jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Janisch, John | Le, Duy | Rahman, Talat S.
Source: arXiv (Cornell University) (preprint, 2025-12-12)
Field: Materials Science — Materials Chemistry

Machine learned potential for defected single layer hexagonal boron nitride Development of machine learned interatomic potentials (MLIP) is critical for performing reliable simulations of materials at length and time scales that are comparable to those in the laboratory. We present here a MLIP suitable for simulations of the temperature dependent structure and dynamics of single layer hexagonal boron nitride (h-BN) with defects and grain boundaries, developed using a strictly local equivariant deep neural network as formulated in the Allegro code. The training dataset consisted of about 30,000 images of h-BN with and without point defects generated with ab-initio molecular dynamics simulations, based on density functional theory (DFT), at 500, 1000, and 1500K. The developed MLIP predicts potential energies and forces with a mean absolute error (MAE) of 4 meV/atom and 60 meV/Angstrom , respectively. It also reproduces phonon dispersion curves and density of vibrational states of pristine bulk h-BN that are comparable with that obtained from density functional theory-based calculations. Molecular dynamics simulations of the motion of the 4|8 grain boundary unit in h-BN shows the first step to have an activation barrier ~2.2 eV, indicating immobility of the grain boundary. Moving the grain boundary units past the first shows much lower activation barriers of ~0.42eV, suggesting a facile motion of the grain boundary once the first movement is stimulated. These simulations yield a scaled mobility of 1.739*10^(-11) m^3/Js for a temperature of 1500K which, given the inherent differences in the set-ups, is not too far from the experimental value of 1.36*10^(-9) m^3/Js. The ability to predict grain boundary mobility within reasonable agreement with experiment demonstrates the robustness of the MLIP and its suitability for reliable simulations of defect structures and dynamics in single layer h-BN. Grain boundary ; Molecular dynamics ; Interatomic potential ; Boron nitride ; Yield (engineering) ; Dispersion (optics) ; Density functional theory ; Phonon ; Boundary (topology) Machine Learning in Materials Science ; Thermal properties of materials ; Graphene research and applications
Authors: Janisch, John | Le, Duy | Rahman, Talat S.
Source: ArXiv.org (article, 2025-12-12)
Field: Materials Science — Materials Chemistry

