jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Marcelo Antunes | David Arencón
Source: Polymers (article, 2026-02-28)
Field: Materials Science — Materials Chemistry

Recent Developments in the Mechanical Behavior of Polymer-Based Composites Polymer-based systems have been shown to have a particular combination of characteristics that make them desirable in technological sectors, such as lightness, insulating properties, and easy molding during processing, as well as mechanical versatility, which is greatly due to their molecular microstructure. Nevertheless, they still present limitations in mechanical performance and use at moderate/high temperatures, considerably restricting their range of applications. Thus, great efforts have been directed towards developing strategies intended to enhance said characteristics and predict their complex mechanical behavior, with the main goal of adapting their properties to the end-use application. The present review considers the most recent developments, focusing on the research published in 2025 and early 2026, and future challenges in the mechanical behavior of polymer-based materials, being structured according to material considerations, more specifically the development of advanced (nano)composites based on high-performance matrices and functional nanoparticles, as well as bio-based polymer (nano)composites obtained from renewable sources and multifunctional smart and meta-materials for monitoring and long-term use; the development of new processing methods, focusing on advanced additive manufacturing; and the use of artificial intelligence and machine learning. All in all, the final objective is generating knowledge that will enable the preparation of components with tailor-made mechanical characteristics and functional properties, covering material design and processing. Molding (decorative) ; Range (aeronautics) ; Materials processing ; Smart material ; Mechanical strength ; Lead (geology) Machine Learning in Materials Science ; Polymer composites and self-healing ; Engineering and Materials Science Studies
Authors: Fanli Zhou | Hao Chen | Pengxiang Xu | Kai Yang | Zongrui Pei | Xianglin Liu
Source: arXiv (Cornell University) (article, 2026-03-22)
Field: Materials Science — Materials Chemistry

Towards Computational Microscope of Chemical Order-Disorder via ML-Accelerated Monte Carlo Simulation Tailoring the performance of next-generation high entropy materials requires a deep understanding of the competition between entropy-driven random solid solution and enthalpy-driven chemical ordering. Investigating such order and disorder complexity demands atomistic simulations that achieve high accuracy, efficiency, and generalizability across vast spatial, temporal, and especially chemical scales. While machine learning (ML) interatomic potentials have transformed molecular dynamics, they remain limited in capturing diffusion-driven chemical evolution over long timescales. The recently introduced SMC-X method brings exciting opportunities. Realizing its full potential requires a comprehensive study, which is the focus of this work. To assess model performance, we systematically benchmark invariant and equivariant architectures using a density functional theory dataset of more than 10,000 configurations spanning seven elements: Fe, Co, Ni, Al, Ti, Ta, and V. To understand the roles of pairwise and higher-order interactions, we decouple their contributions across chemical space using an explainable machine learning approach. We also examine the impact of lattice relaxation by comparing models trained on datasets with and without structural relaxation. Our results clarify how to choose ML surrogate models for Monte Carlo simulations, bridge the gap between theory and experiment, and lay a foundation for establishing ML-accelerated Monte Carlo as a computational microscope for chemical complexity. Monte Carlo method ; Randomness ; Pairwise comparison ; Monte Carlo molecular modeling ; Kinetic Monte Carlo ; Suite ; Chemical space ; Computational simulation ; Generalizability theory Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Zening Yang | Jin Yu | Zhengyu Sun | Chengchen Li | Qionghua Zhou | Weiwei Sun
Source: The Journal of Physical Chemistry Letters (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

