jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vijeeta Patil | Geeta R. Bharamagoudar | Shashikumar G. Totad
Source: Applied Food Research (article, 2026-01-10)
Field: Chemistry — Analytical Chemistry

Towards food safety: Identifying fake rice using SVM and contour analysis In India, rice is the most widely consumed staple food compared to wheat and other millets. However, the emergence of fake or plastic rice, visually indistinguishable from real rice, has raised serious food safety concerns. Consumption of such adulterated rice, made from toxic materials like polystyrene, poses significant health risks. Hence, differentiating counterfeit rice from genuine varieties has become essential. The growing prevalence of synthetic rice poses significant threats to food safety and public health, necessitating the development of reliable and scalable detection systems. This study presents a machine learning-based approach for the identification of fake rice using Support Vector Machine (SVM) and contour analysis. The proposed system leverages image-based features, including morphological, color, and contour-based characteristics, to distinguish real rice grains from spurious ones. In its initial version, referred to as Prediction 1.0 , the model employs traditional image processing techniques for feature extraction and uses SVM-support vector machine classifier to perform binary classification. Preprocessing steps such as resizing, orientation correction, and data augmentation enhanced the model’s ability to generalize across different rice varieties and imaging conditions. The system achieved a high classification confidence of 97.3% , demonstrating its robustness and reliability in detecting fake rice. Visual results from the model underscore the effectiveness of contour-based features in discriminating between genuine and counterfeit grains. The findings highlight the strong potential of combining contour analysis with machine learning techniques to build cost-effective, scalable, and automated solutions for quality control in the rice industry. Support vector machine ; Counterfeit ; Preprocessor ; Spurious relationship ; Classifier (UML) ; Robustness (evolution) ; Pattern recognition (psychology) ; Feature extraction ; Flagging Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Identification and Quantification in Food
Authors: Sabyasachi Tiwari | Bruno Cucco | Viet-Anh Ha | Feliciano Giustino
Source: arXiv (Cornell University) (preprint, 2026-04-02)
Field: Materials Science — Materials Chemistry

Meta-optimization of maximally-localized Wannier functions Maximally-localized Wannier functions are quantum wavefunctions resembling atomic orbitals that are used to describe electrons in condensed matter. Since their introduction in 1997, these functions have become ubiquitous in ab initio materials simulations, including applications in linear-scaling methods, strongly-correlated electron systems, quantum transport, electron-phonon interactions, and topological materials. Despite their widespread adoption in a vast software ecosystem, Wannier functions have not yet attained their fullest potential in the presence of entangled bands, as their optimization remains challenging and labor-intensive. Here, we introduce a universal meta-optimization method that leverages workflow abstraction and machine learning techniques like differential evolution and Bayesian optimization to generate globally optimized Wannier functions without human intervention. We demonstrate this approach through three applications: (i) autonomous interpolation of entangled band structures with millielectronvolt accuracy starting from coarse Brillouin zone grids, (ii) thousand-fold acceleration of fully ab initio Boltzmann transport calculations via the use of minimal coarse Brillouin zone grids, and (iii) ultra-fast high-throughput calculations of high-precision Wannier functions for large materials libraries. This work brings calculations that previously required supercomputers within the reach of personal computers. Wannier function ; Brillouin zone ; Ab initio ; Wave function ; Interpolation (computer graphics) ; Abstraction ; Atomic orbital ; Quantum Machine Learning in Materials Science ; Quantum many-body systems ; Quantum Computing Algorithms and Architecture
Authors: O. Yu. Dorman | K. V. Simeiko
Source: Nuclear Power and the Environment (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Identification of Ways to Improve the Methods for Crushing and Sorting Irradiated Graphite from Nuclear Facilities The article is devoted to the problem of handling irradiated graphite (IG) from nuclear installations (NI). Reactor graphite (or nuclear-grade graphite) has been used as a neutron moderator or reflector in more than 100 nuclear reactors and is a structural and functional material in Generation IV nuclear power systems, in particular in high-temperature gas-cooled reactors (HTGR, HTR, VHTR) and molten salt reactors (MSR). Therefore, the problem of handling IG from nuclear power plants is a pressing scientific issue. For some possible options for the utilization of IG, in particular, incineration, burial, and treatment with ion exchange resins, the issue of crushing and sorting IG is relevant. Based on the analysis of the physical properties of graphite after long-term exposure to high temperatures and neutron irradiation, the authors propose ways to improve the methods of IG crushing and sorting using artificial intelligence (AI). The article describes a method for crushing IG using a ball mill with discharge through a grid and a developed algorithm for separating IG using AI, which includes processing and control cycles using neural networks. Future improvements in the use of a ball mill with discharge through a grid and the separation algorithm should be aimed at reducing energy consumption and adapting to remote control conditions. Graphite ; Fissile material ; Control rod ; Nuclear graphite ; Neutron ; Ball mill ; Nuclear reactor ; Sorting Graphite, nuclear technology, radiation studies ; Environmental and Industrial Safety ; Industrial Engineering and Technologies
