jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Annabelle Canestraight | Anthony J. Dominic | Andrés Montoya−Castillo | Libor Veis | Vojtech Vlcek
Source: ArXiv.org (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

A Stochastic Cluster Expansion for Electronic Correlation in Large Systems Accurate many-body treatments of condensed-phase systems are challenging because correlated solvers such as full configuration interaction (FCI) and the density matrix renormalization group (DMRG) scale exponentially with system size. Downfolding and embedding approaches mitigate this cost but typically require prior selection of a correlated subspace, which can be difficult to determine in heterogeneous or extended systems. Here, we introduce a stochastic cluster expansion framework for efficiently recovering the total correlation energy of large systems with near-DMRG accuracy, without the need to select an active space a priori. By combining correlation contributions from randomly sampled environment orbitals with an exactly treated subspace of interest, the method reproduces total energies for non-reacting and reactive systems while drastically reducing computational cost. The approach also provides a quantitative diagnostic for molecule-solvent correlation, guiding principled embedding decisions. This framework enables systematically improvable many-body calculations in extended systems, opening the door to high-accuracy studies of chemical processes in condensed phase environments. Subspace topology ; Embedding ; Cluster expansion ; Cluster (spacecraft) ; Correlation ; Scale (ratio) ; Density matrix renormalization group ; Energy (signal processing) ; Atomic orbital ; Stochastic process Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Nora Beier | Thomas Gatter | Jakob L. Andersen | Peter F. Stadler
Source: Algorithms for Molecular Biology (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Computing double-pushout graph transformation rules and atom-to-atom maps from KEGG RCLASS data The laveau software makes it possible to extract local atom-to-atom maps from the RCLASSes of the KEGG database, covering a large set of enzyme-catalyzed reactions. The results are made available in the form of DPO rules for use in atom-level models of metabolic networks, filling a crucial gap in the available data. Graph ; Set (abstract data type) ; Software ; KEGG ; Graph rewriting ; Transformation (genetics) Machine Learning in Materials Science ; Graph Theory and Algorithms ; Advanced Graph Neural Networks
Authors: Qihang Wu | Zhiming Deng | Cong Zeng | Haoyuan Cai
Source: Nanomaterials (article, 2026-02-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Accurate Inverse Design of Broadband Solar Metamaterial Absorbers via Joint Forward–Inverse Deep Learning The design of broadband, high-efficiency solar absorbers remains challenging due to the complex and ill-posed inverse mapping from the target optical responses to the physical structures in inverse design optimization. To address this, we propose a joint forward-inverse deep learning framework that enables the rapid and accurate optimization of multilayer metamaterial absorbers. This method integrates an inverse network based on a Modified Swin Transformer with a Multilayer Perceptron forward proxy and performs end-to-end training in a consistency-driven cycle. This strategy reduces the one-to-many ambiguity in inverse design and improves the prediction accuracy, with normalized test mean squared errors of 7.2 × 10<sup>-5</sup> (inverse) and 6.8 × 10<sup>-5</sup> (forward). Using this framework, we optimized an absorber comprising W/SiO<sub>2</sub> hyperbolic metamaterial stacks and TiO<sub>2</sub>/SiO<sub>2</sub> anti-reflection coatings, achieving 97.4% average absorptivity across the 400-1750 nm solar spectrum, along with polarization insensitivity and robust wide-angle performance up to 60° incidence. The outdoor solar heating tests showed that the fabricated absorber reaches a peak temperature of 86.3 °C under natural sunlight, with an irradiance peak of about 850 W/m<sup>2</sup> at noon. This work shows that combining forward and reverse deep learning provides a powerful and scalable paradigm for accelerating the intelligent design of high-performance solar thermal metamaterials. Broadband ; Inverse ; Metamaterial ; Inverse problem ; Artificial neural network ; Multilayer perceptron ; Scalability ; Optimal design ; Deep learning ; Interfacing Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Solar-Powered Water Purification Methods
Authors: Anand Sharma | Chen Liu | Misaki Ozawa | Daniele Coslovich
Source: Physical Review Materials (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

