Title:
MACHINE LEARNING SYSTEM AND METHOD
Document Type and Number:
Japanese Patent JP2021060992
Kind Code:
A
Abstract:
To directly generate samples (S, P) without any additional processing steps by learning of the underlying joint distribution p (S,P) of structure (S)-property (P) relationships, regarding a material design method using machine learning.SOLUTION: A generative adversarial network includes: a latent space vector generator configured to generate random vectors; a generator network configured to receive the random vectors as input and to generate a tuple including both a simulated structure and a simulated property; and a discriminator network configured to be trained from a database including real structures and real properties, to distinguish the simulated structure and the simulated property from the real structure and the real property, and to send a loss function to the generator network. The latent space vector generator, the generator network, and the discriminator network are arranged to learn an underlying joint probability distribution of structure-property p(S, P) simultaneously.SELECTED DRAWING: Figure 2
Inventors:
GANESH HEGDE
HARSONO S SIMKA
HARSONO S SIMKA
Application Number:
JP2020167087A
Publication Date:
April 15, 2021
Filing Date:
October 01, 2020
Export Citation:
Assignee:
SAMSUNG ELECTRONICS CO LTD
International Classes:
G06N3/08; G06N20/00
Attorney, Agent or Firm:
Tadashige Ito
Tadahiko Ito
Osamu Miyazaki
Tadahiko Ito
Osamu Miyazaki