jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aqsa Zulfiqar | Talha Manzoor | Muhammad Bilal Ijaz | Hifza Nawaz | Fayyaz Ahmed | Saeed Akhtar | Faiza Jan Iftikhar | Yasir Nawab | Muhammad Qamar Khan | Muhammad Umar
Source: Textiles (review, 2024-04-22)
Field: Materials Science — Polymers and Plastics

Artificial-Neural-Network-Based Predicted Model for Seam Strength of Five-Pocket Denim Jeans: A Review This study explores previous research efforts concerning prediction models related to the textile and polymer industry, especially garment seam strength, emphasizing critical parameters such as stitch density, fabric GSM, thread type, thread count, stitch classes, and seam types. These parameters play a pivotal role in determining the durability and overall quality of denim jeans based on cellulosic polymer. A significant focus is dedicated to the mathematical computational models employed for predicting seam strength in five-pocket denim jeans. Herein, the discussion poses the application of AI for manufacturing industries, especially for textile and clothing sectors, and highlights the importance of using a machine learning prediction model for sewing thread consumption, seam strength analysis, and seam performance analysis. Therefore, the authors suggest the significant importance of the machine learning prediction model, as future trends anticipate advancements in AI-driven methodologies, potentially leading to high-profile predictions and superior manufacturing processes. The authors also describe the limitation of AI and address a comprehensive model of risk outlines of AI in the manufacturing-based industries, especially the garments industry. Put simply, this review serves as a bridge between the realms of AI, mathematics, and textile engineering, providing a clear understanding of how artificial-neural-network-based models will be shaping the future of seam strength prediction in the denim manufacturing landscape. This type of evolution, based on ANN, will support and enhance the accuracy and efficiency of seam strength predictions by allowing models to discern intricate patterns and relationships within vast and diverse datasets. Denim ; Artificial neural network ; Engineering ; Forensic engineering ; Environmental science ; Computer science ; Artificial intelligence ; Composite material ; Materials science Textile materials and evaluations ; Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality
Authors: Giulia Janzen | C. Smit | Samantha Visbeek | Vincent E. Debets | Chengjie Luo | Cornelis Storm | Simone Ciarella | Liesbeth M. C. Janssen
Source: Physical Review Materials (article, 2024-02-21)
Field: Materials Science — Materials Chemistry

Classifying the age of a glass based on structural properties: A machine learning approach It is well established that physical aging of amorphous solids is governed by a marked change in dynamical properties as the material becomes older. Conversely, structural properties such as the radial distribution function exhibit only a very weak age dependence, usually deemed negligible with respect to the numerical noise. Here we demonstrate that the extremely weak age-dependent changes in structure are, in fact, sufficient to reliably assess the age of a glass with the support of machine learning. We employ a supervised learning method to predict the age of a glass based on the system's instantaneous radial distribution function. Specifically, we train a multilayer perceptron for a model glass former quenched to different temperatures and find that this neural network can accurately classify the age of our system across at least 4 orders of magnitude in time. Our analysis also reveals which structural features encode the most useful information. Overall, this work shows that through the aid of machine learning, a simple structure-dynamics link can, indeed, be established for physically aged glasses. Amorphous solid ; Materials science ; Artificial neural network ; Radial distribution function ; Statistical physics ; Function (biology) ; Artificial intelligence ; Work (physics) ; ENCODE ; Perceptron ; Machine learning ; Biological system ; Computer science ; Molecular dynamics ; Physics ; Thermodynamics ; Crystallography Material Dynamics and Properties ; Thermoregulation and physiological responses
Authors: Patxi Fernandez-Zelaia | Saket Thapliyal | Rangasayee Kannan | Peeyush Nandwana | Yukinori Yamamoto | Andrzej Nycz | Vincent Paquit | Michael Kirka
Source: Additive manufacturing (article, 2024-08-01)
Field: Materials Science — Materials Chemistry

