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Article Dans Une Revue Machine Learning: Science and Technology Année : 2020

Measuring transferability issues in machine-learning force fields: The example of Gold-Iron interactions with linearized potentials

Magali Benoit
Ségolène Combettes
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Ibrahim Khaled
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Julien Lam
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Résumé

Machine-learning force fields have been increasingly employed in order to extend the possibility of current first-principles calculations. However, the transferability of the obtained potential can not always be guaranteed in situations that are outside the original database. To study such limitation, we examined the very difficult case of the interactions in gold-iron nanoparticles. For the machinelearning potential, we employed a linearized formulation that is parameterized using a penalizing regression scheme which allows us to control the complexity of the obtained potential. We showed that while having a more complex potential allows for a better agreement with the training database, it can also lead to overfitting issues and a lower accuracy in untrained systems.
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Dates et versions

hal-03052129 , version 1 (10-12-2020)

Identifiants

  • HAL Id : hal-03052129 , version 1

Citer

Magali Benoit, Jonathan Amodeo, Ségolène Combettes, Ibrahim Khaled, Aurélien Roux, et al.. Measuring transferability issues in machine-learning force fields: The example of Gold-Iron interactions with linearized potentials. Machine Learning: Science and Technology, 2020. ⟨hal-03052129⟩
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