Zongo, K., Béland, L. K. et Ouellet-Plamondon, C..
2022.
« First-principles database for fitting a machine-learning silicon interatomic force field ».
MRS Advances, vol. 7.
pp. 39-47.
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Abstract
Data-driven machine learning has emerged to address the limitations of traditional methods when modeling interatomic interactions in materials, such as electronic density functional theory (DFT) and semi-empirical potentials. These machine-learning frameworks involve mathematical models coupled to quantum mechanical data. In the present article, we focus on the moment tensor potential (MTP) machine-learning framework. More specifically, we provide an account of the development of a preliminary MTP for silicon, including details pertaining to the construction of a DFT database.
Item Type: | Peer reviewed article published in a journal |
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Professor: | Professor Ouellet-Plamondon, Claudiane |
Affiliation: | Génie de la construction |
Date Deposited: | 07 Mar 2022 18:52 |
Last Modified: | 18 Feb 2023 05:00 |
URI: | https://espace2.etsmtl.ca/id/eprint/24067 |
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