Interpretability of linear regression models of glassy dynamics Data-driven models can accurately describe and predict the dynamical properties of glass-forming liquids from structural data. Accurate predictions, however, do not guarantee an understanding of the underlying physical phenomena and the key factors that control them. In this paper, we illustrate the merits and limitations of linear regression models of glassy dynamics built on high-dimensional structural descriptors. By analyzing data for a two-dimensional glass model, we show that several descriptors commonly used in glass-transition studies display multicollinearity, which hinders the interpretability of linear models. Ridge regression suppresses some of the shortcomings of multicollinearity, but its solutions are not concise enough to be physically interpretable. Only by using dimensional reduction techniques we do eventually obtain linear models that strike a balance between prediction accuracy and interpretability. Our analysis points to a key role of local packing and composition fluctuations in the glass model under study. Interpretability ; Linear regression ; Regression ; Dynamics (music) ; Regression analysis ; Key (lock) Material Dynamics and Properties ; Phase-change materials and chalcogenides ; Machine Learning in Materials Science
Authors: José de Jesús Cabrera Castro | Roberto Zitzumbo Guzmán | Luis Francisco Villalobos González
Source: Journal of Applied Research in Technology & Engineering (article, 2026-01-12)
Field: Materials Science — Polymers and Plastics

Optimization in recycled vulcanized rubber - blooming reduction using response surface methodology This study introduces a novel image processing–based vision system for the objective quantification of additive blooming on the surface of rubber blends containing ground tire rubber (GTR). The method applies RGB color channel decomposition to compare pre- and post-blooming states, using Euclidean distance between channels to provide a quantitative metric of blooming intensity over time. Alongside surface analysis, mechanical characterization (tensile strength, elongation at break, and Young’s modulus) was performed to correlate blooming intensity with mechanical performance. A central composite design (24 with 8 axial and 12 center points, including replicates) was used to evaluate the effects of formulation parameters on both blooming and key mechanical properties. Statistical analysis revealed that blooming is mainly influenced by the additive concentration (CCA1), the vulcanizing agent (S), and their interaction, with a good model fit (R² = 81.86%, R² adj. = 76.15%). Elongation was primarily governed by the accelerator (CCD2) (R² adj. = 91.52%), while tensile strength and Young’s modulus were significantly affected by (CCD2), (S), and their interactions (R² adj. = 72.93%, R² adj. = 72.35%, respectively). The optimized formulation, which avoids redundant additive loading already present in GTR, effectively mitigates blooming without compromising mechanical performance. This approach demonstrates strong potential for sustainable rubber recycling with improved mechanical reliability and reduced surface migration. Overall, this work provides two main contributions: (i) a robust, objective methodology for blooming quantification and (ii) data-driven insights for optimizing sustainable rubber formulations, supporting the development of Industry 4.0 oriented quality control strategies. Natural rubber ; Vulcanization ; Response surface methodology ; Ultimate tensile strength ; Central composite design ; Intensity (physics) ; Elongation ; Tear resistance ; RGB color model Polymer Nanocomposites and Properties ; Elasticity and Material Modeling ; Polymer crystallization and properties
Authors: Aldenora dos Santos Vasconcelos | Lorena Vieira Bentolila de Aguiar | Vítor Alves Pessoa | Iracimar Batista do Carmo | Larissa Batista do Nascimento Soares | Giovanna Lima-Silva | Daiane Barão Pereira | Patrick Cruz do Nascimento | Josilene Lima Serra Pereira | Ceci Sales-Campos | Larissa Ramos Chevreuil | Walter José Martínez-Burgos | Roberta Pozzan
Source: Coatings (article, 2026-04-09)
Field: Materials Science — Biomaterials