Authors: Abidemi Adeyoye | Patricia I. Popoola | Olawale Popoola | S. O. Adeosun | Modupeola Dada
Source: International Journal of Hydrogen Energy (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Refractory high-entropy alloys for solid-state hydrogen storage: A computational design review CALPHAD ; Hydrogen storage ; Hydrogen ; Phase (matter) ; Hydride ; Refractory metals ; Process (computing) ; Temperature cycling Hydrogen Storage and Materials ; High Entropy Alloys Studies ; Catalysts for Methane Reforming
Authors: Xiaojing Yao | Zhiheng Ji | Jinxin Sun | Lijuan Meng | Xiuyun Zhang
Source: Inorganic Chemistry (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

The Stability Mechanism of Borophene Edges The edges of two-dimensional (2D) materials are pivotal to both their intrinsic properties and their growth mechanisms. As one of the most isomer-abundant 2D materials, borophene's edges remain underexplored. Herein, we systematically investigate the edge structures and stabilization mechanisms of α-, α<sub>1</sub>-, β<sub>1</sub>-, β<sub>12</sub>-, and χ<sub>3</sub>-phase borophenes. Our findings reveal that the edge stability is highly sensitive to their specific configurations and is notably lower than that of graphene. Among these, double-chain width edges are identified as the most stable configurations, whereas zigzag (ZZ) edges adjacent to hexagonal hole (HH) edges or those with a comblike morphology exhibit considerable instability, accompanied by structural reconstruction. Furthermore, we develop a global descriptor to decipher the influence of edge configurations on stability via an interpretable machine learning (ML) approach. Mechanistic analysis indicates that the edge stability is positively correlated with the density of 4-coordinated B atoms (<i>n</i><sub>4</sub>) and inversely correlated with that of 3-coordinated B atoms (<i>n</i><sub>3</sub>). Our results provide deep insight into the stability of borophene and other 2D materials. Zigzag ; Enhanced Data Rates for GSM Evolution ; Stability (learning theory) ; Borophene ; Hexagonal crystal system ; Morphology (biology) Boron and Carbon Nanomaterials Research ; Graphene research and applications ; 2D Materials and Applications
Authors: Н. А. Кулеш | A. Bolyachkin | Hajime Nakamura | Tadakatsu Ohkubo | Hossein Sepehri‐Amin
Source: Advanced Intelligent Discovery (article, 2026-02-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Harnessing Digital Microstructure for Simulation‐Guided Optimization of Permanent Magnets Optimization of functional materials with properties governed by complex microstructures remains a central challenge in materials science. Accurate modeling of microstructure‐property relationships is often limited by idealized geometries and insufficient integration between experimental characterization and simulation. Here we present an experimental‐to‐computational workflow that combines 3D focused ion beam‐scanning electron microscopy tomography, deep learning‐based segmentation, and physics‐based postprocessing to enable high‐fidelity, structure‐aware modeling. Although this approach can be applied on a broad range of materials, we demonstrate it on a heavy‐rare‐earth‐free Nd‐Fe‐B magnet. We reconstruct a 13 × 29 × 33 µm 3 volume containing ~800 Nd 2 Fe 14 B grains and use patch‐wise micromagnetic simulations to identify microstructural features that control magnetization reversal. As a result, we estimated an upper bound on coercivity and analyzed local switching fields governed by a combination of microstructural factors, revealing the critical role of large nonmagnetic inclusions in strongly textured, exchange‐coupled regions. This methodology extends to multiphysics simulations, enabling data‐driven design of energy‐conversion and other functional materials, while also providing high‐quality data for generative AI and reduced‐order modeling. Microstructure ; Multiphysics ; Coercivity ; Magnet ; Workflow ; Micromagnetics ; Characterization (materials science) ; Magnetization Magnetic Properties of Alloys ; Magnetic properties of thin films ; Machine Learning in Materials Science
Authors: Sabyasachi Tiwari | Bruno Cucco | Viet-Anh Ha | Feliciano Giustino
Source: arXiv (Cornell University) (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

