jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Muhammad Zahid | Hafiz Muhammad Usman | Komal Aslam | Muhammad Shafique | MS KHAN | Nida Mahreen | Muhammad Umar Raza | Faiza Masood | Muhammad Bilal | Muhammad Ahsan Raza
Source: Physical Education Health and Social Sciences (article, 2025-11-05)
Field: Materials Science — Biomaterials

Technological Interventions and Disease Management Strategies for Enhancing Postharvest Quality of Horticultural Commodities Postharvest losses in horticultural commodities remain a major bottleneck in ensuring food and nutritional security worldwide. Fruits and vegetables, being highly perishable, suffer considerable quantitative and qualitative degradation due to physiological disorders, microbial decay, and inefficient postharvest handling. This review synthesizes current advancements in technological and biological interventions aimed at minimizing such losses and extending shelf life. Key strategies include pre-cooling, cold storage, and refrigerated transport as integral components of an optimized cold chain system, alongside controlled and modified atmosphere storage for maintaining product freshness. Edible coatings and biodegradable packaging materials have emerged as sustainable alternatives to synthetic plastics, offering improved moisture regulation and pathogen suppression. Non-thermal processing techniques such as UV-C irradiation and high-pressure treatments help retain nutritional and sensory quality, while smart sensors, machine learning, and IoT-enabled systems are revolutionizing cold-chain monitoring and decision-making. Integrated disease management approaches combining biological control agents, GRAS compounds, essential oils, and cultural practices present eco-friendly substitutes for synthetic fungicides. Collectively, these innovations contribute to improving product quality, marketability, and consumer safety while promoting environmental sustainability. The review highlights the need for scalable, cost-effective, and farmer-accessible technologies to ensure the broad adoption of sustainable postharvest solutions in both developed and developing economies. Postharvest ; Product (mathematics) ; Food security ; Bottleneck ; Cold chain ; Quality (philosophy) ; Production (economics) ; Shelf life ; Food processing Nanocomposite Films for Food Packaging ; Postharvest Quality and Shelf Life Management ; Listeria monocytogenes in Food Safety
Authors: Maimuna
U. Zarewa (22312885) | Tawfik A. Saleh (9500563)
Source: Figshare (article, 2025-09-25)
Field: Materials Science — Materials Chemistry

Refinery-Waste-DerivedCarbon Quantum Dots for SelectiveMetal Ion Fluorescence Sensing: Experimental, DFT, and Deep LearningInsights In this study, we present a sustainable approach for synthesizing carbon quantum dots (CQDs) from petroleum refinery waste, transforming industrial residues into high-performance optical nanomaterials. Three different CQDs samples (QVR1, QVR2, and QVR3) were synthesized and extensively characterized using transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR). Optical properties, assessed via UV–vis absorption and photoluminescence (PL), revealed strong and selective fluorescence enhancement in response to 30 environmentally relevant metal cations and anions. Specifically, QVR1, QVR2, and QVR3 exhibited selective and rapid interactions with Na<sup>+</sup> (half-life = 11.33 s), Cd<sup>2+</sup> (<i>t</i><sub>1/2</sub> = 7.38 s), and K<sup>+</sup> (<i>t</i><sub>1/2</sub> = 17.32 s), respectively. Simplified Molecular Input Line Entry System (SMILES) was extracted using density functional theory (DFT) by RDKit and Open Babel, and combined with experimental PL data and binding energy values in a deep learning framework. The Graph Convolutional Network (GCN) model demonstrated superior predictive performance over ChemBERT, achieving an overall accuracy of 83.3% and a 42.9% recall for minority classes, compared to ChemBERT’s 80.6% accuracy and 0% recall for minority classes. This work highlights the synergistic use of experimental, theoretical, and AI-driven cheminformatics approaches for understanding nanoscale sensing behaviors and establishes refinery-derived CQDs as promising low-cost materials for selective and sensitive optical sensing. Photoluminescence ; Quantum dot ; Fluorescence ; X-ray photoelectron spectroscopy ; Spectroscopy ; Density functional theory ; Convolutional neural network ; Fourier transform infrared spectroscopy ; Hyperspectral imaging Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Electrochemical sensors and biosensors
Authors: Ashkan Shakarami | Yousef Yeganeh | Azade Farshad | Lorenzo Nicolè | Stefano Ghidoni | Nassir Navab
Source: ArXiv.org (preprint, 2025-07-31)
Field: Materials Science — Materials Chemistry

