Invention Grant
- Patent Title: Methods for predicting likelihood of successful experimental synthesis of computer-generated materials by combining network analysis and machine learning
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Application No.: US16004232Application Date: 2018-06-08
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Publication No.: US11580431B2Publication Date: 2023-02-14
- Inventor: Muratahan Aykol , Santosh Karthik Suram , Linda Hung , Patrick Kenichi Herring
- Applicant: Toyota Research Institute, Inc.
- Applicant Address: US CA Los Altos
- Assignee: Toyota Research Institute, Inc.
- Current Assignee: Toyota Research Institute, Inc.
- Current Assignee Address: US CA Los Altos
- Agency: Sheppard, Mullin, Richter & Hampton LLP
- Agent Hector A. Agdeppa; Daniel N. Yannuzzi
- Main IPC: G06N7/00
- IPC: G06N7/00 ; G06N20/00 ; G16C20/10 ; G16C20/70 ; G06K9/62

Abstract:
One aspect of the disclosure relates to systems and methods for determining probabilities of successful synthesis of materials in the real world at one or more points in time. The probabilities of successful synthesis of materials in the real world at one or more points in time can be determined by representing the materials and their pre-defined relationships respectively as nodes and edges in a network form, and computation of the parameters of the nodes in the network as input to a classification model for successful synthesis. The classification model being configured to determine probabilities of successful synthesis of materials in the real world at one or more points in time.
Information query
IPC分类:
G | 物理 |
G06 | 计算;推算或计数 |
G06N | 基于特定计算模型的计算机系统 |
G06N7/00 | 基于特定数学模式的计算机系统 |