jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ehab Al-Emam | Marta Cremonesi | Natalia Ortega Saez | Hilde Soenen | Koen Janssens | Geert Van der Snickt
Source: Gels (article, 2026-01-22)
Field: Materials Science — Biomaterials

Exploring the Potential of Polyvinyl Alcohol–Borax-Based Gels for the Conservation of Historical Silk Fabrics by Comparative Cleaning Tests on Simplified Model Systems Cleaning historical silk textiles is a particularly sensitive operation that requires precise control to prevent mechanical or chemical damage. In this study, we investigate using flexible PVA-borax-based gels to remove soot from silk, i.e., polyvinyl alcohol-borax (PVA-B) gels and polyvinyl alcohol-borax-agarose double network gels (PVA-B/AG DN) loaded with different cleaning agents-namely, 30% ethanol and 1% Ecosurf EH-6-in addition to plain gels loaded with water. These gel formulations were tested on simplified model systems (SMS) and were applied using two methods: placing and tamping. The cleaning results were compared with a traditional contact-cleaning approach; micro-vacuuming followed by sponging. Visual inspection, 3D opto-digital microscopy, colorimetry, and machine-learning-assisted (ML) soot counting were exploited for the assessment of cleaning efficacy. Rheological characterization provided information about the flexibility and handling properties of the different gel formulations. Among the tested systems, the DN gel containing only water, applied by tamping, was easy to handle and demonstrated the highest soot-removal effectiveness without leaving residues, as confirmed by micro-Fourier Transform Infrared (micro-FTIR) analysis. Scanning electron microscope (SEM) micrographs proved the structural integrity of the treated silk fibers. Overall, this work allows us to conclude that PVA-borax-based gels offer an effective, adaptable, and low-risk cleaning strategy for historical silk fabrics. SILK ; Polyvinyl alcohol ; Scanning electron microscope ; Rheology ; Polymer ; Characterization (materials science) Silk-based biomaterials and applications ; Cultural Heritage Materials Analysis ; Collagen: Extraction and Characterization
Authors: Krunal Patel | Vishal Sorathiya | Amar Y. Jaffar | Ayman Alharbi | Fahd M. Aldosari | Abdullah G. Alharbi
Source: Materials Chemistry and Physics (article, 2026-02-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design and optimization of MXene–perovskite solar absorbers using multi-geometry metasurface resonators with AI-based modeling Resonator ; Absorption (acoustics) ; Stack (abstract data type) ; Solar cell ; Broadband ; Metamaterial absorber ; Coupling (piping) ; Plasmon ; Metamaterial Metamaterials and Metasurfaces Applications ; Electromagnetic wave absorption materials ; Thermal Radiation and Cooling Technologies
Authors: Franz Görlich | Julija Zavadlav
Source: Journal of Chemical Information and Modeling (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

Mapping Still Matters: Coarse-Graining with Machine Learning Potentials Coarse-grained (CG) modeling enables molecular simulations to reach time and length scales inaccessible to fully atomistic methods. For classical CG models, the choice of mapping, that is, how atoms are grouped into CG sites, is a major determinant of accuracy and transferability. At the same time, the emergence of machine learning potentials (MLPs) offers new opportunities to build CG models that can in principle learn the true potential of the mean force for any mapping. In this work, we systematically investigate how the choice of mapping influences the representations learned by equivariant MLPs by studying liquid hexane, amino acids, and polyalanine. We find that when the length scales of bonded and nonbonded interactions overlap, unphysical bond permutations can occur. We also demonstrate that correctly encoding species and maintaining stereochemistry are crucial, as neglecting either introduces unphysical symmetries. Our findings provide practical guidance for selecting CG mappings compatible with modern architectures and guide the development of transferable CG models. Encoding (memory) ; Equivariant map ; Development (topology) ; Feature (linguistics) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Material Dynamics and Properties
Authors: Shifu Wang | Changxing Zhang | Biao Xia | Meiqian Wang | Zhiyi Tang | Wei Xu
Source: Materials (article, 2026-01-21)
Field: Materials Science — Ceramics and Composites

