Methods for predicting likelihood of successful experimental synthesis of computer-generated materials by combining network analysis and machine learning
US12205683B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 28, 2022 |
| Grant date | Jan 21, 2025 |
| Priority date | — |
| Expiry date | Apr 3, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N5/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.