Machine learned potential for defected single layer hexagonal boron nitride Development of machine learned interatomic potentials (MLIP) is critical for performing reliable simulations of materials at length and time scales that are comparable to those in the laboratory. We present here a MLIP suitable for simulations of the temperature dependent structure and dynamics of single layer hexagonal boron nitride (h-BN) with defects and grain boundaries, developed using a strictly local equivariant deep neural network as formulated in the Allegro code. The training dataset consisted of about 30,000 images of h-BN with and without point defects generated with ab-initio molecular dynamics simulations, based on density functional theory (DFT), at 500, 1000, and 1500K. The developed MLIP predicts potential energies and forces with a mean absolute error (MAE) of 4 meV/atom and 60 meV/Angstrom , respectively. It also reproduces phonon dispersion curves and density of vibrational states of pristine bulk h-BN that are comparable with that obtained from density functional theory-based calculations. Molecular dynamics simulations of the motion of the 4|8 grain boundary unit in h-BN shows the first step to have an activation barrier ~2.2 eV, indicating immobility of the grain boundary. Moving the grain boundary units past the first shows much lower activation barriers of ~0.42eV, suggesting a facile motion of the grain boundary once the first movement is stimulated. These simulations yield a scaled mobility of 1.739*10^(-11) m^3/Js for a temperature of 1500K which, given the inherent differences in the set-ups, is not too far from the experimental value of 1.36*10^(-9) m^3/Js. The ability to predict grain boundary mobility within reasonable agreement with experiment demonstrates the robustness of the MLIP and its suitability for reliable simulations of defect structures and dynamics in single layer h-BN. Grain boundary ; Molecular dynamics ; Interatomic potential ; Boron nitride ; Yield (engineering) ; Dispersion (optics) ; Density functional theory ; Phonon ; Boundary (topology) Machine Learning in Materials Science ; Thermal properties of materials ; Graphene research and applications
Authors: Kawther K. Younus | Marwah Ghazi Kareem | Hadi Hasan Mussttaf
Source: Open Engineering (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Hybrid mechanics-informed machine learning models for predicting mechanical failure in graphene sponge: a low-data strategy for mechanical engineering applications Abstract Graphene sponge – a structure derivative of graphene that is porous and light-weight – exhibits properties including mechanical and structurally features that are exciting for advanced utilization in a wide variety of engineered applications. As such, the challenging aspect of graphene sponge is accurately inferring its failure strength since it possesses complex hierarchical morphology with limited experimental data. In this study we propose a hybrid approach to predict 3D graphene sponge mechanical failure of products using a new method of taking experimental physical measurements, complemented with machine learning models – Gaussian Process Regression (GPR) and Bayesian Ridge Regression (BRR). To formulate mechanical failure predictions we have characterized our fabricated samples using Raman, X-ray diffraction (XRD), BET surface analysis, nanoindentation and tensile testing methods. For GPR and BRR models, we created and engineered features to enrich the mechanical failure model predictions. GPR performed better than BRR with R 2 = 0.967 and MAE = 0.192 MPa (versus 0.953 and 0.220 MPa) based on R 2 and MAE results. The results provided evidence that AI-based models informed with physics can be productive in low-data mechanical predictions, which supports intelligent design of nanostructured materials. Graphene ; Nanoindentation ; Gaussian process ; Ultimate tensile strength ; Probabilistic logic ; Kriging ; Mechanical failure ; Multiphysics Machine Learning in Materials Science ; Graphene research and applications ; Gaussian Processes and Bayesian Inference
Authors: Mehmet Tunçel | Hasan Kurban | Erchin Serpedin | Mustafa Kurban
Source:  (article, 2025-05-20)
Field: Chemistry — Organic Chemistry

Predicting Optical Bandgaps in C<sub>60</sub> and Functionalized Derivatives from Limited Data for Renewable Energy Applications Accurate optical bandgap prediction is essential for advancing renewable energy materials, but data scarcity and complex non-linear dependencies limit traditional approaches, especially for C<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">60</inf>-based materials in organic photovoltaics. We propose a novel data-efficient machine learning framework that integrates Gaussian Process Regression (GPR) with Density Functional Theory (DFT) simulations to predict absorbance spectra and compute optical bandgaps from limited data. Our approach leverages Bayesian inference for uncertainty quantification, significantly reducing reliance on computationally expensive DFT calculations while maintaining accuracy. We demonstrate that our method generalizes across $\mathbf{C}_{60}$ and functionalized derivatives, enabling scalable and cost-effective bandgap prediction with minimal training samples. Renewable energy ; Computer science ; Optical materials ; Energy (signal processing) ; 3D optical data storage ; Materials science ; Optoelectronics ; Process engineering ; Electrical engineering ; Engineering ; Physics Fullerene Chemistry and Applications
Authors: Hassan Adel Almousa | Hugo G. De Luca | Anthony S. David | Emile S. Greenhalgh | Alexander Bismarck | Milo S. P. Shaffer
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Uniform and Scalable Carbon Nanotube Growth on Carbon Fibers: Insights from Experimental Dynamic Snapshot and Computational Fluid Dynamics Carbon nanotube ; Chemical vapor deposition ; Snapshot (computer storage) ; Gravimetric analysis ; Scalability ; Nanomanufacturing ; Ultimate tensile strength ; Nanoscopic scale Carbon Nanotubes in Composites ; Nanopore and Nanochannel Transport Studies ; Diamond and Carbon-based Materials Research
Authors: Sebastien Röcken (21671087) | Julija Zavadlav (1333341)
Source: Figshare (article, 2025-07-07)
Field: Materials Science — Materials Chemistry

Enhancing MachineLearning Potentials through TransferLearning across Chemical Elements Machine learning potentials (MLPs) can enable simulations of ab initio accuracy at orders of magnitude lower computational cost. However, their effectiveness hinges on the availability of considerable data sets to ensure robust generalization across chemical space and thermodynamic conditions. The generation of such data sets can be labor-intensive, highlighting the need for innovative methods to train MLPs in data-scarce scenarios. Here, we introduce transfer learning of potential energy surfaces between chemically similar elements. Specifically, we leverage the trained MLP for silicon to initialize and expedite the training of an MLP for germanium. Utilizing classical force field and ab initio data sets, we demonstrate that transfer learning surpasses traditional training from scratch in force prediction, leading to more stable simulations and improved temperature transferability. These advantages become even more pronounced as the training data set size decreases. We also observe positive transfer learning effects for most out-of-target properties. Our findings demonstrate that transfer learning across chemical elements is a promising technique for developing accurate and numerically stable MLPs, particularly in a data-scarce regime. Transfer of learning ; Leverage (statistics) ; Generalization ; Ab initio ; Field (mathematics) ; Training set ; Chemical space ; Experimental data ; Data point ; Transfer of training Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Advanced Electron Microscopy Techniques and Applications
Authors: Siddhartha Roy | Susmita Biswas | Nitu Saha | Ahona Ghosh | Rangan Sarkar | Raisa Santra
Source:  (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

AI-Driven Photoluminescence Modeling and Experimental Study of Luminescent Silicon Quantum Dot Ink for Quantum Communication Applications Luminescent materials based on silicon are of good quality and at the same time, they can be used for integrated photonic systems meant for quantum communication. The present paper deals with the study of silicon luminescence from quantum dot ink as a quantum emitter. Moreover, it provides a model for the inked photoluminescence by employing machine learning. Four different regression models were used for the evaluation - MLP, Random Forest, Decision Tree, and Linear Regression. The optimised model, Random Forest got the highest accuracy based on MAE, MSE, and $\mathbf{R}^{\mathbf{2}}$ scores. This combination of quantum photonics and AI modeling gives an idea of the emission dynamics and also facilitates the invention of fast and efficient nextgeneration quantum communication technologies. Quantum dot ; Luminescence ; Photoluminescence ; Silicon ; Photonics ; Quantum Silicon Nanostructures and Photoluminescence ; Photonic and Optical Devices ; Semiconductor Quantum Structures and Devices
Authors: Ömer Mermer | Ayşe Gül Filik | Gökhan Filik
Source: Black Sea Journal of Agriculture (article, 2025-09-11)
Field: Chemistry — Analytical Chemistry

A Big Data-Based Analysis: Color as a Predictor of Silage Quality In this study, the relationship between color parameters and nutrient composition was examined using silage trial data conducted on 10 different plant species or mixtures by the Filik Research Lab. The dataset, comprising a total of 6,254 observations, was analyzed using big data analytics and statistical modeling techniques. Significant associations were identified between color parameters (L*, a*, b*, C*, h°, ΔE, saturation, and hue) and the physical, chemical, and sensory characteristics of the silage (crude protein, NDF, ADF, TDN, NFC). Initially, exploratory data analysis (EDA) and correlation matrices were employed to observe preliminary relationships between color and nutritional components. These were followed by classical and machine learning methods, such as multiple linear regression and Random Forest, to assess the strength and direction of these associations. The analysis revealed a* negative correlation (r = -0.45) between L* (lightness) and crude protein (CP, %), while a strong positive relationship (r = 0.52) was found between b* (yellowness) and NDF. Additionally, a meaningful correlation (r = 0.41) was observed between color saturation and TDN. Random Forest analysis confirmed these findings and highlighted L*, b*, and saturation as the most influential color parameters. These results suggest that color parameters are not only indicators of visual quality but also provide indirect insights into the chemical and nutritional content of silage. This study, conducted within the scope of big data analytics, demonstrates that color data can serve as a* rapid predictive tool, either as an alternative or complement to traditional chemical analyses. This presents a* significant contribution toward the development of digital quality control systems in agricultural production. Silage ; Correlation ; Linear regression ; Regression analysis ; Statistical analysis ; Exploratory data analysis ; Quality (philosophy) ; Regression Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Remote Sensing in Agriculture
Authors: Kumar, Pranav | Körmann, Fritz | Edalati, Kaveh | Grabowski, Blazej | Ikeda, Yuji
Source: ArXiv.org (article, 2025-11-22)
Field: Materials Science — Materials Chemistry

Hydrogen diffusion in TiCr$_2$H$_x$ Laves phases: A combined ab initio and machine-learning-potential study The kinetics of hydrogen diffusion in C15 cubic and C14 hexagonal TiCr$_2$H$_x$ (0 < $x$ <= 4) Laves-phase hydrogen storage alloys is investigated with density functional theory (DFT) and machine learning interatomic potentials (MLIPs). Generalized solid-state nudged elastic band calculations are conducted based on DFT for all symmetrically inequivalent paths between the first-nearest-neighbor face-sharing interstitial sites. The hydrogen migration barriers are substantially higher for the paths that require breaking a Ti-H bond than for those that require breaking a Cr-H bond. Molecular dynamics (MD) simulations with the MLIPs also demonstrate that hydrogen migration occurs more frequently within the hexagonal rings made of the A$_2$B$_2$ interstitial paths, each requiring the breaking of Cr-H bonds, than along the inter-ring paths. The diffusion coefficients of hydrogen obtained from the MD simulations reveal a non-monotonic dependence on hydrogen concentration, which is more pronounced at lower temperatures. Time-averaged radial distribution functions of hydrogen further show that hydrogen avoids face-sharing positions during diffusion and that the hydrogen occupancy at the second-nearest-neighbor edge-sharing positions increases with increasing hydrogen concentration. The diffusion coefficients of hydrogen within 400-1000 K follow an Arrhenius relationship, with activation barriers consistent with most experimental values. One-order of magnitude overestimation of diffusion coefficients compared with some experiments suggests a substantial impact of hydrogen trapping by defects such as Cr vacancies and Ti anti-sites in non-stoichiometric TiCr$_2$ in experiments. Hydrogen ; Diffusion ; Ab initio ; Interstitial defect ; Density functional theory ; Arrhenius equation ; Hydrogen atom ; Spin isomers of hydrogen Hydrogen Storage and Materials ; Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science
Authors: Masato Kotsugi
Source: Vacuum and Surface Science (article, 2025-06-09)
Field: Materials Science — Materials Chemistry

An Explainable AI for Magnetic Function Analysis : Extended Free Energy Model Microscopic image plays a crucial information in functional design, particularly in data-driven materials science. However, extracting meaningful insights from imaging data remains a challenge. This study proposes the Extended Landau Free Energy Model, which integrates persistent homology and machine learning to quantitatively analyze magnetization reversal and pinning mechanisms in nanoscale materials. Using persistent homology, we extract topological features from domain structures and apply principal component analysis (PCA) to construct an energy landscape that describes magnetization reversal dynamics. Ridge regression is employed to correlate extracted features with magnetostatic and exchange energies, enabling a decomposition of energy barriers into their respective interactions. Our results clarify the pinning mechanism by quantifying the contributions of static and exchange interactions, and visualizing their spatial distribution via Hadamard product-based analysis. This method provides a detailed representation of pinning and depinning processes, revealing how structural heterogeneities influence energy barriers. Beyond magnetism, this framework offers broad applications in nanotechnology and data-driven materials science. Function (biology) ; Computer science ; Biology Machine Learning in Materials Science ; Theoretical and Computational Physics ; Magnetic properties of thin films
Authors: Karim Ali Shah | Rodrigo Q. Albuquerque | Christian Brütting | Marcel Dippold | Holger Ruckdäschel
Source: EPub Bayreuth (University of Bayreuth) (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Low-density polyamide 12 foams using Bayesian optimization and inverse design This study introduces a novel, comprehensive approach to optimizing and designing batch foaming of low-density polyamide 12 (PA-12) using advanced machine learning (ML) techniques. Bayesian optimization was used to minimize the foam density, which decreased from approximately 900 to 150 kg/m3 in a single new experiment. A PA-12 foam density of 50 kg/m3, the lowest achieved, was recorded. In addition, an inverse design approach was used to check the robustness of the model by identifying the specific processing parameters required to achieve the desired foam density. Finally, PA-12 foams with similar densities but different processing parameters were obtained using ML. The study highlights the effectiveness of integrating these ML methodologies in the development of lightweight, high-performance polymer foams, which is much more sustainable than traditional methods for achieving low-density foams. Polyamide ; Inverse ; Robustness (evolution) ; Bayesian optimization ; Bayesian probability ; Inverse problem Polymer Foaming and Composites ; Injection Molding Process and Properties ; Polymer Science and PVC
Authors: Johny Lijo | Johannes Schneider | Jonas Bokstaller | Alexander Okupnik
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Framework for Joint application of Machine Learning and Design of experiments in industry: A case study on designing coring tools Coring ; Joint (building) ; Design of experiments ; New product development ; Core (optical fiber) ; Product design ; Machining Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Advanced machining processes and optimization
Authors: Vinoth Suryanarayanan | Pramod Sankar | V Shankaranarayanan | S. Bhuvaneswari
Source:  (article, 2025-03-28)
Field: Chemistry — Analytical Chemistry