Research Frontiers in Numerical Simulation and Mechanical Modeling of Ceramic Matrix Composites: Bibliometric Analysis and Hotspot Trends from 2000 to 2025 Ceramic matrix composites (CMCs) exhibit excellent high-temperature strength, oxidation resistance, and fracture toughness, making them superior to traditional metals and single-phase ceramics in extreme environments such as aerospace, nuclear energy equipment, and high-temperature protection systems. The mechanical properties of CMCs directly influence the reliability and service life of structures; thus, accurately predicting their mechanical response and service behavior has become a core issue in current research. However, the multi-phase heterogeneity of CMCs leads to highly complex stress distribution and deformation behavior in traditional mechanical property testing, resulting in significant uncertainty in the measurement of key mechanical parameters such as strength and modulus. Additionally, the high manufacturing cost and limited experimental data further constrain material design and performance evaluation based on experimental data. Therefore, the development of effective numerical simulation and mechanical modeling methods is crucial. This paper provides an overview of the research hotspots and future directions in the field of CMCs numerical simulation and mechanical modeling through bibliometric analysis using the CiteSpace software. The analysis reveals that China, the United States, and France are the leading research contributors in this field, with 422, 157, and 71 publications and 6170, 3796, and 2268 citations, respectively. At the institutional level, Nanjing University of Aeronautics and Astronautics (166 publications; 1700 citations), Northwestern Polytechnical University (72; 1282), and the Centre National de la Recherche Scientifique (CNRS) (49; 1657) lead in publication volume and/or citation influence. Current research hotspots focus on finite element modeling, continuum damage mechanics, multiscale modeling, and simulations of high-temperature service behavior. In recent years, emerging research frontiers such as interface debonding mechanism modeling, acoustic emission monitoring and damage correlation, multiphysics coupling simulations, and machine learning-driven predictive modeling reflect the shift in CMCs research, from traditional experimental mechanics and analytical methods to intelligent and predictive modeling. Hotspot (geology) ; Ceramic ; Computer simulation ; Finite element method ; Representative elementary volume ; Astronautics ; Reliability (semiconductor) ; Ceramic matrix composite Advanced ceramic materials synthesis ; Aluminum Alloys Composites Properties ; MXene and MAX Phase Materials
Authors: Veera Mohana Rao Kakita | D. Flemming Hansen
Source: Journal of the American Chemical Society (article, 2026-03-02)
Field: Chemistry — Spectroscopy

Deep Learning Assisted Proton Pure Shift NMR Spectroscopy Nuclear magnetic resonance spectroscopy (NMR) plays a key role for the analysis of a plethora of molecules, including natural products and drug-like organic molecules. For such cases, <sup>1</sup>H NMR spectra have proven imperative because of their high sensitivity and atomic resolution. However, these spectra are complicated by overlapped complex multiplet patterns. Here we show a deep-learning approach, which transforms spin-echo modulated <sup>1</sup>H NMR spectra into highly sensitive and high-resolution singlet NMR spectra, that is, virtual homonuclear decoupled pure shift spectra. The approach was evaluated on experimental NMR spectra of complex organic compounds, where it outperforms current methods. The method also predicts uncertainties of the transformation and therefore allows for quantifications, which is a key strength of NMR. We believe that our approach will provide significant advantages when characterizing low-sensitivity samples and systems with exchangeable protons, where signals are not observed in traditional pure-shift spectra and substantial overlaps hamper analysis from conventional spectra. Homonuclear molecule ; NMR spectra database ; Spectral line ; Nuclear magnetic resonance spectroscopy ; Multiplet ; Two-dimensional nuclear magnetic resonance spectroscopy ; Spectroscopy ; Singlet state ; Carbon-13 NMR satellite Advanced NMR Techniques and Applications ; NMR spectroscopy and applications ; Advanced MRI Techniques and Applications
Authors: Wei-Lin Wu | Meng Lu | Shi-Lin Zhu
Source: Physical Review Letters (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

