1 citations · 2 across the 4 of their papers we have counts for
4 papers
Transferable 3D Convolutional Neural Networks for Elastic Constants Prediction in Nanoporous Metals
Sergei Zorkaltsev, Rafał Topolnicki, Tal-El Carmon +4
The topology of nanoporous metals is crucial for determining their mechanical response. In this work, we generated 6,000 gold and 422 silver nanoporous structures and calculated th…
Direction-aware topological descriptors for elastic stiffness tensor prediction in porous materials
Rafał Topolnicki, Michał Bogdan, Jakub Malinowski +3
Classical topological descriptors used in topological data analysis (TDA) are invariant under permutations of spatial axes and therefore cannot represent the loading direction, whi…
Descriptor and Graph-based Molecular Representations in Prediction of Copolymer Properties Using Machine Learning
Elaheh Kazemi-Khasragh, Rocío Mercado, Carlos Gonzalez +1
Copolymers are highly versatile materials with a vast range of possible chemical compositions. By using computational methods for property prediction, the design of copolymers can…
Toward Diverse Polymer Property Prediction Using Transfer Learning
Elaheh Kazemi-Khasragh, Carlos Gonzaleza, Maciej Haranczyk
The prediction of mechanical and thermal properties of polymers is a critical aspect for polymer development. Herein, we discuss the use of transfer learning approach to predict mu…