1 code implementation • 16 Oct 2023 • Arsen Sultanov, Jean-Claude Crivello, Tabea Rebafka, Nataliya Sokolovska
The discovery of new functional and stable materials is a big challenge due to its complexity.
1 code implementation • 21 Nov 2020 • Jean-Claude Crivello, Nataliya Sokolovska, Jean-Marc Joubert
Machine learning (ML) methods are becoming integral to scientific inquiry in numerous disciplines, such as material sciences.
no code implementations • HAL archives-ouvertes 2019 • Asma Atamna, Nataliya Sokolovska, Jean-Claude Crivello
In this work, we present a novel and simple convolutional neural network architecture for supervised learning on graphs that is provably invariant to node permutation.
Ranked #1 on Graph Classification on HYDRIDES
no code implementations • 26 Oct 2018 • Asma Nouira, Nataliya Sokolovska, Jean-Claude Crivello
Our main motivation is to propose an efficient approach to generate novel multi-element stable chemical compounds that can be used in real world applications.