Boosting Crystalline Property Prediction through Dynamical Feature Updating and Wavelet-Denoised Features: A New Deep Learning Model Graph neural networks (GNNs) inherently excel at representing atomistic material, therefore enabling predictions of properties with high accuracy with embedded descriptors. Approaching to accurate prediction of a variety of physical properties of diverse materials, particularly those with multicomponents, remains challenging due to the complexity of many-body atomic interactions and the reliance on handcrafted symmetry descriptors. Here, we introduce the Wavelet Atomic Neighborhood Network (WANN), a novel framework that implicitly captures many-body interactions through an iterative subembedding module for updating atomic features, thereby eliminating the integration of the predefined feature engineering. A wavelet-based regression component is built up through multiscale feature analysis. WANN demonstrates superior accuracy, achieving small mean absolute error (MAE) in predicting a variety of physical and chemical quantities, including thermodynamical, dielectric, piezoelectric, magnetic, and thermoelectric properties. For instance, it could significantly reduce MAE maximum by 61.64% in comparison to Matformer for shear moduli and by 90.84% in comparison to ALIGNN for the exfoliation energy. This scalable and preprocessing-light deep-learning framework provides an efficient toolkit for facilitating high-throughput screening of emergent materials, like high-entropy alloys and ceramics. Scalability ; Feature (linguistics) ; Artificial neural network ; Boosting (machine learning) ; Property (philosophy) ; Deep learning ; Variety (cybernetics) ; Scaling Machine Learning in Materials Science ; High Entropy Alloys Studies ; Inorganic Chemistry and Materials
Authors: Yue Wang | Miaomiao Zhang | Meng Xiao | Yanhui Chen | Yuezhou Zhang
Source: AI & Materials (article, 2026-03-23)
Field: Materials Science — Materials Chemistry

Application of artificial intelligence in biofuel cell catalyst design and system optimization With the global energy transition underway, biofuel cells are gaining increasing attention as a clean energy technology capable of utilizing renewable biomass and organic waste for power generation. Despite the significant advantages of biofuel cells in terms of environmental friendliness and low carbon emissions, challenges related to catalyst efficiency, system complexity, and long-term stability still limit their large-scale application. This review examines the growing role of artificial intelligence (AI) in advancing biofuel cell technology, focusing on its advancements in catalyst screening, system modeling, and optimization. By leveraging AI technologies- including machine learning and deep learning-significant improvements have been demonstrated in material design, system parameter optimization, and performance prediction outcomes. Particular emphasis is placed on AI applications within enzymatic fuel cells and microbial fuel cells, analyzing progress in catalyst material discovery, system simulation, and operational control. By summarizing current breakthroughs and challenges, this analysis aims to offer theoretical support and technical guidance for future development of intelligent, AI-driven green energy systems. Biofuel ; Renewable energy ; Fuel cells ; Biomass (ecology) ; Applications of artificial intelligence ; Electricity generation ; Electric power system Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Xinwei Dong | Fuxin Zheng | Tao Zhang | Xianming Xie | JunYi Deng | Nantao Hu | G Z Wang
Source: ACS Sensors (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Intelligent Visible-Near Infrared Micro-Hyperspectral Sensing System for Rapid Chemical Mapping of Microplastics and Metal Oxides Rapid, non-destructive, and accurate chemical mapping of microscopic materials is critical for advancing chemical analysis and related industries. However, conventional techniques like scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS) and Raman microscopy are often limited by low throughput and high costs. To overcome these barriers, we report the development of an intelligent sensing platform that integrates low-cost visible and near-infrared (Vis-NIR) micro-hyperspectral imaging with a custom-designed deep learning architecture. The core of our innovation is a patch-based, spatial-spectral strategy implemented through a custom-designed multi-attention 3D convolutional neural network with residual connections. This approach effectively compensates for the low chemical specificity of broad Vis-NIR spectra by learning subtle, high-dimensional joint features. The platform's power is demonstrated by its ability to classify a challenging set of eight chemical species, including spectrally indistinct microplastics (polystyrene and poly(methyl methacrylate)) and various metal oxides, with 97.35% accuracy. The high-fidelity chemical maps of complex, multi-component agglomerates were rigorously validated against SEM-EDS, confirming the model's robustness. Critically, our non-destructive optical method achieves this with a throughput several orders of magnitude higher than SEM-EDS. This work provides a powerful and versatile tool for the high-throughput characterization of diverse materials, including metal oxide catalysts, environmental contaminants like microplastics, and other complex heterogeneous systems, with broad applications across scientific and industrial domains. Chemical imaging ; Microplastics ; Raman spectroscopy ; Microscopy ; Near-infrared spectroscopy ; Oxide ; Chemical species ; Spectroscopy ; Convolutional neural network Machine Learning in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications ; Microplastics and Plastic Pollution
Authors: C W Zhang | Beibei Xu | Jing Wan | Jian Liang | Yongshen Wu | Cuixia Wang
Source: Langmuir (article, 2026-01-24)
Field: Materials Science — Materials Chemistry