Toxicological and Environmental Risk Assessment of Biopolymeric Coatings for Horticultural Produce: A Comprehensive Review on Biosafety, Degradation, and Ecological Risks The increasing adoption of biopolymeric and nanostructured coatings for horticultural produce has emerged as a sustainable strategy to mitigate postharvest losses and extend shelf life. However, while their technological performance has been extensively documented, comprehensive and integrative assessments of biosafety, potential human health implications, and environmental risks profiles are still insufficiently explored. This review critically analyzes recent advances in polysaccharide, protein, and lipid-based coatings, including nanoenabled systems incorporating metallic nanoparticles and bioactive agents. The mechanisms underlying gas barrier properties, antimicrobial activity, and preservation efficacy are discussed alongside degradation pathways in composting, soil, and aquatic environments. Particular attention is given to nanoparticle release, migration potential, gastrointestinal fate, and toxicological endpoints such as oxidative stress, genotoxicity, endocrine disruption, and immunomodulation. Ecotoxicological evidence across trophic levels, from microorganisms and invertebrates to fish and amphibians, is examined, highlighting sublethal and mechanistic biomarkers relevant to environmental risk assessment. Regulatory frameworks from major agencies are also compared to contextualize current safety standards and limitations. Overall, although biopolymeric coatings represent promising alternatives to conventional plastics, their life-cycle impacts, transformation products, and nano-related uncertainties require comprehensive, multilevel risk evaluation to ensure truly sustainable and safe postharvest applications. Risk assessment ; Human health ; Sustainability ; Trophic level ; Risk management ; Environmental risk assessment Nanocomposite Films for Food Packaging ; Effects and risks of endocrine disrupting chemicals ; Microplastics and Plastic Pollution
Authors: Yingpeng Qi | Nianke Chen | Zhihui Zhou | Qing Xu | Yang Lv | Xiao Zou | Tao Jiang | Pengfei Zhu | Min Zhu | Dongxue Chen | Zhenrong Sun | Xianbin Li | Dao Xiang
Source: ArXiv.org (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Direct observation of ultrafast amorphous-amorphous transitions indicated by bond stretching and angle bending in phase-change material GeTe The intrinsic nature of glass states and glass transitions at the atomic scale remain a fundamental open question in condensed-matter physics and materials science. By combining femtosecond electron diffraction with time-dependent density-functional theory molecular dynamics simulations, we directly observe ultrafast amorphous-amorphous transitions in amorphous GeTe, manifested as rapid Ge-Te (Ge) bond stretching within 0.2 ps and subsequent angle bending of the Ge-Te (Ge)-Ge motif on a 0.5-2 ps timescale. Critically, the ultrafast bond stretching is accompanied by localized oscillation modes with the frequency of 3.10 THz, unambiguously signaling the local Peierls-like bonding structure and the flexibility of these polarized bonds. These ultrafast collective atomic motions provide a direct structural origin for the boson peak and pay the way for systematic optimization of relaxation and crystallization kinetics. Ultrashort pulse ; Femtosecond ; Bending ; Amorphous solid ; Oscillation (cell signaling) ; Relaxation (psychology) ; Ultrafast electron diffraction ; Atomic units Phase-change materials and chalcogenides ; Material Dynamics and Properties ; Chemical and Physical Properties of Materials
Authors: Baosheng Wang | Xiaoxue Ping | Yang Liu
Source: Journal of Food Science (article, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Hyperspectral System Coupled With a Global Spectral Feature Classification Network to Identify the Origin of Mung Bean Addressing the challenge of geographical origin fraud in mung beans is pivotal, as accurate traceability is fundamental to preserving their quality-specific attributes, protecting branded origins, and ensuring supply chain integrity. In this study, a proposed Global Spectral Feature Classification Network (GSFC-Net) coupled with a hyperspectral system is proposed to identify the quality differences of mung beans from different origins. Using the hyperspectral system, spectral information of mung beans from six different origins is collected and preprocessed. We propose the Global Spectral Feature Calculation Module (GSFCM), which integrates convolution, self-attention, and residual connection to extract full-band deep features that effectively represent the original spectral information. The GSFC-Net is designed to self-adaptively establish a nonlinear mapping between the original spectral information and the origin labels. The rationality of GSFC-NET is verified through structural optimization and ablation experiments. Compared with the state-of-the-art spectral information classification methods, GSFC-Net achieves outstanding classification performance and stability, with an accuracy of 98.10%, precision of 98.09%, recall of 98.55%, and an F<sub>1</sub>-score of 98.32%. In conclusion, this study presents an effective analytical method for identifying mung bean origin, providing a technical method to ensure product authenticity and support fair trade in the market. Hyperspectral imaging ; Residual ; Feature (linguistics) ; Pattern recognition (psychology) ; Precision and recall ; Traceability ; Mung bean Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Smart Agriculture and AI