Stress-Aware Resilient Neural Training This paper introduces Stress-Aware Learning, a resilient neural training paradigm in which deep neural networks dynamically adjust their optimization behavior - whether under stable training regimes or in settings with uncertain dynamics - based on the concept of Temporary (Elastic) and Permanent (Plastic) Deformation, inspired by structural fatigue in materials science. To instantiate this concept, we propose Plastic Deformation Optimizer, a stress-aware mechanism that injects adaptive noise into model parameters whenever an internal stress signal - reflecting stagnation in training loss and accuracy - indicates persistent optimization difficulty. This enables the model to escape sharp minima and converge toward flatter, more generalizable regions of the loss landscape. Experiments across six architectures, four optimizers, and seven vision benchmarks demonstrate improved robustness and generalization with minimal computational overhead. The code and 3D visuals will be available on GitHub: https://github.com/Stress-Aware-Learning/SAL. Robustness (evolution) ; Maxima and minima ; Artificial neural network ; Training (meteorology) ; Generalization ; Noise (video) Machine Learning in Materials Science ; Stochastic Gradient Optimization Techniques ; Advanced Neural Network Applications
Authors: Neha Meriya Binu | Rahul Krishnan Pathinarupothi
Source:  (article, 2025-12-12)
Field: Materials Science — Materials Chemistry

Machine Learning Based Prediction of Biomaterial Hardness for Medical Devices Medical-grade biomaterials need to be accurately characterized for their mechanical properties, with hardness being a prominent selection criterion for clinical applications. Well-established methods, such as Vickers and Brinell hardness tests, while common, are generally time-consuming and dependent on expert equipment, posing problems during initial material screening. This paper presents a machine learning approach to predict hardness based on a small number of readily available material properties, such as tensile strength, elongation at break, and density. The dataset comprises 25 biocompatible materials with six features selected from MatWeb. Linear Regression, Ridge Regression, Random Forest, Gradient boosting, and XG-Boost models were implemented, incorporating feature reduction through Principal Component Analysis (PCA). This enables a fast, data-driven prediction of Vickers and Brinell hardness, with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R^{2}$</tex> values of 0.82 and 0.83, respectively. Furthermore, once the value of one of these types of hardness, Brinell or Vickers, is available or predicted, the value of the other can be deduced with great accuracy. In particular, Vickers hardness was predicted from Brinell hardness and density by using Ridge Regression with an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R^{2}$</tex> value of 0.96. Brinell scale ; Vickers hardness test ; Ridge ; Random forest ; Feature selection ; Ultimate tensile strength ; Feature (linguistics) ; Principal component analysis ; Elastic modulus Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Metal and Thin Film Mechanics
Authors: Siemann, Gesa-R. | Curcio, Davide | Mortensen, Anders S. | Sanders, Charlotte E. | Zhang, Yu | Rigden, Jennifer | Majchrzak, Paulina | Biswas, Deepnarayan | Springate, Emma | Singha, Ratnadwip | Schoop, Leslie M. | Hofmann, Philip
Source: Open MIND (preprint, 2025-11-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Clustering-Enhanced Time- and Angle-Resolved Photoemission Study of LaTe$_3$: Absence of a Photoinduced Secondary CDW in the Electronic Structure Optical control offers a compelling route for tailoring material properties on an ultrafast time scale. Ordered states such as charge density waves (CDWs) can be transiently melted by an ultrafast light excitation. This is also the case for the rare-earth tritelluride LaTe$_3$, a prototypical CDW compound. For this material it has recently been reported that the suppression of the primary CDW allows the transient formation of a second CDW, whose wave vector is orthogonal to the primary one. This creates the intriguing scenario where light enables switching between two distinct ordered phases of the material. While the second CDW has so far been observed by structural techniques, it remains an open question how the interplay of the two CDW phases is reflected in the material's electronic structure. We investigate this via time- and angle-resolved photoemission measurements of LaTe$_3$. The complex Fermi contour is probed using a FeSuMa analyzer, which records the photoemission intensity of the entire Fermi contour at once. The dynamics revealed by the FeSuMa analyzer are complemented by measurements using a conventional hemispherical electron analyzer. We combine conventional data analysis with $k$-means clustering, an unsupervised machine learning technique, demonstrating its strong potential for disentangling large datasets. While we do not find any features that cannot be explained by the melting and re-establishment of the primary CDW, distinct dynamics and coherent oscillations are observed in the different branches of the Fermi contour. Ultrashort pulse ; Charge density wave ; Electronic structure ; Fermi Gamma-ray Space Telescope ; Photoemission spectroscopy ; Fermi level ; Angle-resolved photoemission spectroscopy Iron-based superconductors research ; Organic and Molecular Conductors Research ; Inorganic Chemistry and Materials
Authors: Dimitrios Lezos | Dimitrios Vogiatzis
Source:  (article, 2025-06-18)
Field: Chemistry — Spectroscopy

