Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Emily G. Saldanha"'
Publikováno v:
Journal of Cheminformatics, Vol 15, Iss 1, Pp 1-18 (2023)
Abstract Deep learning models have proven to be a powerful tool for the prediction of molecular properties for applications including drug design and the development of energy storage materials. However, in order to learn accurate and robust structur
Externí odkaz:
https://doaj.org/article/eab988fe3b204333aaa12b69180e87cc
Autor:
Peiyuan Gao, Amity Andersen, Jonathan Sepulveda, Gihan U. Panapitiya, Aaron Hollas, Emily G. Saldanha, Vijayakumar Murugesan, Wei Wang
Publikováno v:
Scientific Data, Vol 9, Iss 1, Pp 1-9 (2022)
Measurement(s) quantum descriptors • molecular descriptors • physical descriptors Technology Type(s) Computation • experiment
Externí odkaz:
https://doaj.org/article/c2c07e975fef49a6b321bb169843c426
Autor:
Peiyuan Gao, Amity Andersen, Jonathan Sepulveda, Gihan U. Panapitiya, Aaron Hollas, Emily G. Saldanha, Vijayakumar Murugesan, Wei Wang
Publikováno v:
Scientific data. 9(1)
Aqueous organic redox flow batteries offer an environmentally benign, tunable, and safe route to large-scale energy storage. The energy density is one of the key performance parameters of organic redox flow batteries, which critically depends on the