Zobrazeno 1 - 10
of 135
pro vyhledávání: '"Patra, Tarak K"'
Machine learning models have been progressively used for predicting materials properties. These models can be built using pre-existing data and are useful for rapidly screening the physicochemical space of a material, which is astronomically large. H
Externí odkaz:
http://arxiv.org/abs/2409.09691
Autor:
Rajahmundry, Ganesh K, Patra, Tarak K
Solid polymer electrolytes exhibit ion conduction and high mechanical properties, thus are promising materials for future energy storage devices. The ion conductivity in an SPE is intricately connected to salt ion distribution in the polymer matrix.
Externí odkaz:
http://arxiv.org/abs/2406.00270
Vitrimers are polymer networks that can undergo bond exchange reactions. They dynamically rearrange their structures while maintaining their overall integrity, thus resulting in unique properties such as self-healing, reprocessability, shape memory a
Externí odkaz:
http://arxiv.org/abs/2402.16226
Glasses offer a broad range of tunable thermophysical properties that are linked to their compositions. However, it is challenging to establish a universal composition-property relation of glasses due to their enormous composition and chemical space.
Externí odkaz:
http://arxiv.org/abs/2308.11151
Machine learning offers promising tools to develop surrogate models for polymer structure-property relations. Surrogate models can be built upon existing polymer data and are useful for rapidly predicting the properties of unknown polymers. The accur
Externí odkaz:
http://arxiv.org/abs/2308.09898
Autor:
Carrillo, Jan Michael Y., P, Vijith, Patra, Tarak K., Chen, Zhan, Russell, Thomas P., Sankaranarayanan, Subramanian KRS, Sumpter, Bobby G., Batra, Rohit
Star block copolymers (s-BCPs) have potential applications as novel surfactants or amphiphiles for emulsification, compatbilization, chemical transformations and separations. s-BCPs are star-shaped macromolecules comprised of linear chains of differe
Externí odkaz:
http://arxiv.org/abs/2308.08226
Autor:
Himanshu, Patra, Tarak K
Deep learning models are gaining popularity and potency in predicting polymer properties. These models can be built using pre-existing data and are useful for the rapid prediction of polymer properties. However, the performance of a deep learning mod
Externí odkaz:
http://arxiv.org/abs/2210.06622
Polymer nanocomposites (PNCs) offer a broad range of thermophysical properties that are linked to their compositions. However, it is challenging to establish a universal composition-property relation of PNCs due to their enormous composition and chem
Externí odkaz:
http://arxiv.org/abs/2208.11448
Autor:
Ramesh, Praneeth S, Patra, Tarak K
Analysis of molecular scale interactions and chemical structure offers an enormous opportunity to tune material properties for targeted applications. However, designing materials from molecular scale is a grand challenge owing to the practical limita
Externí odkaz:
http://arxiv.org/abs/2111.09659
Autor:
Bale, Ashwin A, Patra, Tarak K
The correlations between the sequence of monomers in a polymer and its three-dimensional structure is a grand challenge in polymer science and biology. The properties and functions of macromolecules depend on their 3D shape that has appeared to be di
Externí odkaz:
http://arxiv.org/abs/2107.06439