Identification of Novel Drug-Drug Interactions as Out-of-Distribution Samples This work proposes a novel approach to predicting side effects in drug-drug interactions (DDIs) by identifying them as Out of Distribution (OoD) samples. The rise of polypharmacy has increased both the number of drug combinations administered to patients and the risk of DDIs, many such interactions remain undocumented in clinical trials or medical records, posing a significant challenge for patient safety. While traditional DDI prediction is typically framed as a supervised machine learning problem, novel side effects lack labeled data. To address this, we introduce a hybrid approach that combines diverse drug representations with OoD detection to capture unseen interactions. Our method integrates multiple drug embeddings generated from diverse characteristics, including biomedical texts, molecular structures, knowledge graphs and similarity profiles to generate rich latent representations that enable us to predict the presence of novel DDIs as OoD samples. Drug ; Identification (biology) ; Computer science ; Distribution (mathematics) ; Pharmacology ; Medicine ; Mathematics ; Biology Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals ; Receptor Mechanisms and Signaling
Authors: Siemann, Gesa-R. | Curcio, Davide | Mortensen, Anders S. | Sanders, Charlotte E. | Zhang, Yu | Rigden, Jennifer | Majchrzak, Paulina | Biswas, Deepnarayan | Springate, Emma | Singha, Ratnadwip | Schoop, Leslie M. | Hofmann, Philip
Source: ArXiv.org (article, 2025-11-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Clustering-Enhanced Time- and Angle-Resolved Photoemission Study of LaTe$_3$: Absence of a Photoinduced Secondary CDW in the Electronic Structure Optical control offers a compelling route for tailoring material properties on an ultrafast time scale. Ordered states such as charge density waves (CDWs) can be transiently melted by an ultrafast light excitation. This is also the case for the rare-earth tritelluride LaTe$_3$, a prototypical CDW compound. For this material it has recently been reported that the suppression of the primary CDW allows the transient formation of a second CDW, whose wave vector is orthogonal to the primary one. This creates the intriguing scenario where light enables switching between two distinct ordered phases of the material. While the second CDW has so far been observed by structural techniques, it remains an open question how the interplay of the two CDW phases is reflected in the material's electronic structure. We investigate this via time- and angle-resolved photoemission measurements of LaTe$_3$. The complex Fermi contour is probed using a FeSuMa analyzer, which records the photoemission intensity of the entire Fermi contour at once. The dynamics revealed by the FeSuMa analyzer are complemented by measurements using a conventional hemispherical electron analyzer. We combine conventional data analysis with $k$-means clustering, an unsupervised machine learning technique, demonstrating its strong potential for disentangling large datasets. While we do not find any features that cannot be explained by the melting and re-establishment of the primary CDW, distinct dynamics and coherent oscillations are observed in the different branches of the Fermi contour. Ultrashort pulse ; Charge density wave ; Electronic structure ; Fermi Gamma-ray Space Telescope ; Photoemission spectroscopy ; Fermi level ; Angle-resolved photoemission spectroscopy Iron-based superconductors research ; Organic and Molecular Conductors Research ; Inorganic Chemistry and Materials
Authors: Korbinian Liebl (4602382) | Gregory A. Voth (246826)
Source: Figshare (article, 2025-04-17)
Field: Materials Science — Materials Chemistry

