Results 161 to 170 of about 522,455 (311)

A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions

Advanced Engineering Materials, EarlyView.
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice, Krzysztof S. Stopka, Michael D. Sangid   +2 more
wiley   +1 more source

Machine-learning potentials for efficient simulations of anisotropic colloids

The Journal of Chemical Physics
Simulating interactions between non-spherical colloidal particles is computationally challenging due to the complex dependency of forces and energies on their geometry. Instead of a position and orientation, we represent each shape by a small set of points with the same symmetry, which allows the use of descriptor-based and end-to-end models for ...
B. Ruşen Argun, Antonia Statt
openaire   +3 more sources

Lattice Thermal Conductivity of Monolayer InSe Calculated by Machine Learning Potential. [PDF]

Nanomaterials (Basel), 2023
Han J, Zeng Q, Chen K, Yu X, Dai J.
europepmc   +1 more source

Prediction of Surface Topography Parameters in Direct Laser Interference Patterning of Stainless Steel Using Infrared Monitoring and Convolutional Neural Networks

Advanced Engineering Materials, EarlyView.
This study presents an infrared monitoring approach for direct laser interference patterning (DLIP) combined with a convolutional neural network (CNN). Thermal emission data captured during structuring are used to predict surface topography parameters.
Lukas Olawsky, Marcelo Sallese, Leander Kläber, Clemens Kuhn, Keming Du, Andrés Fabián Lasagni   +5 more
wiley   +1 more source

Using Machine Learning to Design a Flexible LOC Counter

, 2017
The results of counting the size of programs in terms of Lines-of-Code (LOC) depends on the rules used for counting (i.e. definition of which lines should be counted).
Bargowski, D., Staron, Miroslaw,, Wilhelm Meding, Hebig, Regina,, Ochodek, M.,, Miroslaw Staron, Ochodek, M., Hebig, Regina, Regina Hebig, Ochodek, Miroslaw,, Hebig, R., Bargowski, Dominik,, Staron, Miroslaw, Miroslaw Ochodek, Dominik Bargowski, Meding, Wilhelm, Bargowski, D.,, Meding, Wilhelm,   +17 more
core   +1 more source

Interactome-Based Machine Learning Predicts Potential Therapeutics for COVID-19

ACS Omega, 2023
Nimisha Ghosh, Indrajit Saha, Anna Gambin   +2 more
doaj   +1 more source

Zein‐Based Adhesives: Sustainable Extraction and Application in Bioadhesive Technologies

Advanced Engineering Materials, EarlyView.
Zein is extracted from corn gluten meal using a simple and scalable process with high yield (~90%). The resulting protein is applied in bioadhesives modified with Ca2+ and Fe3+ ions, exhibiting substrate‐dependent adhesion. The findings demonstrate competitive bonding performance and highlight the role of ionic interactions in tuning adhesion ...
Paula Bertolino Sanvezzo, Daniela Winsley Valereto Friozi, Vinícius de Oliveira, Marcia Cristina Branciforti   +3 more
wiley   +1 more source

Distribution of Bound Conformations in Conformational Ensembles for X-ray Ligands Predicted by the ANI-2X Machine Learning Potential. [PDF]

J Chem Inf Model, 2023
Han F, Hao D, He X, Wang L, Niu T, Wang J.   +5 more
europepmc   +1 more source

Thermodynamic Pathways of Nonequilibrium Solidification in Wire‐Arc Additive Manufacturing Fe‐Based Multicomponent Alloy Structures

Advanced Engineering Materials, EarlyView.
Geometry‐driven thermal behavior in wire‐arc additive manufacturing (WAAM) influences microstructural evolution during nonequilibrium solidification of a chemically complex Fe–Cr–Nb–W–Mo–C nanocomposite system. By comparing different deposits configurations, distinct entropy–cooling rate correlations, segregation, and carbide evolution are revealed ...
Blanca Palacios, Anthony Gonzalez, Aleksander Aleman, Tyler Dolmetsch, Tanaji Paul, Arvind Agarwal   +5 more
wiley   +1 more source

The evolution of machine learning potentials for molecules, reactions and materials

Chemical Society Reviews
This review offers a comprehensive overview of the development of machine learning potentials for molecules, reactions, and materials over the past two decades, evolving from traditional models to the state-of-the-art.
Junfan Xia, Yaolong Zhang, Bin Jiang
openaire   +3 more sources