Meta-optimization of maximally-localized Wannier functions Maximally-localized Wannier functions are quantum wavefunctions resembling atomic orbitals that are used to describe electrons in condensed matter. Since their introduction in 1997, these functions have become ubiquitous in ab initio materials simulations, including applications in linear-scaling methods, strongly-correlated electron systems, quantum transport, electron-phonon interactions, and topological materials. Despite their widespread adoption in a vast software ecosystem, Wannier functions have not yet attained their fullest potential in the presence of entangled bands, as their optimization remains challenging and labor-intensive. Here, we introduce a universal meta-optimization method that leverages workflow abstraction and machine learning techniques like differential evolution and Bayesian optimization to generate globally optimized Wannier functions without human intervention. We demonstrate this approach through three applications: (i) autonomous interpolation of entangled band structures with millielectronvolt accuracy starting from coarse Brillouin zone grids, (ii) thousand-fold acceleration of fully ab initio Boltzmann transport calculations via the use of minimal coarse Brillouin zone grids, and (iii) ultra-fast high-throughput calculations of high-precision Wannier functions for large materials libraries. This work brings calculations that previously required supercomputers within the reach of personal computers. Wannier function ; Brillouin zone ; Ab initio ; Wave function ; Interpolation (computer graphics) ; Abstraction ; Atomic orbital ; Quantum Machine Learning in Materials Science ; Quantum many-body systems ; Quantum Computing Algorithms and Architecture
Authors: Jie Liu | Lin Zhang
Source: Advanced Theory and Simulations (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Insight Into Packing Structures as well as Electrical States for Ti Nanoparticles on Heating Through Integrating Machine Learning Potential With a MD/QM Hybrid Strategy ABSTRACT This study employs an integrated multiscale simulation strategy that combines machine learning potential, molecular dynamics, and density‐functional tight‐binding methods to investigate the temperature‐ and size‐dependent structural transitions and electronic state evolution in titanium nanoparticles (2.2–4.4 nm). The aim is to provide atomic‐level insight into the relationship between structural rearrangements and electronic properties during thermal processes. Analysis of packing patterns reveals distinct structural transformation pathways across different particle sizes. The potential energy and its difference reveal a transition temperature range (400–1450 K) that marks significant changes in atomic thermal vibration, strongly influenced by particle size. Fermi energy and the energy gaps between the highest occupied molecular orbital and the lowest unoccupied molecular orbital illustrate key electronic behavior trends, showing a decrease in the energy gap with increasing temperature and particle size, eventually approaching zero for larger nanoparticles, indicating enhanced metallic character. Mulliken charge distributions further exhibit a characteristic core‐shell spatial pattern, while charge density differences emphasize surface electron depletion and internal charge redistribution, which vary systematically with nanoparticle size. Nanoparticle ; Particle (ecology) ; Charge (physics) ; Electronic structure ; Fermi level ; Range (aeronautics) ; Mulliken population analysis ; Band gap ; Thermal Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; nanoparticles nucleation surface interactions
Authors: North Pinkley | Uchhwas Banik | Nayeem Anam | Aastha Oza | Kevin J. Ledford | Bhavya Sharma
Source: Sensors (article, 2026-03-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design, Implementation, and Advances in Indirect SERS Sensors for Biomedical and Human-Health-Related Analyte Detection Novel, accurate molecular diagnostics are driving new advances across medicine, public health, and environmental monitoring. Surface-enhanced Raman spectroscopy (SERS) nanotags are powerful platforms for ultrasensitive, multiplexed, and quantitative detection of molecular targets. This review focuses on indirect sensing strategies, where SERS nanotags act as signal transducers, resulting in enhanced and unique Raman spectra upon binding of target analytes (high specificity) and allowing for ultralow limits of detection. These indirect SERS sensors typically consist of a plasmonic core, a Raman reporter molecule, and a ligand that targets the analyte of interest. Each of these components contributes to the sensitivity, stability, and selectivity of the system. Rational design of SERS nanotags requires balancing enhancement efficiency with reproducibility, biocompatibility, and assay integration. The choice of reporter molecules, for instance, governs spectral uniqueness and enables multiplexed detection of multiple analytes within a single sample. Recent advances in artificial intelligence and machine learning are accelerating nanotag development by enabling predictive control over nanostructure geometry, composition, and optical response. SERS nanotags are increasingly being integrated into diagnostic formats, such as lateral flow assays and microfluidic devices, offering both qualitative and quantitative analysis at the point of care. This review provides an overview of key design principles, common strategies for nanostructure functionalization and stabilization, and emerging biosensing applications, serving as a practical guide for researchers seeking to design and implement SERS nanotags. Analyte ; Biosensor ; Microfluidics ; Surface-enhanced Raman spectroscopy ; Raman spectroscopy ; Plasmon ; Nanostructure Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Guanchen Wu | Chung-Yueh Yuan | Kareem Hegazy | Samuel M. Blau | Teresa Head-Gordon
Source: arXiv (Cornell University) (preprint, 2026-03-22)
Field: Materials Science — Materials Chemistry

