Z
Zineb Belkacemi
Researcher at École Normale Supérieure
Publications - 4
Citations - 135
Zineb Belkacemi is an academic researcher from École Normale Supérieure. The author has contributed to research in topics: Iterative learning control & Iterative method. The author has an hindex of 3, co-authored 3 publications receiving 51 citations.
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Journal ArticleDOI
Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems.
Paraskevi Gkeka,Gabriel Stoltz,Gabriel Stoltz,Amir Barati Farimani,Zineb Belkacemi,Michele Ceriotti,John D. Chodera,Aaron R. Dinner,Andrew L. Ferguson,Jean-Bernard Maillet,Hervé Minoux,Christine Peter,Fabio Pietrucci,Ana J. Silveira,Alexandre Tkatchenko,Zofia Trstanova,Rafal P. Wiewiora,Tony Lelièvre,Tony Lelièvre +18 more
TL;DR: A review of the current understanding of goals, benefits, and limitations of machine learning techniques for computational studies on atomistic systems, focusing on the construction of empirical force fields from ab-initio databases and the determination of reaction coordinates for free energy computation and enhanced sampling.
Posted Content
Chasing Collective Variables using Autoencoders and biased trajectories
TL;DR: In this paper, a new iterative method involving collective variables (CV) learning with autoencoders is proposed, called Free Energy Biasing and Iterative Learning with AutoEncoders (FEBILAE), which includes the reweighting scheme to ensure that the learning model optimizes the same loss and achieves CV convergence.
Posted Content
Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems
Paraskevi Gkeka,Gabriel Stoltz,Gabriel Stoltz,Amir Barati Farimani,Zineb Belkacemi,Michele Ceriotti,John D. Chodera,Aaron R. Dinner,Andrew L. Ferguson,Jean-Bernard Maillet,Hervé Minoux,Christine Peter,Fabio Pietrucci,Ana J. Silveira,Alexandre Tkatchenko,Zofia Trstanova,Rafal P. Wiewiora,Tony Lelièvre,Tony Lelièvre +18 more
TL;DR: A review of the current understanding of machine learning techniques for computational studies on atomistic systems, focusing on the construction of empirical force fields from ab-initio databases and the determination of reaction coordinates for free energy computation and enhanced sampling can be found in this article.
Journal ArticleDOI
Autoencoders for dimensionality reduction in molecular dynamics: Collective variable dimension, biasing, and transition states.
TL;DR: In this article , an autoencoder-learned collective variable (CV) in conjunction with adaptive biasing force Langevin dynamics is used to characterize the dynamics of heat shock protein 90 (Hsp90).