Low-Frequency In-Plane Phonons Dominate Thermal Transport in Montmorillonite: Machine-Learning Molecular Dynamics Insights Montmorillonite (MMT), a foundational constituent of permafrost systems, plays a critical role in determining the thermophysical properties of frozen soils through its thermal transport behavior. The current understanding of these properties remains limited because of the experimental challenges associated with cryogenic conditions and the inherent limitations of classical molecular dynamics potentials in accurately capturing low-temperature phonon interactions. In this study, we integrate the neuroevolution potential (NEP) framework with active learning to develop a high-precision, data-driven model for evaluating the thermal conductivity of MMT. Nonequilibrium molecular dynamics, homogeneous nonequilibrium molecular dynamics, and spectral heat current are employed as complementary computational approaches to systematically simulate and rigorously compare the thermal conductivity of MMT. The results show that the thermal conductivity of MMT reaches 4.28 W m<sup>-1</sup> K<sup>-1</sup> at 300 K, with thermal transport predominantly governed by in-plane phonons (>85% contribution) and low-frequency phonon modes (<20 THz). Furthermore, temperature-dependent analyses indicate that increasing temperature enhances phonon-phonon scattering, thereby reducing thermal conductivity. In contrast, out-of-plane phonons exhibit markedly lower temperature sensitivity with changes in thermal conductivity not exceeding 0.15 W m<sup>-1</sup> K<sup>-1</sup>. Complementary phonon density of states, phonon lifetime, and group velocity analyses confirm that thermal transport is dominated by dispersive, long-lived, low-frequency in-plane phonons, whereas high-frequency vibrations are strongly localized and contribute minimally. This work validates the accuracy of the NEP model for complex minerals and elucidates the thermal transport mechanisms of MMT, thereby providing a theoretical foundation for permafrost thermal stability and cold-region engineering applications. Phonon ; Thermal conductivity ; Non-equilibrium thermodynamics ; Molecular dynamics ; Permafrost ; Thermal conduction ; Thermal Thermal properties of materials ; Climate change and permafrost ; Geothermal Energy Systems and Applications
Authors: Qing Li | Haikuan Dong | Penghua Ying | Zheyong Fan
Source: International Journal of Heat and Mass Transfer (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

Anisotropic and isotropic elasticity and thermal transport in monolayer C24 networks from machine-learning molecular dynamics Isotropy ; Anisotropy ; Elasticity (physics) ; Monolayer ; Molecular dynamics ; Thermal Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Thermal properties of materials
Authors: Emily Xi Tan | Cam Tu Tran | Gia Minh Kieu | In Yee Phang | Xing Yi Ling
Source: ACS Applied Materials & Interfaces (article, 2026-03-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Interfacial Chemistry in SERS: Mechanisms, Controls, and Materials Surface-enhanced Raman scattering (SERS) is a powerful spectroscopic tool for molecular analysis, in which molecular-surface interactions, adsorption energetics, and electronic coupling govern activation, selectivity, and sensitivity. Despite this, SERS interpretation and design have been dominated by electromagnetic field enhancement, leaving chemical enhancement and interfacial understanding limited and fragmented, with surface chemistry often treated as a black box in which ligands and coatings are applied empirically with little mechanistic insight. In this perspective, we reemphasize SERS as an interfacial chemical phenomenon, viewing SERS substrates as chemically active interfaces with tunable energetic, geometric, and electronic states. We introduce a SERS interfacial chemical phase diagram that organizes substrate-analyte interactions across these dimensions, enabling mechanistic understanding, rational control, and predictive design. Furthermore, we evaluate emerging strategies for actively tuning interfacial states through chemical functionalization, porous and compartmentalized materials, adaptive architectures, and hybrid systems, and highlight opportunities to integrate computation, standardized interfacial metadata, and interpretable machine learning to advance SERS into a chemically designable and predictive platform for targeted analysis and operando studies. Raman scattering ; Adsorption ; Coupling (piping) ; Phase diagram ; Rational design ; Raman spectroscopy ; Chemical stability ; Chemical species Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Nanocluster Synthesis and Applications
Authors: Saeid Ghomi | Mahmoud Moradi
Source: Biophysical Journal (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

