jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ian Slagle | Faisal M. Alamgir | Victor Fung
Source: Open MIND (preprint, 2026-02-24)
Field: Materials Science — Materials Chemistry

Determining Atomic Structure from Spectroscopy via an Active Learning Framework Determining atomic structure from spectroscopic data is central to materials science but remains restricted to a limited set of techniques and material classes, largely due to the computational cost and complexity of structural refinement. Here we introduce ActiveStructOpt, a general framework that integrates graph neural network surrogate models with active learning to efficiently determine candidate structures that reproduce target spectra with minimal computational expenditure. Benchmarking with X-ray pair distribution function data, and with the more computationally demanding simulations of X-ray absorption near-edge spectra (XANES) and extended X-ray absorption fine structure (EXAFS), demonstrate that ActiveStructOpt reliably determines structures that match closely in spectra across diverse materials classes. Under equivalent computational budgets, ActiveStructOpt outperforms existing structure determination methods. By enabling data-efficient, multi-objective structural refinement across a broad range of computable spectroscopic techniques, ActiveStructOpt provides a flexible and extensible approach to atomic structure determination in complex materials. Artificial neural network ; Spectral line ; Benchmarking ; Set (abstract data type) ; Function (biology) ; Range (aeronautics) ; Spectroscopy ; Computational complexity theory Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Electrocatalysts for Energy Conversion
Authors: Yinan Wang | Arpita Varadwaj | Teruyasu Mizoguchi | Masato Kotsugi
Source: arXiv (Cornell University) (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

Decoding Dopant-Induced Electronic Modulation in Graphene via Region-Resolved Machine Learning of XANES Revealing how heteroatom doping alters the local electronic structure of graphene is crucial for understanding and controlling its functional properties. In this study, we combine density functional theory (DFT) and machine learning (ML) to interpret how boron (B) and nitrogen (N) dopants influence the local electronic environments of graphene. A dataset of 415 DFT-simulated XANES spectra from 91 distinct configurations was analyzed using a region-specific approach by decomposing each spectrum into pi*, sigma*, and post-edge regions. Random forest models trained on these spectral segments identified the pi* region as the most informative for predicting key local electronic descriptors, particularly the Bader charge and mean dopant-carbon bond length. The Bader charge quantifies dopant-induced charge redistribution and local bonding polarity, directly reflecting the degree of electronic perturbation introduced by heteroatom substitution. The enhanced predictive power of the pi* region arises from its strong coupling to the perturbed pi-electron network, which captures these charge-transfer and hybridization effects more effectively than sigma* or post-edge regions. These findings establish Bader charge as a robust and physically meaningful descriptor for quantifying dopant-induced electronic modulation and demonstrate that region-resolved ML analysis of XANES spectra provides a powerful pathway to uncover structure-property relationships in doped graphene and related materials. XANES ; Graphene ; Electronic structure ; Density functional theory ; Charge (physics) ; Spectral line ; Charge density ; Heteroatom Graphene research and applications ; Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research
Authors: N. LeMessurier | E. Katz | R. Pant | S. Ganley | H. Salzmann | L. M. McCaslin | J. M. Weber | J. D. Eaves
Source: arXiv (Cornell University) (preprint, 2026-04-03)
Field: Chemistry — Physical and Theoretical Chemistry

Electron dynamics mediate the water-carbon π bond The intermolecular interaction between a water molecule and the electrons in aromatic π systems--the water-π bond--lies at the heart of many chemical processes, yet its properties remain challenging to measure experimentally and model computationally. Infrared spectroscopy of pyrene anions hydrated by a single water molecule reveals vibrational and electronic motions that are often hidden in condensed phase measurements. Results from new machine-learning approaches to potentials and dipole moments show that the electron dynamics of the aromatic π cloud quench signals from some of water's vibrations and amplify others. The observed interplay between electronic and vibrational motions has general implications for modeling intermolecular interactions between water and aromatic systems in clusters, solutions, and at interfaces. Intermolecular force ; Molecule ; Pyrene ; Dipole ; Infrared ; Infrared spectroscopy ; Electron ; Spectroscopy ; Molecular vibration Crystallography and molecular interactions ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Jiafu Yang | Siyuan Chen | Pengcheng Xing | Dayu Hu | Kehan Liu | Zongjian Ye | Jieyi Xia | Jing He | Yijing Qian | Tianshu Wu