DeepQuark: A Deep-Neural-Network Approach to Multiquark Bound States For the first time, we implement the deep-neural-network-based variational Monte Carlo approach for the multiquark bound states, whose complexity surpasses that of electron or nucleon systems due to strong SU(3) color interactions. We design a novel and high-efficiency architecture, DeepQuark, to address the unique challenges in multiquark systems such as stronger correlations, extra discrete quantum numbers, and intractable confinement interaction. Our method demonstrates competitive performance with state-of-the-art approaches, including diffusion Monte Carlo and Gaussian expansion method, in the nucleon, doubly heavy tetraquark, and fully heavy tetraquark systems. Notably, it outperforms existing calculations for pentaquarks, exemplified by the triply heavy pentaquark. For the nucleon, we successfully incorporate three-body flux-tube confinement interactions without additional computational costs. In tetraquark systems, we consistently describe hadronic molecule T_{cc} and compact tetraquark T_{bb} with an unbiased form of wave function ansatz. In the pentaquark sector, we obtain weakly bound D[over ¯]^{*}Ξ_{cc}^{*} molecule P_{ccc[over ¯]}(5715) with S=5/2 and its bottom partner P_{bbb[over ¯]}(15569). They can be viewed as the analogs of the molecular T_{cc}. We recommend experimental search of P_{ccc[over ¯]}(5715) in the D-wave J/ψΛ_{c} channel. DeepQuark holds great promise for extension to larger multiquark systems, overcoming the computational barriers in conventional methods. It also serves as a powerful framework for exploring confining mechanism beyond two-body interactions in multiquark states, which may offer valuable insights into nonperturbative QCD and general many-body physics. Tetraquark ; Hadron ; Bound state ; Quantum Monte Carlo ; Pentaquark ; Monte Carlo method ; Gaussian ; Wave function Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture
Authors: Lijia Shang | Shuhong Zhou | Xinran Zhang | Junjie Chen | Sisi Wen | Di Liu | Ming Mu | Bing Zhao | Wei Song
Source: Biosensors and Bioelectronics (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

Multiplexed discrimination and ultrasensitive determination of pesticides using a Cu-O-Mo nanozyme tri-channel array driven by machine learning Interpretability ; Pesticide ; Multiplex ; Analyte ; Biosensor ; Pesticide residue Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Ying Chung | Katsumi Yoshida
Source: Journal of the American Ceramic Society (article, 2026-02-27)
Field: Materials Science — Ceramics and Composites

Predicting the Thermal Conductivity of Porous SiC Ceramics by Machine Learning With Novel Encoding ABSTRACT A novel encoding strategy, using molecular weight (MW) to represent the type of sintering additives, was proposed to perform predictions of thermal conductivity of porous silicon carbide ceramics by machine learning in the consideration of including the scientific meaning. For comparison, a conventional strategy, labeling those parameters with random numbers, was used. The predictive model built with MW encoding produces similar prediction accuracy with the one built with label encoding when the test data is randomly split. Permutation importance ranked total porosity as the dominant factor when using both encoding strategies. In addition, process‐only model and two‐stage model were proposed to support process design. With the presence of porosity in the explanatory variables, the two‐stage model showed improvements in error rates. The principal component analysis results further show that porosity reduced the coverage of the first two components, indicating an additional, partly independent source of variation. Leave‐one‐study‐out validation across seven literature groups of the primary model reveals large aggregate errors were driven by a single ultralow and narrow‐range cohort. Errors markedly improved either by excluding this cohort or by using a log‐scale target while retaining it. This framework combines novel encoding along with realistic cross‐study generalization for process‐aware modeling. Porosity ; Encoding (memory) ; Permutation (music) ; Silicon carbide ; Principal component analysis ; Resampling ; Thermal conductivity ; Ceramic Advanced ceramic materials synthesis ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Yiheng Zhou | Dun Wu | Yuanchao Gan | Xuemei Li | Xiaolong Nan | Guoqiang Luo | Yuying Yu | Jianbo Hu
Source: International Journal of Impact Engineering (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