A Hybrid Bottom-Upand Data-Driven Machine LearningApproach for Accurate Coarse-Graining of Large Molecular Complexes Bottom-up coarse-graining refers to the development of low-resolution simulation models that are thermodynamically consistent with certain distributions from fully atomistic simulations. Force-matching and relative entropy minimization represent two major, frequently applied methods that allow to develop such bottom-up models. Nevertheless, atomistic simulations can often provide only limited sampling of the phase space. For bottom-up coarse-graining, these limitations may result in overfitting of the atomistic reference data, especially for large molecular complexes, where the learning may be agnostic of the actual affinities between binding partners. As a solution to this problem, we devise a data-driven machine learning hybrid coarse-graining concept that represents a regularized version of the relative entropy minimization approach. We demonstrate that this new approach allows one to develop coarse-grained models for molecular complexes that reproduce the targeted binding affinity but also describe the underlying complex structure accurately. The trained models therefore show diverse behavior as they can undergo frequent unbinding and binding events and are also transferable for simulating entire protein lattices, e.g., for a virus capsid. Overfitting ; Binding affinities ; Minification ; Entropy (arrow of time) ; Affinities ; Kullback–Leibler divergence ; Principle of maximum entropy ; Molecular dynamics Block Copolymer Self-Assembly ; Protein Structure and Dynamics ; Machine Learning in Materials Science
Authors: Daviti Gochitashvili | Maxwell Meyers | Chenji Wang | Aleksey N. Kolmogorov
Source: ArXiv.org (preprint, 2025-07-18)
Field: Materials Science — Materials Chemistry

Improving structure search with hyperspatial optimization and TETRIS seeding Advanced structure prediction methods developed over the past decades include an unorthodox strategy of allowing atoms to displace into extra dimensions. A recently implemented global optimization of structures from hyperspace (GOSH) has shown promise in accelerating the identification of global minima on potential energy surfaces defined by simple interatomic models. In this study, we extend the GOSH formalism to more accurate Behler-Parrinello neural network (NN) potentials, make it compatible with efficient local minimization algorithms, and test its performance on nanoparticles and crystalline solids. For clusters modeled with NN potentials, four-dimensional optimization offers fairly modest improvement in navigating geometric relaxation pathways and incurs increased computational cost largely offsetting the benefit, but it provides a significant advantage in facilitating atom swaps in nanoalloys. In comparison, the introduction of a moderate, controlled bias for generating more physically sensible starting configurations, achieved via TETRIS-inspired packing of atomic blocks, has a more direct impact on the efficiency of global structure searches. The benchmarked systems are Lennard-Jones clusters, Au or Cu-Pd-Ag nanoparticles and binary Sn alloys described by NN potentials, and compounds with covalent B or BC frameworks modeled with density functional theory Maxima and minima ; Global optimization ; Minification ; Energy minimization ; Binary number ; Formalism (music) ; Artificial neural network ; Atom (system on chip) ; Density functional theory Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Yuki Sakai | Yoshiki Iwazaki
Source: Japanese Journal of Applied Physics (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

