molecular dynamics ; Dynamics (music) ; Surface (topology) ; Potential energy Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies