公开(公告)号：US20200279148A1

公开(公告)日：2020-09-03

申请号：US16784717

申请日：2020-02-07

Applicant: Preferred Networks, Inc.

Inventor： Daisuke MOTOKI ,  Kosuke NAKAGO

IPC: G06N3/04 ,  G06N3/08

Abstract: A material structure analysis scheme for using machine learning to predict a general structure of an arbitrary material is provided. One aspect of the present disclosure relates to a material structure analysis method, including acquiring, by one or more processors, structural data representing a structure of a material and spectral data representing a spectrum of a material, inputting, by the one or more processors, the structural data to a first neural network to acquire a structural feature from the first neural network, inputting, by the one or more processors, the spectral data to a second neural network to acquire a spectral feature from the second neural network, and determining, by the one or more processors, a degree of coincidence between the material corresponding to the structural data and the material corresponding to the spectral data based on the structural feature and the spectral feature.

公开(公告)号：US20240111998A1

公开(公告)日：2024-04-04

申请号：US18534252

申请日：2023-12-08

Applicant: Preferred Networks, Inc.

Inventor： Daisuke MOTOKI ,  Chikashi SHINAGAWA ,  So TAKAMOTO ,  Hiroki IRIGUCHI

IPC: G06N3/045 ,  G06N3/084

CPC classification number: G06N3/045 ,  G06N3/084

Abstract: An inferring device includes one or more memories; and one or more processors. The one or more processors are configured to input information on each atom in an atomic system into a second model to infer a difference between energy based on a first-principles calculation corresponding to the atomic system and energy of an interatomic potential function corresponding to the atomic system.

公开(公告)号：US20210183051A1

公开(公告)日：2021-06-17

申请号：US17189592

申请日：2021-03-02

Applicant: Preferred Networks, Inc.

Inventor： Kosuke NAKAGO ,  Daisuke MOTOKI ,  Masaki WATANABE ,  Tomoki KOMATSU

IPC: G06T7/00 ,  G06N3/08

Abstract: With respect to an inference method performed by at least one processor, the method includes inputting, by the at least one processor, into a learned model, second non-processed image data and second parameter data of a simulator, and inferring, by the at least one processor using the learned model, second processed image data. The learned model has been trained so that first processed image data, obtained as an output in response to first non-processed image data and first parameter data of the simulator for the first non-processed image data being input, approaches first simulator processed image data, obtained as a result of the simulator for the first non-processed image data by using the first parameter data.

公开(公告)号：US20220207370A1

公开(公告)日：2022-06-30

申请号：US17698950

申请日：2022-03-18

Applicant: Preferred Networks, Inc.

Inventor： Daisuke MOTOKI

IPC: G06N3/08 ,  G06N5/04

Abstract: An inferring device includes one or more memories and one or more processors. The one or more processors input a vector relating to an atom into a first network which extracts a feature of the atom in a latent space from the vector relating to the atom, and infer the feature of the atom in the latent space through the first network.