公开(公告)号：US20240282412A1

公开(公告)日：2024-08-22

申请号：US18523079

申请日：2023-11-29

Applicant: FOUNDATION OF SOONGSIL UNIVERSITY-INDUSTRY COOPERATION

Inventor： Kyoung Min MIN ,  In Hyo LEE ,  Joon Chul KIM

IPC: G16C60/00 ,  G06N3/0455 ,  G06N3/0475 ,  G16C20/30 ,  G16C20/70

CPC classification number: G16C60/00 ,  G06N3/0455 ,  G06N3/0475 ,  G16C20/30 ,  G16C20/70

Abstract: A technology capable of finding new 2D materials with high elastic modulus and shear modulus through using deep learning, machine learning, and high-throughput calculation methods are described. A device for modeling 2-dimensional (2D) material using machine learning includes a material classification unit receiving data on the virtual inorganic material chemical formulas from the virtual inorganic material generation unit and classifying a 2D material among the plurality of virtual inorganic material chemical formulas into a preliminary 2D material, a space group analysis unit receiving data on the preliminary 2D material from the material classification unit, predicting the space group of the preliminary 2D material, and selecting the preliminary 2D material having the same space group as an existing 2D material as a structurally similar 2D material.

公开(公告)号：US20240355994A1

公开(公告)日：2024-10-24

申请号：US18347691

申请日：2023-07-06

Applicant: FOUNDATION OF SOONGSIL UNIVERSITY-INDUSTRY COOPERATION

Inventor： Kyoung Min MIN ,  Min Seon KIM

IPC: H01M4/04 ,  H01M4/525 ,  H01M10/054

CPC classification number: H01M4/04 ,  H01M4/525 ,  H01M10/054 ,  H01M2004/028

Abstract: An apparatus for selecting a sodium-ion battery cathode material using machine learning includes an input data generation unit configured to select candidate materials among a plurality of materials possible to be used as cathode materials for sodium-ion batteries and generate O3 input data and P3 input data respectively for O3 structure materials and P3 structure materials formed depending on structural transition during charge and discharge from each candidate material, and a material classification unit configured to receive the O3 and P3 input data from the input data generation unit and classify the candidate materials depending on stability in a pristine state and desodiated state, respectively, by performing machine learning on data of the plurality of O3 and P3 structure materials using a pristine model and a desodiated model as prediction models.