jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yinan Wang | Arpita Varadwaj | Teruyasu Mizoguchi | Masato Kotsugi
Source: arXiv (Cornell University) (preprint, 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: Nigar Asadova | Kaoutar Boussaoud | Jörg Meyer | Frank Tristram | Carsten Rockstuhl
Source: Open MIND (preprint, 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: Raymond P. H. Wu | Sirirat Sae Lim | Haishang Wu | Susumu Otsuki
Source: Oxford Open Materials Science (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

The exploration of enhanced methods in data science and applications in materials science Abstract This study develops a structured framework for improving data quality governance and regression reliability in materials informatics (MI). The contributions include: (i) a composite data quality indicator (R : M ratio) for evaluating regression coherence, (ii) an encoder–decoder mechanism for restructuring heterogeneous laboratory data into ML-compatible representations, (iii) a design-space control strategy integrating feature evaluation and clustering, and (iv) a coefficient-informed masking process for model-guided yield optimization. These components are integrated within a conceptual MI platform emphasizing process-level consistency. A laboratory dataset comprising 520 samples and 260 multivariate features was analyzed using multiple machine learning (ML) architectures to evaluate regression performance and design-space behavior. Experimental results indicate that regression coherence, as measured by the R : M ratio, is associated with improved predictive stability, while structured feature and cluster validation enhance design-space interpretability. The masking strategy generates model-derived projections suggesting potential yield improvements under controlled conditions. Beyond empirical evaluation, the study highlights methodological challenges related to data foundation, structural alignment, and model validation. By integrating regression-coherence metrics with structured design-space control, this work advances more transparent and reproducible MI workflows. Process (computing) ; Materials informatics ; Masking (illustration) ; Informatics ; Standardization ; Feature (linguistics) ; Quality (philosophy) Machine Learning in Materials Science ; Big Data and Digital Economy ; Advanced Materials Characterization Techniques
Authors: William Sandholt | Nicolas Gauquelin | John Mangeri | Edwin Dollekamp | Gyanendra Panchal | Tamazouzt Chennit | Annick De Backer | Arno Annys | Nikolas Vitaliti | Andrea Roberto Insinga | Jonas Mejlby Hansen | Rajesh Mandal | Davi R. Rodrigues | Sandra Van Aert | Katja Wurster | Arghya Bhowmik | Ivano E. Castelli | So̷ren Bredmose Simonsen | Thomas S. Jespersen | Richard D. James | Bharat Jalan | Jo Verbeeck | Juan Maria Garcia Lastra | Nini Pryds
Source: arXiv (Cornell University) (article, 2026-03-28)
Field: Materials Science — Materials Chemistry

Designing dislocation-driven polar vortex networks in twisted perovskites Twisting two atomic layers produces a geometric moire pattern, but bonding-induced interfacial reconstruction fundamentally transforms this into an ordered dislocation network - a distinction obscured in weakly-bonded van der Waals systems. Although in-plane topological vortex nanostructures arising from twisting-induced lateral strain modulation have been linked to periodic moire patterns in freestanding perovskite layers and 2D bilayers, their coupling to the interfacial dislocation network in twisted layers remains unresolved. Here we demonstrate that twisting freestanding SrTiO3 layers undergo interfacial reconstruction into a network of screw dislocations, accompanied by the emergence of in-plane topological vortices. Unlike in previous reports, these vortices are associated with the periodicity of the dislocation network rather than with geometric moire patterns. Four-dimensional scanning transmission electron microscopy (4D-STEM) reveals long-range ordered vortex-antivortex arrays with nearly continuous polarisation rotation. A machine-learning interatomic potential, trained on first-principles calculations, together with phase-field modelling, confirms that competing strains within the dislocation network stabilize polar vortex-antivortex pairs and drive the emergence of an electronic superlattice with a well-defined periodicity. Our results establish twist-controlled dislocation networks as a new and versatile route to designing local polar and electronic structures in oxide materials. Superlattice ; Dislocation ; Vortex ; Moiré pattern ; Polar ; van der Waals force ; Perovskite (structure) ; Topological defect Electronic and Structural Properties of Oxides ; Topological Materials and Phenomena ; Chemical and Physical Properties of Materials
Authors: Shavita | Sneha Sharma | Amit L. Sharma | Suman Singh
Source: Journal of Power Sources (article, 2026-02-06)
Field: Materials Science — Materials Chemistry