Machine-Learned Leftmost Hessian Eigenvectors for Robust Transition State Finding The reliable determination of transition states (TSs) benefits from second-order information for robust convergence and validation, but the computational expense of Hessians prohibits their routine use in TS optimization. Here, we present a machine-learning-driven TS optimizer that directly predicts the leftmost Hessian eigenvector (LMHE), the critical mode that locally approximates the reaction coordinate encompassing the TS. We demonstrate that our LMHE optimizer recovers TS solutions at the same rate as full Hessian optimizers, and more robustly from degraded initial guess geometries, thereby eliminating the excessively long wall times characteristic of full-Hessian approaches and reducing total gradient evaluations compared to standard quasi-Newton methods. We further improve accuracy and robustness using uncertainty quantification for identifying occasional LMHE prediction failures, that then falls back to a full Hessian update from the machine learned potential at that optimization step, avoiding expensive active learning. Overall our methodology and semi-automated workflow delivers second-order stability at first-order computational expense to provide a highly efficient engine for high-throughput reaction discovery. Hessian matrix ; Robustness (evolution) ; Eigenvalues and eigenvectors ; Convergence (economics) ; Stability (learning theory) ; Hessian equation ; Rate of convergence Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Neural Networks and Reservoir Computing
Authors: M. Fatih Ergin | Hasniye Yaşa | Hülya Çelik Onar
Source: RSC Advances (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Green manufacturing of amoxicillin trihydrate: a malic acid-assisted crystallization framework enhanced by Taguchi–ANN optimization Industrial production of amoxicillin trihydrate (AMCT) often suffers from low yield, impurity inclusion, and inconsistent crystal morphology. This study introduces a scalable green crystallization strategy using malic acid as a biodegradable habit modifier, developed as part of an improved eco-friendly AMCT manufacturing framework. A hybrid optimization approach integrating Taguchi design with Artificial Neural Network (ANN) modeling was employed to capture both linear and nonlinear interactions among critical process variables. Multi-technique characterization (XRD, FTIR, DSC, BET, LC-MS) confirmed that malic acid preserves lattice integrity while substantially refining particle attributes, reducing crystallite size from 85.9 to 66.4 nm and increasing specific surface area from 5.27 to 11.07 m<sup>2</sup> g<sup>-1</sup>. This significant increase in surface area is a key physical factor theoretically favoring improved dissolution kinetics. The ANN model exhibited excellent predictive performance (<i>R</i> <sup>2</sup> > 0.99) for both purity and yield. Under optimized conditions (2.5 M malic acid, pH 5.5, 60 min, 1500 rpm), AMCT crystals were obtained with 99.21% purity and 61.82% yield. These results demonstrate a robust, data-driven framework for sustainable AMCT production, providing a high-performance alternative to conventional mineral-acid-based crystallization methods. Crystallization ; Amoxicillin ; Malic acid ; Crystal (programming language) ; Quality (philosophy) Crystallization and Solubility Studies ; Drug Solubulity and Delivery Systems ; Food Chemistry and Fat Analysis
Authors: Yuan Fang | Ipen Demirel | Xiaopu Zhang | Yuchuan Shao | Jianda Shao | John J. Boland
Source: arXiv (Cornell University) (preprint, 2026-03-02)
Field: Materials Science — Materials Chemistry

Pathway to lowest-energy structures for the surface triple junction verified by machine learning The behavior of surface triple junctions (STJ) at emergent grain boundaries on free surfaces are critical contributors to material properties, particularly at the nanoscale. Interest has been stimulated by recent experimental observations of local grain rotation, the restructuring of emergent grain boundaries and the resultant levels of stress generation, and computationally by the introduction of the core-shift method for building wedge disclination structures at STJ and the establishment of relationships between atomic calculation and the continuum elastic theory. Despite this progress, the lowest-energy structure for the surface triple junction is still unknown. Here, we carried out the experimental observation of the local surface deformation at STJ, the local stress analysis and ergodic searches for metastable structures. We identified the zipped Y-shaped notch, rather than the zipped V-shaped notch or wedge disclination, as the most stable structures, which was subsequently verified with machine learning methods for a wide range of boundaries. Local stress analysis explains this energetic preference. Our findings provide fundamental insight into STJ energetics and offer a possible framework for understanding kinetically diffusive deformation and the pathway for engineering related material properties, in particular thin-film properties. Triple junction ; Wedge (geometry) ; Metastability ; Grain boundary ; Disclination ; Deformation (meteorology) Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Microstructure and mechanical properties
Authors: Sreeraj Rajan Warrier | Jayasri Dontabhaktuni
Source: Applied Physics A (article, 2026-03-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hybrid Quantum-Classical Inverse Design of Metasurfaces for Tailored High Q-factor Response Inverse ; Inverse problem ; Inverse scattering problem ; Frequency response Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Advanced Antenna and Metasurface Technologies