First-principles database for fitting a machine-learning silicon interatomic force field

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
Professor:	Professor Ouellet-Plamondon, Claudiane
Affiliation:	Génie de la construction
Date Deposited:	07 Mar 2022 18:52
Last Modified:	24 Mar 2022 15:59
URI:	https://espace2.etsmtl.ca/id/eprint/24067

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