Source: Chemico-Biological Interactions (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

Carbon quantum dots exacerbate Parkinson's disease-like pathology through microglia-mediated neuroinflammatory responses with toxicological implications Quantum dot ; Carbon quantum dots ; Immune system ; Carbon fibers ; Pathogenesis ; Oxidative stress Carbon and Quantum Dots Applications ; Parkinson's Disease Mechanisms and Treatments ; Electrochemical sensors and biosensors
Authors: N. LeMessurier | E. Katz | R. Pant | S. Ganley | H. Salzmann | L. M. McCaslin | J. M. Weber | J. D. Eaves
Source: arXiv (Cornell University) (article, 2026-04-03)
Field: Chemistry — Physical and Theoretical Chemistry

Electron dynamics mediate the water-carbon π bond The intermolecular interaction between a water molecule and the electrons in aromatic π systems--the water-π bond--lies at the heart of many chemical processes, yet its properties remain challenging to measure experimentally and model computationally. Infrared spectroscopy of pyrene anions hydrated by a single water molecule reveals vibrational and electronic motions that are often hidden in condensed phase measurements. Results from new machine-learning approaches to potentials and dipole moments show that the electron dynamics of the aromatic π cloud quench signals from some of water's vibrations and amplify others. The observed interplay between electronic and vibrational motions has general implications for modeling intermolecular interactions between water and aromatic systems in clusters, solutions, and at interfaces. Intermolecular force ; Molecule ; Pyrene ; Dipole ; Infrared ; Infrared spectroscopy ; Electron ; Spectroscopy ; Molecular vibration Crystallography and molecular interactions ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Ian Slagle | Faisal M. Alamgir | Victor Fung
Source: ArXiv.org (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

Determining Atomic Structure from Spectroscopy via an Active Learning Framework Determining atomic structure from spectroscopic data is central to materials science but remains restricted to a limited set of techniques and material classes, largely due to the computational cost and complexity of structural refinement. Here we introduce ActiveStructOpt, a general framework that integrates graph neural network surrogate models with active learning to efficiently determine candidate structures that reproduce target spectra with minimal computational expenditure. Benchmarking with X-ray pair distribution function data, and with the more computationally demanding simulations of X-ray absorption near-edge spectra (XANES) and extended X-ray absorption fine structure (EXAFS), demonstrate that ActiveStructOpt reliably determines structures that match closely in spectra across diverse materials classes. Under equivalent computational budgets, ActiveStructOpt outperforms existing structure determination methods. By enabling data-efficient, multi-objective structural refinement across a broad range of computable spectroscopic techniques, ActiveStructOpt provides a flexible and extensible approach to atomic structure determination in complex materials. Artificial neural network ; Spectral line ; Benchmarking ; Set (abstract data type) ; Function (biology) ; Range (aeronautics) ; Spectroscopy ; Computational complexity theory Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Electrocatalysts for Energy Conversion
Authors: Tianxi Gong | Zhenyu Han | Xiaoyu Luo