Machine learning models for predicting electrochemical behavior of MXene for energy storage applications Supercapacitor ; Capacitance ; Energy storage ; Electrochemistry ; Energy (signal processing) ; Current (fluid) ; Experimental data ; Cyclic voltammetry ; Voltammetry MXene and MAX Phase Materials ; Supercapacitor Materials and Fabrication ; Advancements in Battery Materials
Authors: Sofiene Achour | Zied Hosni | Bahoueddine Tangour | A. H. Said
Source: Materials Chemistry and Physics (article, 2026-03-14)
Field: Chemistry — Inorganic Chemistry

A hybrid DFT–machine learning approach for fast HOMO–LUMO gap prediction in naphthalene diimide MOF linkers Naphthalene diimides (NDIs) are a class of electron-deficient aromatic compounds widely explored for applications in organic electronics, photovoltaics, and metal-organic frameworks (MOFs), owing to their high electron affinity, robust stability, and tunable optoelectronic properties. Precise modulation of the HOMO-LUMO energy gap is critical to optimize their performance, yet accurate prediction remains challenging due to the limitations of conventional density functional theory (DFT) approaches, including functional sensitivity and computational cost. In this study, we present a hybrid DFT-machine learning (ML) framework to efficiently and accurately predict the energy gaps of 195 structurally diverse NDI derivatives with a broad spectrum of electron-donating and electron-withdrawing substituents. DFT calculations were performed only to compute the reference HOMO-LUMO gap used as the regression target. All machine-learning input features were generated solely from the molecular SMILES representations using RDKit-based cheminformatics descriptors. Among the tested algorithms, LightGBM achieved the best performance (R 2 = 0.86, RMSE = 0.25 eV), exhibiting robust generalization, low error dispersion, and meaningful uncertainty estimates. SHAP analysis revealed that topological, electronic, and steric descriptors play a central role in modulating the energy gap. Compared to baseline models, our hybrid approach significantly reduces computational time while capturing complex structure-property relationships. Beyond virtual screening, the model offers mechanistic interpretability and enables the rational design of NDI with tailored gaps for optoelectronic and MOF-based applications. This work lays the foundation for integrating active learning, transfer learning, and uncertainty quantification in future workflows targeting π-conjugated systems with high functional diversity. • LightGBM+RDKit predicts B3LYP/6-31G(d,p) gaps for 195 NDI derivatives. • External test vs literature gaps (n=32) with gap-type labeling (optical/electrochem). • SHAP reveals topological and E-state descriptors governing NDI gap modulation. • Break-even analysis: net CPU-time savings after screening ∼200 candidates. • Workflow enables fast pre-screening of NDI MOF linkers without new DFT runs. Diimide ; Naphthalene ; Band gap ; Hybrid material ; Characterization (materials science) Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Advanced Chemical Physics Studies
Authors: Thomas Planko | Harald Fitzek | Stefanie Eichinger | Johannes Rattenberger | Günther Koraimann | Hartmuth Schröttner
Source: Materials Chemistry and Physics (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

Microbiologically influenced corrosion (MIC) of steel – a study using correlative SEM, EDXS and Raman microscopy Mackinawite ; Corrosion ; Scanning electron microscope ; Greigite ; Raman spectroscopy ; Pitting corrosion ; Biofilm ; Correlative Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Metal Extraction and Bioleaching
Authors: Annika Stellhorn | Juan G. C. Palma | Alicia Backs | Anders Bergman | A. B. Klautau | Emmanuel Kentzinger | C. Bednarski-Meinke | Steffen Tober | E. Blackburn | Juri Barthel | Nina‐Juliane Steinke | Helena M. Petrilli | I. P. Miranda
Source: arXiv (Cornell University) (article, 2026-04-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Disorder-induced chirality in superconductor-ferromagnet heterostructures revealed by neutron scattering and multiscale modeling Chirality in superconductor-ferromagnet hybrids strongly influences phenomena such as the observable signatures of long-range triplet superconductivity, but its microscopic origin in nominally centrosymmetric ferromagnets is still unclear. Here, we combine structural characterization, polarization-analyzed grazing-incidence small-angle neutron scattering (PA-GISANS), first-principles calculations, and deep-learning-assisted multiscale modeling to study FePd and Nb/FePd heterostructures. Experimentally, we observe partial L1$_0$ order, atomic intermixing, anti-phase boundaries, and a depth-dependent defect gradient across the FePd layer, together with a finite net magnetic chirality at room temperature. The GISANS asymmetry indicates that the main chiral contribution lies in-plane, with an additional out-of-plane component associated with depth-dependent magnetic inhomogeneity. Theoretically, we show that chemical disorder in FePd, especially when combined with a compositional gradient, produces finite Dzyaloshinskii-Moriya interactions and stabilizes chiral finite-$\mathbf{q}$ magnetic modulations with mixed Bloch-Néel character. In the mesoscopic model, the resulting in-plane modulation length approaches the experimentally observed range. These results identify disorder and compositional gradients as intrinsic microscopic sources of net chirality in FePd-based films, showing that the observed chirality does not arise only from interface effects. Chirality (physics) ; Mesoscopic physics ; Asymmetry ; Neutron scattering ; Ferromagnetism ; Multiscale modeling ; Scattering Iron-based superconductors research ; Physics of Superconductivity and Magnetism ; Magnetic properties of thin films
Authors: Mattia Vallerio | Antonio Del Rio Chanona | Francisco J. Navarro-Brull
Source: Digital Chemical Engineering (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