Structural and dielectric properties of BaTiO<sub>3</sub> nanoparticles Abstract We construct and employ a machine-learning potential to perform molecular dynamics simulations on BaTiO 3 nanoparticles. The potential constructed in this study successfully reproduce rhombohedral, orthorhombic, tetragonal, and cubic phases observed in bulk BaTiO 3 at different simulation temperatures, as well as surface properties. We consider cubic-shaped and spherical nanoparticles to study how the shape and surface structures influence the structural and dielectric properties. Based on our analysis of the displacement vectors of titanium atoms relative to the surrounding oxygen atoms (corresponding to dipole moments), the surface structure strongly affects the order of the displacement vectors even near the center of the particle. A tetragonal-like order appears in cubic-shaped nanoparticles around a diameter of 11.6 nm while a similar order cannot be seen in an 18 nm spherical nanoparticle. Dielectric ; Nanoparticle ; Materials science ; Ferroelectricity ; Nanotechnology ; Chemical engineering ; Optoelectronics ; Engineering Ferroelectric and Piezoelectric Materials ; Electronic and Structural Properties of Oxides ; Microwave Dielectric Ceramics Synthesis
Authors: Alcocer, Yuri
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2025-12-15)
Field: Materials Science — Materials Chemistry

Universal Semantic Coupling: A Fifth Interaction Governing Structure Formation We report the discovery of a universal dimensionless coupling that governs information-geometric back-reaction in structured systems and measure its critical exponent characterizing approach to semantic equilibrium. Hamilton’s principle established that classical mechanics, optics, and thermodynamics are projections of a unified variational structure. We demonstrate this structure extends into information-geometric space through the Canonical Semantic Framework (CSF), which predicts that systems maintaining sustained structure operate near a semantic equilibrium manifold I ≈ 1, where I represents the ratio of information-geometric capacity to physical flux. We test this framework in three radically different domains spanning ten orders of magnitude in timescale: (1) Quantum feedback control: of superconducting qubits yields with counterintuitive scalings inconsistent with standard control theory: optimal feedback gain and recovery time . Crucially, Fisher information stabilizes 80% faster than state-space metrics , demonstrating top-down causation from information geometry to physical observables. (2) Polymer rheology: using experimental xanthan gum frequency-sweep data across five formulations gives , invariant despite threefold variation in elastic modulus and salt concentration. (3) Neural network grokking: dynamics reveal critical scaling with R² > 0.98 as systems approach generalization, identifying I = 1 as a genuine critical manifold with mean-field-like exponent, the first measured semantic critical exponent. The equilibrium couplings agree within 4% (combined ), while the critical exponent γ ≈ 0.73 defines a candidate universality class for semantic phase transitions. These systems span quantum coherent, classical dissipative, and computational regimes with no shared microscopic physics, yet exhibit identical information-geometric structure. We propose semantic equilibrium as a fundamental organizing principle: long-lived structured systems maintain I ≈ 1 via back-reaction with characteristic coupling and universal approach dynamics governed by exponent γ ≈ 0.7-0.8. This represents an effective organizing principle at the information-matter interface, operationally, a fifth interaction, beyond gravity and gauge forces, coupling information geometry to physical dynamics. The framework makes testable predictions for cold atoms, active matter, and cosmological structure formation. Quantum ; Scaling ; Physical system ; Quantum entanglement ; Unitarity ; Quantum system ; Complex system ; Critical exponent ; Qubit Machine Learning in Materials Science ; Quantum many-body systems ; Neural Networks and Reservoir Computing
Authors: Peña-Cano, Pablo | Piaggi, Pablo M.
Source: arXiv (Cornell University) (preprint, 2025-11-18)
Field: Materials Science — Materials Chemistry

Hybrid ab initio and empirical machine learning models for the potential energy surface We propose a methodology to generate hybrid machine learning models for the potential energy surface trained simultaneously on data from ab initio electronic structure calculations and on thermodynamic and/or structural observables from experiment. The approach is based on the use of a loss function that includes the mean square error of observables with respect to their experimental values, in addition to the usual terms involving the mean square error of the energies and forces with respect to ab initio data. We employ a reweighting procedure that allows for the calculation of ensemble averages of observables during training for arbitrary values of the model parameters and on the fly. The method is general and can be applied to any set of static observables. We illustrate the usefulness of this approach by applying it to the generation of hybrid models for liquid water that reproduce accurately the experimental density maximum, the density isobar at 1 bar, and the radial distribution function in molecular dynamics simulations. Observable ; Ab initio ; Function (biology) ; Surface (topology) ; Energy (signal processing) ; Potential energy surface ; Mean squared error ; Statistical ensemble ; Potential energy Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced Chemical Physics Studies