Source: Food Packaging and Shelf Life (article, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Monitoring the freshness change of stored Pacific whiteleg shrimp through inkjet-printed composite indicator coupled with artificial neural network Pacific whiteleg shrimp ( Litopenaeus vannamei ) is a significant aquatic product extensively produced in the city of Zhuhai, Guangdong and other regions in the Greater Bay Area of China. Due to its high perishability during post-harvest storage, deteriorative reactions can result in substantial quality decrease over time, thereby causing potential economic loss and food wastage in the fishery industry. Hence developing a supplementary method in real-time monitoring shrimp freshness change is promising. In this study, we developed an inkjet-printed composite indicator, which was based on printing three pre-formulated inks onto photo papers: bromocresol purple, indigo carmine red, and reduced indigo carmine. The color-responsive indicator was applied inside the package to monitor the freshness change of whiteleg shrimps stored at 4 ºC for 9 days, where chemical and microbiological analyses were also conducted to assess shrimp quality. It was observed that the color changes of the indicator over storage were well correlated with the shrimp quality parameters including pH, total volatile basic nitrogen (TVB-N), total microbial counts and lipid oxidation products. After processing the indicator’s color response images, the artificial neural network (ANN) models were constructed, demonstrating strong predictive capability, especially with R 2 over 0.85 for pH and peroxide value. These positive results showed that the developed colorimetric indicator coupled with ANN can be a potential non-destructive method in evaluating real-time shrimp freshness, which may be also applicable to other fish and shellfish products with similar spoilage mechanisms. • Composite colorimetric indicator was developed via piezoelectric inkjet printing. • Indicator’s color response was substantial during shrimp deterioration over 9 days. • Biochemical changes and microbial growth of stored shrimp were analyzed. • Artificial neural network was employed to well predict real-time shrimp quality. Shrimp ; Litopenaeus ; Shellfish ; Food spoilage ; Water quality ; Raceway ; Algal bloom ; Aquaculture Spectroscopy and Chemometric Analyses ; Water Quality Monitoring Technologies ; Water Quality Monitoring and Analysis
Authors: Jiqin Huang | Huimin Zhang | Ying Zhao
Source: Electronics (article, 2026-02-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Retrieval of Microscopic Parameters for Terahertz Graphene Metasurfaces via Attention-Based Deep Learning Terahertz (THz) technology is finding increasingly widespread applications in biosensing, high-speed communication, and stealth materials. Meanwhile, graphene, as a quintessential two-dimensional material, has emerged as a core component of THz devices due to its unique optoelectronic properties. However, the precise and non-destructive characterization of the complex conductivity of graphene at the microscopic scale remains a formidable challenge. Conventional measurement methods often suffer from limitations associated with contact resistance or intricate sample preparation processes. In this paper, we propose a non-invasive parameter inversion method based on deep learning. We design a tri-layer graphene-silica-copper metasurface structure featuring a central cavity and establish a high-fidelity scattering model that incorporates physical effects such as edge diffraction and multi-mode resonance. Utilizing the Radar Cross Section (RCS) data generated by this model, we train a Deep Enhanced Conductivity Predictor (DECP) network integrated with a Convolutional Block Attention Module (CBAM). Experimental results demonstrate that the proposed network can accurately reconstruct the complex conductivity of graphene from far-field RCS data. The coefficients of determination (R2) for the prediction of both real and imaginary parts exceed 0.99, with a Root Mean Square Error (RMSE) as low as the order of 10−5. This study not only validates the effectiveness of data-driven approaches in material characterization but also provides a novel paradigm for the real-time monitoring and intelligent design of terahertz metasurfaces. Terahertz radiation ; Deep learning ; Graphene ; Scattering ; Conductivity ; Computation ; Characterization (materials science) ; Inversion (geology) Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Terahertz technology and applications
Authors: Nigar Asadova | Kaoutar Boussaoud | Jörg Meyer | Frank Tristram | Carsten Rockstuhl