Dynamic yield strength of Ta under shock-ramp compression measured by a machine-learning enhanced self-consistent Lagrangian analysis method Bauschinger effect ; Stress space ; Plasticity ; Robustness (evolution) ; Softening ; Yield (engineering) ; Strength of materials ; Dynamic loading ; Shock (circulatory) ; Compression (physics) High-Velocity Impact and Material Behavior ; High-pressure geophysics and materials ; Energetic Materials and Combustion
Authors: Sandeep Prabhu | C. R. Rathish | K Vaishnavi | Raman Dhivya | U. Arun Kumar
Source: Journal of Optics (article, 2026-02-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

An optical high-sensitivity terahertz metasurface SPR sensor with a MoS₂–graphene hybrid structure and machine learning for refractive-index-based brain tumor biomarker sensing Terahertz radiation ; Dielectric ; Sensitivity (control systems) ; Refractive index ; Surface plasmon resonance ; Surface plasmon ; Plasmon Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Terahertz technology and applications
Authors: Yongbo Shi | Yu Bao | Shixun Cao | Ye Su | Keke Song | Lu Xiao | Ping Qian
Source: Journal of Applied Physics (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Investigation of the transport behavior of BP and BAs using the Boltzmann transport equation and machine learning methods It is important to predict the theoretical upper limit of lattice thermal conductivity and carrier mobility in the face of high uncertainty in experimental measurements of boron phosphide (BP) and boron arsenides (BAs). Using the Boltzmann transport equation (BTE) approach, we quantify the contributions of three-phonon and four-phonon scattering to thermal conductivity. The thermal conductivity of BP is determined by three-phonon scattering and is independent of four-phonon scattering. Due to the large bandgap between acoustic and optical phonons in BAs, three-phonon scattering is prevented, while four-phonon scattering is permitted. At room temperature, the predicted thermal conductivity with three- and four-phonon scattering is 505 W/m K (BP) and 1506 W/m K (BAs). After considering isotope scattering, the thermal conductivity is reduced to 468 and 1124 W/m K, respectively. Neuroevolution potential is trained using machine learning methods. Subsequently, the extrapolated lattice thermal conductivity through nonequilibrium molecular dynamics simulation is 304 W/m K (BP) and 1008 W/m K (BAs), respectively. The Heyd-Scuseria-Ernzerhof (HSE) hybrid functional and spin-orbit coupling (SOC) are employed to correct bandgap and band edge shape. Utilizing the Wannier function interpolation methodology, the ambipolar mobility predicted by the iterative BTE is μa=1.61×103cm2/Vs (BP) and μa=1.79×103cm2/Vs (BAs) at room temperature, which is in good agreement with the experimental measurements. By analyzing the characteristics of branch-dependent phonon scattering, the dominant scattering of charge carriers is attributed to longitudinal acoustic phonons. Boltzmann equation ; Thermal conductivity ; Scattering ; Phonon ; Phonon scattering ; Lattice Boltzmann methods ; Band gap ; Convection–diffusion equation Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermography and Photoacoustic Techniques
Authors: Mikołaj Jan Gawkowski | Mingjia Li | Benjamin Xu Shi | Venkat Kapil
Source: Machine Learning Science and Technology (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