BPS2026 – Deep dynamical network analysis (DDNA): A hybrid deep learning framework for molecular dynamics simulations Deep learning ; Molecular dynamics ; Deep neural networks ; Artificial neural network ; Dynamics (music) ; Network model Machine Learning in Materials Science ; Protein Structure and Dynamics ; Model Reduction and Neural Networks
Authors: Habtamu Fekadu Etefa | Francis B. Dejene
Source: International Journal of Molecular Sciences (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

Making the Bridge Between Experiment and Theory in Metal Oxides for Renewable Energy: Based on TiO2, ZnO, and BiVO4 Metal oxides such as TiO<sub>2</sub>, ZnO, and BiVO<sub>4</sub> have emerged as pivotal materials for renewable energy technologies owing to their versatile electronic, optical, and catalytic properties. This review highlights the importance of bridging experimental investigations with Density Functional Theory (DFT) to deepen the understanding of structure-property relationships in these systems. Experimental approaches provide critical insights into synthesis strategies, performance evaluation, and sustainability, whereas DFT offers predictive power at the atomic scale by elucidating electronic structures, reaction mechanisms, and defect dynamics. The synergy of these methods enables the rational design of advanced materials for photocatalysis, solar cells, and energy storage applications. Looking ahead, research opportunities lie in the development of doped and heterostructured metal oxides, the integration of machine learning for accelerated material discovery, and the implementation of in situ/operando studies that capture time-resolved phenomena. By making the bridge between experiment and theory, significant progress can be achieved toward sustainable and efficient energy solutions. Bridging (networking) ; Renewable energy ; Density functional theory ; Bridge (graph theory) ; Sustainable energy ; Energy storage ; Solar energy ; Energy density ; Rational design Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; TiO2 Photocatalysis and Solar Cells
Authors: Gen Zu | Ning Mao | Claudia Felser | Yang Zhang
Source: Nano Letters (article, 2026-04-06)
Field: Materials Science — Materials Chemistry

Interpretation of Crystal Energy Landscapes with Kolmogorov–Arnold Networks Characterizing crystalline energy landscapes is essential to predicting thermodynamic stability, electronic structure, and functional behavior. While machine learning (ML) enables rapid property predictions, the "black-box" nature of most models limits their utility for generating new scientific insights. Here, we introduce Kolmogorov-Arnold Networks (KANs) as an interpretable framework to bridge this gap. Unlike conventional neural networks with fixed activation functions, KANs employ learnable functions that reveal underlying physical relationships. We developed the Element-Weighted KAN, a composition-only model that achieves state-of-the-art accuracy in predicting formation energy, band gap, and work function across large-scale data sets. Crucially, without any explicit physical constraints, KANs uncover interpretable chemical trends aligned with the periodic table and quantum mechanical principles through embedding analysis, correlation studies, and principal component analysis. These results demonstrate that KANs provide a powerful framework with high predictive performance and scientific interpretability, establishing a new paradigm for transparent, chemistry-based materials informatics. Embedding ; Interpretation (philosophy) ; Bridge (graph theory) ; Artificial neural network ; Principal component analysis ; Crystal structure prediction ; Function (biology) ; Energy (signal processing) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Inorganic Chemistry and Materials
Authors: Paulo R. Pires | Thalis H.B. da Silva | Kun Gao | Kaja H. Hiorth | Tiago F.T. Cerqueira | Théo Cavignac | Pierre-Paul De Breuck | Hai‐Chen Wang | Đorđe Dangić | Yue-Wen Fang | Antonio Sanna | Wenwen Cui | Ion Errea | Päivi Törmä | Miguel A.L. Marques
Source: Computational Materials Today (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