Authors: Ahmed Selmi | A. Hela | M. Gassoumi | E. K. Hlil | Abdelaziz Bouazizi
Source: Research Square (preprint, 2026-02-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Study of Solid-State Synthesized Pr0.55Sr0.45Mn1-xCrxO3 Perovskites with x=0.0, 0.05, 0.1 and 0.15: Rietveld analysis, Magnetic and Magnetocaloric Properties for Magnetic Refrigeration Applications Magnetic refrigeration ; Rietveld refinement ; Orthorhombic crystal system ; Magnetization ; Curie temperature ; Crystallite ; Isothermal process ; Stoichiometry Magnetic and transport properties of perovskites and related materials ; Advanced Thermoelectric Materials and Devices ; Chemical and Physical Properties of Materials
Authors: Nobuya Yokogawa | Yasuaki Ito | Satoki Tsuji | Haruto Fujii | Kanta Suzuki | Koji Nakano | Victor Parque | Akihiko Kasagi
Source: Concurrency and Computation Practice and Experience (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

<scp>GPU</scp> ‐Accelerated One‐Electron Integral Computation for Quantum Chemistry ABSTRACT In Quantum chemical computation, numerical schemes such as the Hartree–Fock (HF) and density functional theory (DFT) are widely used to solve the Schrödinger equation numerically, to realize experiment‐free prediction and analysis of key molecular properties such as structure and energy. Computing one‐electron integrals, such as kinetic energy integrals and nuclear attraction integrals, is essential in both HF and DFT to characterize the molecular electronic states. However, as molecules of practical interest grow in size and angular momentum, computing one‐electron orbitals becomes computationally expensive in most cases. Although computing kinetic energy integrals on CPUs is straightforward, bottlenecks in CPU‐GPU data transfer have often been overlooked. In this study, we propose an efficient method to compute both the kinetic‐energy and nuclear‐attractive integrals on GPUs. First, we explicitly and symbolically expand recurrence relations based on the Obara–Saika and McMurchie–Davidson methods to eliminate redundant operations, thus improving computational efficiency. Second, we implemented a hybrid method that selects the best/fastest of both methods depending on the integration task. Third, we achieved further speedups by using CUDA streams to parallelize the execution of multiple kernels and efficiently utilize multiprocessor resources on the GPU. Computational experiments using NVIDIA A100 GPUs and Intel Xeon Gold 6338 CPU on relevant molecules of interest demonstrated the superiority of our one‐electron integral GPU implementations, achieving a speedup of 20.2 times over PySCF, and a speedup of 132.6 times over GPU4PySCF. Speedup ; Xeon ; Xeon Phi ; CUDA ; Quantum computer ; Computation ; Density functional theory ; Multiprocessing Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Matrix Theory and Algorithms
Authors: Bin Wei | Mengfan Chai | Chang Liu | Junyan Liu | Yongheng Li | Wenyu Zhang | Qing Wang | Erzhen Mu | Binbin Wang | Zhifang Zhou | Changpeng Lin | X Chen | Meihua Hu | Yunpeng Zheng | Yuan-Hua Lin
Source: npj Quantum Materials (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Phonon bunching induced strong four-phonon scattering for low lattice thermal conductivity in layered Na2CoSe2O Layered oxyselenides are of interest as promising mid- to high-temperature thermoelectric applications due to their tunable electrical properties and intrinsically low lattice thermal conductivity (κ). Understanding microscopic phonon mechanisms driving low-κ in these materials is essential for rational design. Here, we report a layer-differentiated phonon transport in Na2CoSe2O via solving the Wigner transport equation based on first-principles calculations, where CoSe6 and Na6O octahedra dominate acoustic and low-energy optical phonons, respectively, together inducing acoustic-optical bunching (a typical feature in homo-layered structures such as bilayer graphene and MoS2). In addition, Na6O generates high-energy flat optical phonons, in contrast to oxide-typical dispersive modes. This phonon dispersion feature, constrained by three-phonon scattering channels, necessitates interpretation via four-phonon processes, which simultaneously enhance wavelike coherent tunneling effects. Consequently, by