dc.contributor.author |
Agarwal, Sheena |
|
dc.contributor.author |
Mehta, Shweta |
|
dc.contributor.author |
Joshi, Kavita |
|
dc.date.accessioned |
2023-01-02T10:20:47Z |
|
dc.date.available |
2023-01-02T10:20:47Z |
|
dc.date.issued |
2023-01-02 |
|
dc.identifier.citation |
New J. Chem., 2020, 44, 8545 |
en |
dc.identifier.uri |
http://dspace.ncl.res.in:8080/xmlui/handle/20.500.12252/6186 |
|
dc.description.uri |
DOI: 10.1039/d0nj00633e |
en |
dc.format.extent |
9 p. |
en |
dc.language.iso |
en_US |
en |
dc.publisher |
Royal Society of Chemistry |
en |
dc.subject |
Artificial Intelligence |
en |
dc.subject |
Quantum Chemistry |
en |
dc.subject |
Density Functional Theory |
en |
dc.title |
Understanding the ML black box with simple descriptors to predict cluster–adsorbate interaction energy |
en |
dc.type |
Article |
en |
local.division.division |
Physical and Materials Chemistry Division |
en |