Plénières22 > Presentations

Conférence Invitée

Cosmin Marinica :
Vincent Favre-Nicolin : AI for automated data collection & analysis - an ESRF perspective
Nicola Marzari : Digital Infrastructures empowering materials discovery
Maxime Sangnier : A concise overview of classification and clustering methods
Jean-Luc Parouty : Deep Learning, histoire et principes... de la régression aux GANs
Gian-Marco Rignanese : OPTIMADE: A Common REST API for Materials Databases Interoperability
Sandrine Lyonnard :
Gérard Ramstein : Solving optimization problems with machine learning Application to materials science
 

Présentations Orales

Maciej Jakub Karcz  : Machine-learning aided calculation of atomic-scale properties in chemically disordered (U, Pu)O2 fuels
Lune Maillard : Nested_fit: developments and tests
Devergne  :
Aloïs Castellano : Ab Initio Canonical Sampling based on Variational Inference
Lam :
Almeida de Mendonca :
Basile Herzog : Gold standard finite temperature simulations of materials via machine learning
Meier :
Swinburne :
Dubois :
Daniel Forster : Analysis of high resolution transmission electron microscopy images by deep learning: Example of AgCo nanoalloys
Monchot :
Redhouane Boudjehem : Deep learning for sparse spectral ptychographicx-ray computed tomography (Spect-PXCT)
Purushottam raj purohit  :
Cao Junhao :
Allera :
Hicham Khodja : Artificial Intelligence for Ion Beam analysis spectroscopies
Lisa Rateau : The recourse to artificial intelligence to design Co-free wear-resistant alloys
Garel :
Aurore Lomet : Symbolic artificial intelligence for new material design
Thibault Charpentier : Modelling NMR Spectroscopy of Oxide Glasses with Machine Learning
Gaëtan Percebois : Determination of the disorder potential from quantum transport data using machine learning methods
Thibault Sohier : Finding and engineering high-conductivity 2D semiconductors from first principles
Marie-Pierre Gaigeot : Graph Theory for Molecular Dynamics Simulations
Demange :