Machine learning driven exploration of hydride superconductors at ambient pressure Hydrogen-rich materials are among the most promising candidates for achieving high-temperature superconductivity due to their light atomic mass and strong phonon-mediated Cooper pairing. While many high-temperature superconducting hydrides have been reported experimentally, their practical applicability remains limited due to the required extreme conditions, motivating the search for stable or metastable superconducting phases at ambient pressure. Here, we combine ab initio electron–phonon coupling calculations with machine learning methods to explore over two million hydride structures, identifying more than 600 compounds with predicted critical temperatures ( T c ) above 20 K. This dataset reveals a large chemical and structural diversity among potential superconductors, including perovskites, kagome lattices, and compounds with isolated hydrogen octahedra, among others. Despite this diversity, high- T c materials consistently exhibit substantial hydrogen contributions to the electronic density of states at the Fermi level. Symbolic regression analysis quantitatively confirms this correlation. Thermodynamic analysis shows that all identified superconductors lie above the convex hull of stability, with energies typically exceeding 100 meV/atom above the hull. This implies that experimental synthesis can only be obtained through non-equilibrium approaches, including high-pressure or high-temperature methods followed by quenching, or by thin-film deposition techniques. Moreover, as many promising candidates resemble degenerate semiconductors, a possible synthesis route may involve controlled doping strategies of parent semiconducting compounds. This work substantially expands the known landscape of hydride superconductors and establishes design principles to guide future experimental realization. Ambient pressure ; Superconductivity ; High pressure ; Pressure sensor Machine Learning in Materials Science ; Inorganic Fluorides and Related Compounds ; Hydrogen Storage and Materials
Authors: Gen Zu | Ning Mao | Claudia Felser | Yang Zhang
Source: arXiv (Cornell University) (preprint, 2026-04-06)
Field: Materials Science — Materials Chemistry

Interpretation of Crystal Energy Landscapes with Kolmogorov-Arnold Networks Characterizing crystalline energy landscapes is essential to predicting thermodynamic stability, electronic structure, and functional behavior. While machine learning (ML) enables rapid property predictions, the "black-box" nature of most models limits their utility for generating new scientific insights. Here, we introduce Kolmogorov-Arnold Networks (KANs) as an interpretable framework to bridge this gap. Unlike conventional neural networks with fixed activation functions, KANs employ learnable functions that reveal underlying physical relationships. We developed the Element-Weighted KAN, a composition-only model that achieves state-of-the-art accuracy in predicting formation energy, band gap, and work function across large-scale datasets. Crucially, without any explicit physical constraints, KANs uncover interpretable chemical trends aligned with the periodic table and quantum mechanical principles through embedding analysis, correlation studies, and principal component analysis. These results demonstrate that KANs provide a powerful framework with high predictive performance and scientific interpretability, establishing a new paradigm for transparent, chemistry-based materials informatics. Embedding ; Interpretation (philosophy) ; Bridge (graph theory) ; Artificial neural network ; Function (biology) ; Principal component analysis ; Energy (signal processing) ; Property (philosophy) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Inorganic Chemistry and Materials
Authors: Hao Wu | Peiheng Lai | Chenhang Zheng | Han Cheng | Yang Zhang | Andy Pau Chen | T. Yang | Minggang Zeng | Bing Li | Ming Yang
Source: Electrochimica Acta (article, 2026-03-23)
Field: Materials Science — Materials Chemistry

Chemistry-wise transfer active learning accelerated investigation of high-entropy alloys towards CO2 reduction Ternary operation ; Unary operation ; Transfer of learning ; Intermetallic ; Embedding ; Active learning (machine learning) ; Dimensionality reduction ; Process (computing) ; Reduction (mathematics) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts