scholarly journals Supporting the Automated Generation of Modular Product Line Safety Cases

Author(s):  
André L. de Oliveira ◽  
Rosana T. V. Braga ◽  
Paulo C. Masiero ◽  
Yiannis Papadopoulos ◽  
Ibrahim Habli ◽  
...  
2020 ◽  
Vol 1 ◽  
pp. 867-876 ◽  
Author(s):  
T. Friedrich ◽  
S. Schmitt ◽  
S. Menzel

AbstractIn product development, an automated generation of shape variations enables a rapid assessment of potentially appealing design directions. We present a framework for computing a product line-up of automotive body shapes based on spectral methods for mesh processing. We calculate the eigenspace projections of 3D vehicle meshes and identify the relevant style as well as content components based on the eigenvalues. The style of a model is then transferred to arbitrary prototype content car shapes, which allows for a rapid portfolio generation of various car types with minimal user interaction.


2021 ◽  
pp. 130-145
Author(s):  
Ramy Shahin ◽  
Sahar Kokaly ◽  
Marsha Chechik

Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>


Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>


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