Source: ArXiv.org (article, 2026-02-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A T-matrix database to promote information-driven research in nanophotonics Information-driven methods from machine learning and artificial intelligence for exploring the optical response of metasurfaces and, more generally, photonic systems rely on well-annotated datasets for training. For metasurfaces made from a periodic or aperiodic arrangement of scatterers, the primary information encoding their response is the optical properties of these individual scatterers. In the linear regime, that response is entirely contained in the transition or T-matrix of the individual scatterer. However, despite the widespread use of these T-matrices in exploring advanced photonic materials within the larger community, there is no common infrastructure for distributing them with consistent metadata and a standard representation. That would be important to avoid the repetitive, resource-intensive computation of these T-matrices by researchers worldwide and to enable data-driven research. To overcome this limitation, we introduce the Daphona T-matrix portal at https://tmatrix.scc.kit.edu/, a web-based platform for interactive searching, filtering, and exporting standardized data containing structure-property relations for a wide range of scatterers, as expressed by their T-matrices. Besides introducing this infrastructure, we demonstrate how the available data enables addressing scientific questions in the broader context of information-driven research. The multiple illustrative examples in our contribution cover both surrogate forward models and inverse design models, and operate either directly on the T-matrix or alternatively on optical observables of metasurfaces made from these scatterers. Metadata ; Context (archaeology) ; Aperiodic graph ; Photonics ; Nanophotonics ; Encoding (memory) ; Point (geometry) Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Plasmonic and Surface Plasmon Research
Authors: G. Thamizharasan | R. D. Eithiraj
Source: Advanced Theory and Simulations (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Wide Bandgap Oxychalcogenide With Low Effective Mass, Strong Excitonic Effects, and High Figure of Merit: BaTa <sub>4</sub> Te <sub>3</sub> O <sub>17</sub> ABSTRACT A comprehensive first‐principles and machine‐learning‐assisted study of the oxychalcogenide BaTa 4 Te 3 O 17 , highlighting its promise as a multifunctional material for thermoelectric, optoelectronic, and photocatalytic applications. Density functional theory (DFT) calculations show a direct bandgap of 3.3 eV with mixed dispersive and flat valence/conduction states, promoting anisotropic carrier transport. The effective electron and hole masses along the Γ–Γ direction exhibit more balanced carrier masses (m e * = 0.682 m 0 , m h * = 0.714 m 0 ), corresponding to a reduced mass of 0.348 m 0 , a binding energy of 260 meV, and a Bohr radius of 6.48 Å, signifying weaker exciton confinement. Thermoelectric analysis yields a Seebeck coefficient of 1029.23 µV K −1 and a figure of merit ZT ≈ 0.94 at 300 K, improving at higher temperatures. The band‐edge positions align well with the hydrogen evolution potential, suggesting photocatalytic suitability. To complement DFT results, supervised regression models (XGBoost and ensemble‐stacking) predict E g ≈ 3.3 eV (R 2 = 0.55) and ZT ≈ 0.94 with &gt;90% accuracy. This integrated DFT–ML framework demonstrates a cost‐effective route for screening and optimizing heteroanionic oxychalcogenides for next‐generation energy and electronic applications. Bohr radius ; Effective mass (spring–mass system) ; Band gap ; Exciton ; Figure of merit ; Binding energy ; Electronic band structure ; RADIUS Iron-based superconductors research ; Advanced Condensed Matter Physics ; Crystal Structures and Properties
Authors: Federico Grasselli | Kevin Rossi | Stefano de Gironcoli | Andrea Grisafi
Source: Open MIND (preprint, 2026-02-11)
Field: Materials Science — Materials Chemistry

Long-range electrostatics in atomistic machine learning: a physical perspective The inclusion of long-range electrostatics in atomistic machine learning (ML) is receiving increasing attention for achieving quantum-mechanical accuracy in predicting a wide range of molecular and material properties. However, there is still no general prescription on how long-range physical effects should be incorporated into the model while preserving well-established locality principles underlying most transferable ML representations. Here, we provide a physical perspective on the problem, by discussing how distinct contributions to the system's electrostatics can be captured through the adoption of different learning paradigms. Specifically, we discern between local charge models, which rely either on explicit charge-density decompositions or implicit auxiliary variables, and models where a notion of nonlocality is deliberately introduced, either via self-consistent procedures or by using nonlocal descriptors and learning architectures. We further address the related aspect of incorporating finite-field effects through the coupling with the system's polarization, relevant for the application of an external electric bias. We conclude by discussing the implications for the simulation of electrochemical interfaces, where long-range electrostatics are essential to capture the interplay between charge redistribution, interfacial dynamics, and ionic screening, and for ionic transport phenomena, which, although less explored, appear far less sensitive to their inclusion. Electrostatics ; Quantum nonlocality ; Perspective (graphical) ; Charge (physics) ; Range (aeronautics) ; Static electricity ; Coupling (piping) ; Locality Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Quantum many-body systems