All you need is noise — from feature selection to explainable industrial AI Modern chemical plants record thousands of sensor tags, yet only a small fraction meaningfully influence yield, quality, or safety. Identifying those key drivers is often more difficult than building the predictive model itself. In this work, we show that appending one or more Synthetic Noise Features (SNFs), non-informative random variables known a priori , provide a simple reference for judging variable relevance. We show the impact of this model agnostic step across three workflows. In supervised learning, noise features establish an automatic cutoff for the feature importance, guide model regularization and signal when the dataset itself lacks predictive information. In unsupervised learning, they provide an unbiased threshold preventing spurious anomalies and latent dimensions. Finally, we demonstrate the applicability of this approach to small datasets typical of experimental work and Design of Experiments (DoE), including Definitive Screening , Response Surface , and space-filling designs, as well as active learning using Bayesian optimization . By turning nothing but noise into a quantitative benchmark, SNFs offer an immediately deployable safeguard against overfitting and misplaced experimental effort in data-driven chemical engineering. • Synthetic Noise Features (SNFs) give a single, model-agnostic rule for variable relevance in supervised, unsupervised, and adaptive-learning workflows. • SNF thresholds act as a penalization signal to prune overgrown trees and scale back neural networks. • In unsupervised learning, SNFs flag noise-dominated latent variables and drive dimensionality reduction for anomaly detection models. • Coupling an SNF with Gaussian-process (GP) or other uncertainty metrics during active learning identifies inert factors, potentially preventing wasteful exploration. • Algorithm-agnostic; can be implemented immediately in industrial settings. Overfitting ; Spurious relationship ; Feature selection ; Noise (video) ; Pattern recognition (psychology) ; Feature (linguistics) ; Supervised learning ; Latent variable ; Bayesian probability Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Machine Learning and Data Classification
Authors: G. Jeffrey Snyder
Source: Advanced Materials (article, 2026-03-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The Possibility of New Complex Magnet Materials Magnets are essential for mobile consumer electronics, electric motors that power industry and the future of transportation as well as generating and transforming most electric power. Strong magnets reduce the size and weight of motors and generators as well as improve efficiency. The most powerful <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>Nd</mml:mi> <mml:mn>2</mml:mn></mml:msub> <mml:mi>Fe</mml:mi></mml:mrow> <mml:annotation>${\rm Nd}_2{\rm Fe}$</mml:annotation></mml:semantics> </mml:math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mrow></mml:mrow> <mml:mn>14</mml:mn></mml:msub> <mml:mi>B</mml:mi></mml:mrow> <mml:annotation>$_{14}{\rm B}$</mml:annotation></mml:semantics> </mml:math> -based magnets have a complex structure like millions that are expected to exist but have not been made and characterized. With the recent developments of AI materials discovery techniques, which enable data-driven and machine-learning-assisted screening, together with computational approaches that can accurately predict intrinsic magnetic properties of a given structure, and high-throughput autonomous labs, the discovery of new, ultra-powerful magnet materials with saturation magnetization greater than 2.5 Tesla or magnetic energy density ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>B</mml:mi> <mml:msub><mml:mi>H</mml:mi> <mml:mrow><mml:mi>m</mml:mi> <mml:mi>a</mml:mi> <mml:mi>x</mml:mi></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$BH_{max}$</mml:annotation></mml:semantics> </mml:math> ) greater than 800 kJ/ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:msup><mml:mi>m</mml:mi> <mml:mn>3</mml:mn></mml:msup> <mml:annotation>${\rm m}^{3}$</mml:annotation></mml:semantics> </mml:math> is now quite possible. Magnet ; Magnetization ; Electric motor ; Electric power ; Saturation (graph theory) ; Magnetic field ; Power (physics) ; Power density Magnetic Properties of Alloys ; Machine Learning in Materials Science ; Magnetic Properties and Applications
Authors: Ruohan Shen | Yujia Zhai | Hongda Chen | Qinqin Shao | Jia Liu
Source: Materials Characterization (article, 2026-02-27)
Field: Materials Science — Materials Chemistry