Denoising diffusion probabilistic models for generative alloy design Materials science ; Probabilistic logic ; Diffusion ; Generative grammar ; Alloy ; Metallurgy ; Artificial intelligence ; Thermodynamics ; Computer science Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Additive Manufacturing Materials and Processes
Authors: Soohaeng Yoo Willow | Dong Geon Kim | R. Sundheep | Amir Hajibabaei | Kwang S. Kim | Chang Woo Myung
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Active sparse Bayesian committee machine potential for isothermal–isobaric molecular dynamics simulations Recent advancements in machine learning potentials (MLPs) have significantly impacted the fields of chemistry, physics, and biology by enabling large-scale first-principles simulations. Among different machine learning approaches, kernel-based MLPs distinguish themselves through their ability to handle small datasets, quantify uncertainties, and minimize over-fitting. Nevertheless, their extensive computational requirements present considerable challenges. To alleviate these, sparsification methods have been developed, aiming to reduce computational scaling without compromising accuracy. In the context of isothermal and isobaric ML molecular dynamics (MD) simulations, achieving precise pressure estimation is crucial for reproducing reliable system behavior under constant pressure. Despite progress, sparse kernel MLPs struggle with precise pressure prediction. Here, we introduce a virial kernel function that significantly enhances the pressure estimation accuracy of MLPs. Additionally, we propose the active sparse Bayesian committee machine (BCM) potential, an on-the-fly MLP architecture that aggregates local sparse Gaussian process regression (SGPR) MLPs. The sparse BCM potential overcomes the steep computational scaling with the kernel size, and a predefined restriction on the size of kernel allows for fast and efficient on-the-fly training. Our advancements facilitate accurate and computationally efficient machine learning-enhanced MD (MLMD) simulations across diverse systems, including ice-liquid coexisting phases, Li<sub>10</sub>Ge(PS<sub>6</sub>)<sub>2</sub> lithium solid electrolyte, and high-pressure liquid boron nitride. Molecular dynamics ; Bayesian probability ; Isobaric process ; Machine learning ; Kernel (algebra) ; Computer science ; Bayesian inference ; Statistical physics ; Artificial intelligence ; Physics ; Chemistry ; Computational chemistry ; Mathematics ; Thermodynamics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Niamh O’Neill | Christoph Schran | Stephen J. Cox | Angelos Michaelides
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Biomaterials

Crumbling crystals: on the dissolution mechanism of NaCl in water Dissolution of ionic salts in water is ubiquitous, particularly for NaCl. However, an atomistic scale understanding of the process remains elusive. Simulations lend themselves conveniently to studying dissolution since they provide the spatio-temporal resolution that can be difficult to obtain experimentally. Nevertheless, the complexity of various inter- and intra-molecular interactions require careful treatment and long time scale simulations, both of which are typically hindered by computational expense. Here, we use advances in machine learning potential methodology to resolve at an <i>ab initio</i> level of theory the dissolution mechanism of NaCl in water. The picture that emerges is that of a steady ion-wise unwrapping of the crystal preceding its rapid disintegration, reminiscent of crumbling. The onset of crumbling can be explained by a strong increase in the ratio of the surface area to volume of the crystal. Overall, dissolution comprises a series of highly dynamical microscopic sub-processes, resulting in an inherently stochastic mechanism. These atomistic level insights contribute to the general understanding of dissolution mechanisms in other crystals, and the methodology is primed for more complex systems of recent interest such as water/salt interfaces under flow and salt crystals under confinement. Dissolution ; Mechanism (biology) ; Chemical physics ; Chemistry ; Materials science ; Thermodynamics ; Chemical engineering ; Crystallography ; Mineralogy ; Physics ; Physical chemistry ; Philosophy ; Epistemology ; Engineering Calcium Carbonate Crystallization and Inhibition ; Electrostatics and Colloid Interactions ; Spectroscopy and Quantum Chemical Studies
Authors: Yanxing Wang | Théo Jaffrelot Inizan | Chengwen Liu | Jean‐Philip Piquemal | Pengyu Ren
Source: The Journal of Physical Chemistry B (article, 2024-03-06)
Field: Materials Science — Materials Chemistry

Incorporating Neural Networks into the AMOEBA Polarizable Force Field Neural network potentials (NNPs) offer significant promise to bridge the gap between the accuracy of quantum mechanics and the efficiency of molecular mechanics in molecular simulation. Most NNPs rely on the locality assumption that ensures the model's transferability and scalability and thus lack the treatment of long-range interactions, which are essential for molecular systems in the condensed phase. Here we present an integrated hybrid model, AMOEBA+NN, which combines the AMOEBA potential for the short- and long-range noncovalent atomic interactions and an NNP to capture the remaining local covalent contributions. The AMOEBA+NN model was trained on the conformational energy of the ANI-1x data set and tested on several external data sets ranging from small molecules to tetrapeptides. The hybrid model demonstrated substantial improvements over the baseline models in term of accuracy as the molecule size increased, suggesting its potential as a next-generation approach for chemically accurate molecular simulations. Scalability ; Locality ; Force field (fiction) ; Statistical physics ; Artificial neural network ; Transferability ; Computer science ; Ranging ; Range (aeronautics) ; Amoeba (genus) ; Biological system ; Computational chemistry ; Physics ; Chemistry ; Artificial intelligence ; Machine learning ; Materials science ; Biology Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Kıvanç Sağlık | Siddharth Srinivasan | V Petrov Victor | Xizu Wang | Wei Zhang | Qingyu Yan
Source: Materials Today Energy (article, 2024-09-30)
Field: Materials Science — Materials Chemistry

Machine learning for next-generation thermoelectrics Materials science ; Thermoelectric materials ; Engineering physics ; Nanotechnology ; Systems engineering ; Engineering ; Thermal conductivity ; Composite material Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Gas Sensing Nanomaterials and Sensors
Authors: Fanil T. Latypov | E. V. Fomin | Vasiliy S. Krasnikov | Alexander E. Mayer
Source: Mechanics of Materials (article, 2024-02-18)
Field: Materials Science — Materials Chemistry

Modeling of shock wave propagation in porous magnesium based on artificial neural network Materials science ; Artificial neural network ; Porosity ; Shock wave ; Magnesium ; Shock (circulatory) ; Porous medium ; Mechanics ; Acoustics ; Composite material ; Metallurgy ; Artificial intelligence ; Computer science ; Physics High-Velocity Impact and Material Behavior ; Geotechnical and Geomechanical Engineering ; Microstructure and mechanical properties
Authors: Hugo Rossignol | Michail Minotakis | Matteo Cobelli | Stefano Sanvito
Source: Journal of Chemical Information and Modeling (article, 2024-01-25)
Field: Materials Science — Materials Chemistry

Machine-Learning-Assisted Construction of Ternary Convex Hull Diagrams In the search for novel intermetallic ternary alloys, much of the effort goes into performing a large number of <i>ab initio</i> calculations covering a wide range of compositions and structures. These are essential to building a reliable convex hull diagram. While density functional theory (DFT) provides accurate predictions for many systems, its computational overheads set a throughput limit on the number of hypothetical phases that can be probed. Here, we demonstrate how an ensemble of machine-learning (ML) spectral neighbor-analysis potentials (SNAPs) can be integrated into a workflow for the construction of accurate ternary convex hull diagrams, highlighting regions that are fertile for materials discovery. Our workflow relies on using available binary-alloy data both to train the SNAP models and to create prototypes for ternary phases. From the prototype structures, all unique ternary decorations are created and used to form a pool of candidate compounds. The SNAPs ensemble is then used to prerelax the structures and screen the most favorable prototypes before using DFT to build the final phase diagram. As constructed, the proposed workflow relies on no extra first-principles data to train the ML surrogate model and yields a DFT-level accurate convex hull. We demonstrate its efficacy by investigating the Cu-Ag-Au and Mo-Ta-W ternary systems. Convex hull ; Ternary operation ; Workflow ; Computer science ; Ternary plot ; Diagram ; Phase diagram ; Regular polygon ; Computational science ; Algorithm ; Chemistry ; Mathematics ; Phase (matter) ; Geometry ; Database Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Lanjing Wang | Honghao Chen | Longqi Yang | Jiali Li | Yong Li | Xiaonan Wang
Source: Chemical Engineering Journal (article, 2024-03-24)
Field: Materials Science — Materials Chemistry

Single-atom catalysts property prediction via Supervised and Self-Supervised pre-training models Overfitting ; Machine learning ; Artificial intelligence ; Leverage (statistics) ; Computer science ; Property (philosophy) ; Scalability ; Predictive modelling ; Supervised learning ; Reduction (mathematics) ; Artificial neural network ; Mathematics ; Database Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalytic Processes in Materials Science
Authors: Huang Qin | Hai Zhang | Kai Wang | Xingzi Wang | Weidong Fan
Source: Surface Science (article, 2024-03-11)
Field: Materials Science — Materials Chemistry

Theoretical study of CO2 hydrogenation to methanol on modified Au/In2O3 catalysts: Effects of hydrogen spillover and activation energy prediction for hydrogen transfer Hydrogen spillover ; Spillover effect ; Hydrogen ; Catalysis ; Activation energy ; Chemistry ; Kinetic energy ; Methanol ; Density functional theory ; Work (physics) ; Thermodynamics ; Computational chemistry ; Chemical physics ; Physical chemistry ; Physics ; Organic chemistry ; Quantum mechanics Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Carbon dioxide utilization in catalysis
Authors: Pengjie Shi | Shizhe Feng | Zhiping Xu
Source: Extreme Mechanics Letters (article, 2024-03-19)
Field: Materials Science — Materials Chemistry

Non-equilibrium nature of fracture determines the crack paths Fracture toughness ; Fracture mechanics ; Anisotropy ; Materials science ; Lattice (music) ; Atomic units ; Fracture (geology) ; Stress intensity factor ; Stress field ; Mechanics ; Statistical physics ; Structural engineering ; Physics ; Composite material ; Engineering ; Optics Machine Learning in Materials Science ; Microstructure and mechanical properties ; Graphene research and applications
Authors: Goktug Ercakir | Gokhan Onder Aksu | Seda Keskın
Source: Scientific Reports (article, 2024-10-22)
Field: Chemistry — Inorganic Chemistry

Understanding CO adsorption in MOFs combining atomic simulations and machine learning This study introduces a computational method integrating molecular simulations and machine learning (ML) to assess the CO adsorption capacities of synthesized and hypothetical metal-organic frameworks (MOFs) at various pressures. After extracting structural, chemical, and energy-based features of the synthesized and hypothetical MOFs (hMOFs), we conducted molecular simulations to compute CO adsorption in synthesized MOFs and used these simulation results to train ML models for predicting CO adsorption in hMOFs. Results showed that CO uptakes of synthesized MOFs and hMOFs are between 0.02-2.28 mol/kg and 0.45-3.06 mol/kg, respectively, at 1 bar, 298 K. At low pressures (0.1 and 1 bar), Henry's constant of CO is the most dominant feature, whereas structural properties such as surface area and porosity are more influential for determining the CO uptakes of MOFs at high pressure (10 bar). Structural and chemical analyses revealed that MOFs with narrow pores (4.4-7.3 Å), aromatic ring-containing linkers and carboxylic acid groups, along with metal nodes such as Co, Zn, Ni achieve high CO uptakes at 1 bar. Our approach evaluated the CO uptakes of ~ 100,000 MOFs, the most extensive and diverse set studied for CO capture thus far, as a robust alternative to computationally demanding molecular simulations and iterative experiments. Adsorption ; Bar (unit) ; Porosity ; Metal-organic framework ; Molecular dynamics ; Materials science ; Metal ; Molecule ; Chemistry ; Computer science ; Chemical engineering ; Physical chemistry ; Computational chemistry ; Organic chemistry ; Physics ; Composite material ; Metallurgy Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Xinyue Ma | Jianmei Yuan | Yuliang Mao
Source: Materials Today Chemistry (article, 2024-09-17)
Field: Materials Science — Materials Chemistry

Accelerating the design and screening of surface-functionalized monolayer SiC for photocatalytic water splitting and ultraviolet applications via machine learning Ultraviolet ; Photocatalysis ; Monolayer ; Materials science ; Ultraviolet light ; Surface modification ; Nanotechnology ; Surface (topology) ; Water splitting ; Chemical engineering ; Optoelectronics ; Photochemistry ; Chemistry ; Catalysis ; Engineering ; Organic chemistry MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Graphene research and applications
Authors: Hélder R. O. Rocha | Roy Roukos | Sara Abou Dargham | Jimmy Romanos | Denis Chaumont | Jair A. L. Silva | H. J. Wörtche
Source: Materials & Design (article, 2024-05-31)
Field: Materials Science — Materials Chemistry

Optimizing a machine learning design of dielectric properties in lead-free piezoelectric ceramics Designing lead-free piezoelectric ceramics with tailored electrical properties remains a critical challenge for various applications. In this paper we present a novel methodology integrating Machine Learning (ML) and optimization procedures to fine-tune electrical properties in lead-free (1-x) Na0.5 Bi0.5 TiO3 - x CaTiO3 piezoelectric ceramics. A comprehensive dataset of dielectric measurements serves as the foundation for training ML models that accurately predict the permittivity (ε′) and dielectric loss (tan δ) as functions of Ca2+ concentration (x % Ca), temperature and frequency. Two ML techniques are evaluated: random forest regression, and Multi-Layer Perceptron neural network Regression (MLPR). The MLPR model exhibited a superior regression performance, achieving a correlation coefficient of 0.931 and a root mean squared error of 0.029. The MLPR was then optimized by the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to maximizes ε′ while minimizes tan δ. Within the NSGA-II framework, the optimal values were found at the Pareto curve knee, corresponding to a frequency, temperature, and x % Ca of 609.739 kHz, 398.15 K, and 6.10, respectively, resulting in ε′ equal to 857.87 and tan δ equal to 0.0120. This approach demonstrates the effectiveness of combining ML and optimization for designing the electrical properties of piezoelectric ceramics, paving the way for more efficient and targeted material development. Materials science ; Piezoelectricity ; Dielectric ; Ceramic ; Lead (geology) ; Composite material ; Engineering physics ; Optoelectronics ; Engineering Ferroelectric and Piezoelectric Materials ; Acoustic Wave Resonator Technologies ; Dielectric materials and actuators
Authors: Rodrigo A. F. Alves | William Ferreira Giozza | Luiz Antônio Ribeiro | Marcelo Lopes Pereira
Source: Materials Today Communications (article, 2024-06-21)
Field: Materials Science — Materials Chemistry

Exploring the thermal and mechanical properties of PAI-Graphene monolayers and nanotubes: Insights from molecular dynamics simulations Materials science ; Molecular dynamics ; Graphene ; Monolayer ; Thermal ; Nanotechnology ; Chemical physics ; Dynamics (music) ; Computational chemistry ; Thermodynamics Graphene research and applications ; 2D Materials and Applications ; Carbon Nanotubes in Composites
Authors: Ibrahim Kurt | Imke Krauhausen | Simone Spolaor | Yoeri van de Burgt
Source: Advanced Science (article, 2024-04-29)
Field: Materials Science — Polymers and Plastics

Predicting Blood Glucose Levels with Organic Neuromorphic Micro‐Networks Accurate glucose prediction is vital for diabetes management. Artificial intelligence and artificial neural networks (ANNs) are showing promising results for reliable glucose predictions, offering timely warnings for glucose fluctuations. The translation of these software-based ANNs into dedicated computing hardware opens a route toward automated insulin delivery systems ultimately enhancing the quality of life for diabetic patients. ANNs are transforming this field, potentially leading to implantable smart prediction devices and ultimately to a fully artificial pancreas. However, this transition presents several challenges, including the need for specialized, compact, lightweight, and low-power hardware. Organic polymer-based electronics are a promising solution as they have the ability to implement the behavior of neural networks, operate at low voltage, and possess key attributes like flexibility, stretchability, and biocompatibility. Here, the study focuses on implementing software-based neural networks for glucose prediction into hardware systems. How to minimize network requirements, downscale the architecture, and integrate the neural network with electrochemical neuromorphic organic devices, meeting the strict demands of smart implants for in-body computation of glucose prediction is investigated. Neuromorphic engineering ; Artificial neural network ; Computer science ; Flexibility (engineering) ; Artificial pancreas ; Computer architecture ; Software ; Artificial intelligence ; Embedded system ; Diabetes mellitus ; Medicine Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Electrochemical sensors and biosensors
Authors: Dongdong Wang | Jian Zheng | Linhui Li | Zile Liang | Yiyin Zheng | Shunying Huang | Xinyu Zheng | Zhu Zhou | Dan Dai
Source: Journal of Food Composition and Analysis (article, 2024-05-11)
Field: Chemistry — Analytical Chemistry

Rapid detection of endogenous impurities in walnuts using near-infrared hyperspectral imaging technology Hyperspectral imaging ; Preprocessor ; Artificial intelligence ; Artificial neural network ; Support vector machine ; Near-infrared spectroscopy ; Pattern recognition (psychology) ; Computer science ; Biological system ; Chemistry ; Optics ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research