considering anharmonic phonon renormalization, strong scattering of heat-carrying bunched phonons reduces κ by ~28%, with ~13% compensatory contribution from phonon coherence, yielding a low lattice thermal conductivity of 1.52 W m−1 K−1 at 300 K in Na2CoSe2O. This work provides new insights into the specific vibrational mechanisms, phonon bunching induced strong four-phonon scattering, highlights the critical role of distinct structural layers in governing thermal transport, and enriches the fundamental thermal transport mechanism in layered oxyselenides. Phonon ; Thermal conductivity ; Anharmonicity ; Scattering ; Phonon scattering ; Thermoelectric effect ; Quantum tunnelling ; Graphene Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; 2D Materials and Applications
Authors: William Marsh Baldwin | Ilyes Batatia | Martin Vondrák | Johannes T. Margraf | Gábor Csányi
Source: ArXiv.org (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Design Space of Self--Consistent Electrostatic Machine Learning Interatomic Potentials Machine learning interatomic potentials (MLIPs) have become widely used tools in atomistic simulations. For much of the history of this field, the most commonly employed architectures were based on short-ranged atomic energy contributions, and the assumption of locality still persists in many modern foundation models. While this approach has enabled efficient and accurate modelling for many use cases, it poses intrinsic limitations for systems where long-range electrostatics, charge transfer, or induced polarization play a central role. A growing body of work has proposed extensions that incorporate electrostatic effects, ranging from locally predicted atomic charges to self-consistent models. While these models have demonstrated success for specific examples, their underlying assumptions, and fundamental limitations are not yet well understood. In this work, we present a framework for treating electrostatics in MLIPs by viewing existing models as coarse-grained approximations to density functional theory (DFT). This perspective makes explicit the approximations involved, clarifies the physical meaning of the learned quantities, and reveals connections and equivalences between several previously proposed models. Using this formalism, we identify key design choices that define a broader design space of self-consistent electrostatic MLIPs. We implement salient points in this space using the MACE architecture and a shared representation of the charge density, enabling controlled comparisons between different approaches. Finally, we evaluate these models on two instructive test cases: metal-water interfaces, which probe the contrasting electrostatic response of conducting and insulating systems, and charged vacancies in silicon dioxide. Our results highlight the limitations of existing approaches and demonstrate how more expressive self-consistent models are needed to resolve failures. Electrostatics ; Locality ; Representation (politics) ; Salient ; Space (punctuation) ; Computation ; Polarization (electrochemistry) ; Meaning (existential) Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Electrocatalysts for Energy Conversion
Authors: Xi Lin | Wenyan Xu | Chuncai Xu | Fubiao Ye | Lijuan Fan | Bin Qiu | Yan Xu
Source: Journal of Analytical Science & Technology (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

A signal “off–on” voltammetric aptasensor of interleukin-18 based on bimetallic iron and nickel organic framework nanozyme By synthesizing a bimetallic iron and nickel organic framework (FeNi-MOF) hybrid as the nanozyme, herein we developed an innovative voltammetric ‘off–on’ aptasensor platform of interleukin-18 (IL-18) for the first-time. This nanozyme catalyzes the conversion of 1,2-diaminobenzene to diaminophenazinc (DAP), an electroactive substance. However, upon binding to the aptamer (Apt) sensing of IL-18, its enzyme-like activity is inhibited as the active sites are obscured by the Apt, leading to the disappearance of DAP current (signal ‘off’). Interestingly, in the presence of IL-18, the activity is restored because specific binding between the Apt and IL-18 displaces the Apt from the FeNi-MOF surface, resulting in an increase in DAP current (signal ‘on’). After optimizing key conditions, this ‘off-on’ nanozyme-based electrochemical aptasensor demonstrates excellent detection performance for IL-18 and provides a novel, simple, and reliable strategy for detecting various biomarkers by simply altering the relative Apt. Bimetallic strip ; Aptamer ; Nickel ; Electrochemistry ; Electrode ; Metal-organic framework Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Inflammasome and immune disorders