M. Cools-Ceuppens
Constructing a new localization scheme to improve the electrostatic potential of explicit electron force fields
Boosting the discovery rate of energy materials using deep learning
Master of Science in Engineering Physics
2020
Comparing different machine learning force fields: a case study of aluminium
ISBN/ISSN:
Poster
Conference / event / venue
The 34th Winter School in Theoretical Chemistry: Machine Learning
Helsinki, Finland
Monday, 10 December, 2018 to Thursday, 13 December, 2018
Comparing Different Machine Learning Force Fields: A Case Study of Aluminium
ISBN/ISSN:
Poster
Conference / event / venue
MOFSIM 2019
Ghent, Belgium
Wednesday, 10 April, 2019 to Friday, 12 April, 2019
Explicit-electron force fields in the frame of the Modern Theory of Polarization
Boosting the discovery rate of energy materials using deep learning
Machine learning and materials science: ab initio screening to microstructure analysis
ISBN/ISSN:
Poster
Conference / event / venue
34th winter school in theoretical chemistry: Machine Learning in Theoretical Chemistry
Helsinki, Finland
Monday, 10 December, 2018 to Thursday, 13 December, 2018
Evaluating a linear machine learning force field for aluminium
ISBN/ISSN:
Talk
Conference / event / venue
L'intelligence artificielle pour la chimie des matériaux
Paris, France
Tuesday, 25 September, 2018
Explicit-electron force fields in the frame of the Modern Theory of Polarization
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