Journal Articles Minerals Engineering Year : 2024

Enhanced Machine Learning Molecular Simulations for optimization of flotation selectivity: A perspective paper

Y. Foucaud
Josué Mesquita
  • Function : Author
  • PersonId : 1489282
Henrique Turrer
  • Function : Author
  • PersonId : 1489283

Abstract

The recovery of valuable minerals in froth flotation industry relies on finding inexpensive and environmentally friendly reagents that selectively adsorb upon surfaces and interfaces. Computer simulations, especially when extended, provide access to detailed mechanistic information on solvent configurations and may ascertain crucial dynamical events over the adsorption process. Further, in silico throughput screening can prevent both the high cost of experiments and the related risks to the environment. Yet, a better compromise between accuracy and computational cost must be met. Machine learning (ML) simulations may ease the latter and suggest solidophilic reagents able to improve the flotation efficiency, shedding new light on discerning descriptors able to accurately capture the nature of the molecule-surface interaction. In this work, our recent advancements in modeling of new accurate mineral-water interfaces based on active learning of ab initio molecular dynamics trajectories have been introduced. The case of some habitual oxides and minerals liberated in mining industry will be taken as examples.
Embargoed file
Embargoed file
0 2 1
Year Month Jours
Avant la publication
Thursday, April 10, 2025
Embargoed file
Thursday, April 10, 2025
Please log in to request access to the document

Dates and versions

hal-04884885 , version 1 (13-01-2025)

Licence

Identifiers

Cite

David Dell'Angelo, Y. Foucaud, Josué Mesquita, Juliette Lainé, Henrique Turrer, et al.. Enhanced Machine Learning Molecular Simulations for optimization of flotation selectivity: A perspective paper. Minerals Engineering, 2024, 218, pp.109016. ⟨10.1016/j.mineng.2024.109016⟩. ⟨hal-04884885⟩
0 View
0 Download

Altmetric

Share

More