Authors: Francesco Di Colandrea | Lorenzo Marrucci | Filippo Cardano
Source: Nanophotonics (article, 2026-02-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Skyrmionic Polarization Textures in Structured Dielectric Planar Media Skyrmionic patterns of optical fields have recently emerged across diverse photonic platforms. Here, we show that such textures also arise in the polarization eigenstates of light propagation through flat dielectric devices with an engineered, space-dependent optic axis orientation. We focus on two-dimensional periodic structures, where propagation through multiple devices maps onto quantum dynamics on a synthetic optical lattice. Adopting the condensed-matter framework, a spatial period defines an effective Brillouin zone, and polarization eigenstates can be grouped in two bands, with the role of energy played by the opposite phase delay. When such eigenstates exhibit skyrmionic textures, the corresponding lattice model shows the topology of a Chern insulator. We validate these concepts in a system of three tunable liquid-crystal metasurfaces. Using machine learning, polarization eigenmodes are reconstructed over one spatial period. We identify configurations of the devices' parameters that lead to topologically nontrivial bands, where we directly observe skyrmionic eigenpolarization textures. We also extract local observables of lattice models, such as the Berry curvature and the quantum metric. We finally report a numerical simulation of an all-optical quantum Hall effect emerging when light propagates through a sequence of such devices, arranged to mimic the effect of an external force on the lattice. Planar ; Polarization (electrochemistry) ; Dielectric ; Texture (cosmology) Metamaterials and Metasurfaces Applications ; Optical Coatings and Gratings ; Photonic Crystals and Applications
Authors: Gianluca Levi | Max Kroesbergen | Louis Thirion | Yorick L. A. Schmerwitz | Elvar Ö. Jónsson | Pavlo Bilous | Philipp Hansmann | Hannes Jónsson
Source: Journal of Chemical Theory and Computation (article, 2026-03-23)
Field: Materials Science — Materials Chemistry

Orbital Optimization and Neural-Network-Assisted Configuration Interaction Calculations of Rydberg States Rydberg excited states of molecules pose a challenge for electronic structure calculations because of their highly diffuse electron distribution. Even large and elaborate atomic basis sets tend to underrepresent the long-range tail, overly confining the Rydberg state. An approach is presented here where the molecular orbitals are variationally optimized for the excited state using a plane wave basis set in a Hartree-Fock calculation, followed by a configuration interaction calculation. The use of excited state optimized orbitals greatly enhances the convergence of the many-body calculation, as illustrated by a full configuration interaction calculation of the 2s Rydberg state of H<sub>2</sub>. A neural-network-based selective configuration interaction approach is then applied to calculations of 3s and 3p states of H<sub>2</sub>O and NH<sub>3</sub>. The obtained values of excitation energy are in close agreement with experimental measurements as well as previous many-body calculations where sufficiently diffuse atomic basis sets were used. Calculations using atomic basis sets lacking extra diffuse functions, such as aug-cc-pVTZ, give significantly higher estimates due to confinement of the Rydberg states. Rydberg formula ; Excited state ; Configuration interaction ; Atomic orbital ; Rydberg state ; Basis set ; Excitation ; Basis (linear algebra) ; Rydberg atom Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Meiyu Si | Chunguang Liu
Source: Talanta (article, 2026-01-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Artificial intelligence assisted surface enhanced Raman scattering sensing achieves effective on-site analysis Raman scattering ; Preprocessor ; Interference (communication) ; Raman spectroscopy ; SIGNAL (programming language) ; Signal processing ; Process (computing) ; Data acquisition Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Innovative Microfluidic and Catalytic Techniques Innovation