ExplainableAI with Transfer Learning for Crop Disease Assessment Using Thermal Imaging The process of maintaining crop to preserve its health naturally is responsible and challenging task in this era of chemical pesticides/insecticides. So, crop management before it is affected by any disease can be a crucial step ahead to avoid the overuse of chemicals. This current work deals with the application of plant disease prediction from thermal images. For this purpose, the popular transfer learning model, EfficientNetB0 utilized for multi-label classification problem. Normal image classification causes more errors than thermal image classification because of blurry images, no clear visuals of the leaves, temperature variations that can't be determined, etc. So EfficientNetB0 model is used which is more balanced, accurate, and efficient. This EfficientNetB0 model combines XAI for a better understanding of the model and gives a higher rate of accuracy than other traditional models. This study highlights the combination of a deep learning model and thermal imaging technique to build a strong, non-invasive mechanism for crop disease management. Computer science ; Transfer of learning ; Crop ; Artificial intelligence ; Agronomy Spectroscopy and Chemometric Analyses ; Infrared Thermography in Medicine ; Smart Agriculture and AI
Authors: Ryoko Oishi‐Tomiyasu
Source: ArXiv.org (preprint, 2025-06-10)
Field: Materials Science — Materials Chemistry

Piecewise-linear embeddings of the space of 3D lattices into $\RR^{13}$ for high-throughput handling of lattice parameters We present two methods to continuously and piecewise-linearly parametrize rank-3 lattices by vectors of $\RR^{13}$, which provides an efficient way to judge if two sets of parameters provide nearly identical lattices within their margins of errors. Such a parametrization can be used to speed up scientific computing involving periodic structures in $\RR^3$ such as crystal structures, which includes database querying, detection of duplicate entries, and structure generation via deep learning techniques. One gives a novel application of Conway's vonorms and conorms, and another is achieved through a natural extension of Ry{\u s}hkov's $C$-type to the setting modulo $3$. Voronoi vectors modulo 3 obtained in the latter approach provide an algorithm for enumerating of all potential isometries under perturbations of lattice parameters. Modulo ; Lattice (music) ; Voronoi diagram ; Parametrization (atmospheric modeling) ; Space (punctuation) ; Discrete space ; Vector space Machine Learning in Materials Science ; Quasicrystal Structures and Properties ; Mathematical Approximation and Integration
Authors: Maziyar Sabet
Source: Materials Chemistry and Physics (article, 2025-10-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Defect-engineered rGO–NiCo2O4 hybrids: Eco-Friendly synthesis, interfacial chemistry, and reproducibility via bayesian optimization Graphene ; Bayesian optimization ; Raman spectroscopy ; X-ray photoelectron spectroscopy ; Capacitance ; Electrode ; Metrology ; Oxide Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Aerogels and thermal insulation