Accurate segmentation of high-density dislocations in micrographs with DeepCrack model Dislocations are fundamental defects in crystalline materials that critically influence mechanical properties and deformation mechanisms. Accurate segmentation of dislocation lines in micrographs is essential for quantitative microstructure analysis, yet it remains challenging due to complex contrast variations, high density, and morphological complexity. In this work, we adapt and retrain the DeepCrack model, a deep learning architecture originally designed for crack detection, to segment dislocations in experimental micrographs. Using a carefully annotated dataset of only 124 images acquired via scanning transmission electron microscopy and X-ray topography, which exhibit diverse dislocation morphologies and densities, the retrained model demonstrates strong generalization capability and accurate segmentation performance. It performs robustly even on highly challenging cases with extremely dense dislocation networks and significant image noise, albeit with a trade-off in output quality to avoid overfitting by shortening model training epochs. To overcome this limitation, a model recursion strategy was explored, which demonstrates potential for achieving top-tier recognition accuracy and high-quality binarized segmentation. • DeepCrack model was retrained to achive accurate segmentation of high-density dislocations in micrographs. • The model maintains a strong generalization capability and shows robustness even on extreme test cases. • Recursive model application holds potential toward superlative, near-binary results. Segmentation ; Overfitting ; Dislocation ; Robustness (evolution) ; Generalization ; Pattern recognition (psychology) ; Deformation (meteorology) ; Deep learning Microstructure and mechanical properties ; Composite Material Mechanics ; Optical measurement and interference techniques
Authors: M. Lebeda | Jan Drahokoupil | Petr Vlčák | Šimon Svoboda | Axel van de Walle
Source: Journal of Computational Science (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

SimplySQS: An automated and reproducible workflow for special quasirandom structure generation with ATAT The special quasirandom structure (SQS) method is widely used for modeling disordered materials under periodic boundary conditions, with the ATAT mcsqs module being one of the most established implementations. However, SQS generation with mcsqs typically relies on manual preparation of input files, ad hoc execution scripts, and post-processing steps, which introduces user-dependent errors and limits reproducibility. Here, we present SimplySQS ( https://simplysqs.com ), an automated and reproducible workflow for SQS generation that is delivered through an online, interactive interface. SimplySQS guides users through structure import, compositional and supercell definition, and cluster parameter selection, while automatically generating all required ATAT input files and a single all-in-one execution script that encapsulates the complete search process. By standardizing input preparation, execution, and output analysis, the framework minimizes errors associated with manual file handling and enables consistent reproducibility of SQS searches. The workflow is demonstrated on the Pb 1- x Sr x TiO 3 (PSTO, including PbTiO 3 (PTO) and SrTiO 3 (STO)) perovskite system. SQSs spanning the entire concentration range were generated using a single automated bash script produced by SimplySQS , after which all resulting structures were subjected to geometry optimization using a universal machine-learning interatomic potential (MACE MATPES-r²SCAN-0). This approach reliably reproduced the experimentally observed cubic-to-tetragonal transition near x ≈ 0.5, with lattice parameters deviating by less than 1% in the cubic region ( x > 0.5) and less than 4% in the tetragonal region ( x ≤ 0.5). Overall, SimplySQS transforms SQS generation with ATAT into an intuitive, reproducible, and systematic framework for modeling disordered materials. Workflow ; Tetragonal crystal system ; Lattice (music) ; Cluster (spacecraft) ; Range (aeronautics) ; Data structure ; Boundary (topology) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ferroelectric and Piezoelectric Materials