(Online) Transferable prediction of formation energy across lattices of increasing sizes

Presentation information

Contributed talks (Machine learning/Quantum computing)

Machine learning, data science, and quantum computing

Parallel session 1

Sun. Aug 6, 2023 1:40 PM - 3:20 PM 301 (Room 2) (3F)

Chair: Nobuyasu Ito (RIKEN R-CCS)

1:40 PM - 2:00 PM

*Massimiliano Lupo Pasini¹, Mariia Karabin¹, Markus Eisenbach¹ (1. Oak Ridge National Laboratory (United States of America))

Keywords:Graph convolutional neural networks, Deep Learning, Solid solution alloys, Formation energy, Lattice size

Abstract password authentication.
Password is required to view the abstract. Please enter a password to authenticate.