The good, the bad, and the ugly of atomistic learning for ‘clusters-to-bulk’ generalization Abstract Training machine learning interatomic potentials (MLIPs) on total energies of molecular clusters using differential or transfer learning is becoming a popular route to extend the accuracy of correlated wave-function theory to condensed phases. A key challenge, however, lies in validation, as reference observables in finite-temperature ensembles are not available at the reference level. Here, we construct synthetic reference data from pretrained MLIPs and evaluate the generalizability of cluster-trained models on ice-Ih, considering scenarios where both energies and forces and where only energies are available for training. We study the accuracy and data-efficiency of differential, single-fidelity transfer, and multi-fidelity transfer learning against ground-truth thermodynamic observables. We find that transferring accuracy from clusters to bulk requires regularization, which is best achieved through multi-fidelity transfer learning when training on both energies and forces. By contrast, training only on energies introduces artefacts: stable trajectories and low energy errors conceal large force errors, leading to inaccurate microscopic observables. More broadly, we show that accurate reproduction of microscopic structure correlates strongly with low force errors but only weakly with energy errors, whereas global properties such as energies and densities correlate with low energy errors. This highlights the need to incorporate forces during training or to apply careful validation before production. Our results highlight the promise and pitfalls of cluster-trained MLIPs for condensed phases and provide guidelines for developing—and critically, validating—robust and data-efficient MLIPs. Generalizability theory ; Generalization ; Observable ; Construct (python library) ; Energy (signal processing) ; Transfer of learning ; Reference data ; Differential (mechanical device) Machine Learning in Materials Science ; Quantum many-body systems ; Block Copolymer Self-Assembly
Authors: Kai Zhang | Yongyan Xu
Source: Synthetic Metals (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

Machine learning-accelerated DFT study of Al-doped graphene and h-BN: Contrasting sensing mechanisms for organophosphate (DMMP) detection Graphene ; Physisorption ; Density functional theory ; Adsorption ; Dimethyl methylphosphonate ; Band gap ; Fermi level ; Electronic structure Graphene research and applications ; Boron and Carbon Nanomaterials Research ; Pesticide Exposure and Toxicity
Authors: Mohammed Ali Dheyab | Wesam Abdullah | Sara Abdulwahab | Sadeen Alsarayreh | Mothana Hussein Hussein Tarawneh | Mutaz Mohammad Alsardi | Mansour A. Alanazi | Abdul Aziz Azlan
Source: RSC Mechanochemistry (article, 2026-01-01)
Field: Materials Science — Biomaterials

Force-driven architectonics of inorganic nanomaterials: pathways to smart and functional interfaces This review presents force-driven architectonics in mechanochemistry, demonstrating how mechanical energy programs inorganic nanomaterials into controlled architectures and smart interfaces for sustainable catalysis, energy, biomedicine, and sensing. Nanomaterials ; Sustainable energy ; Interface (matter) ; Smart material Calcium Carbonate Crystallization and Inhibition ; Machine Learning in Materials Science ; Pickering emulsions and particle stabilization
Authors: Lingyu Kong | Jaeheon Shim | Guoxiang Hu | Victor Fung
Source: npj Computational Materials (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

Scalable foundation interatomic potentials via message-passing pruning and graph partitioning Atomistic foundation models (AFMs) have great promise as accurate interatomic potentials, and have enabled data-efficient molecular dynamics simulations with near quantum mechanical accuracy. However, AFMs remain markedly slower at inference and are far more memory-intensive than conventional interatomic potentials, due to the need to capture a wide range of chemical and structural motifs in pre-training datasets requiring deep, parameter-rich model architectures. These deficiencies currently limit the practical use of AFMs in molecular dynamics (MD) simulations at extended temporal and spatial scales. To address this problem, we propose a general workflow for accelerating and scaling AFMs containing message-passing architectures. We find that removing low-contribution message-passing layers from AFM backbones serves as an effective pruning method, significantly reducing the parameter count while preserving the accuracy and data-efficiency of AFMs. Once pruned, these models become more accessible for large scale simulations via a graph-partitioned, GPU-distributed strategy, which we implement and demonstrate within the AFM fine-tuning platform MatterTune. We show that this approach supports million-atom simulations on both single and multiple GPUs, and enables task-specific large-scale simulations at nanosecond timescales with AFM-level accuracy. Scalability ; Scaling ; Molecular dynamics ; Interatomic potential ; Workflow ; Pruning ; Inference ; Limit (mathematics) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Quantum many-body systems