Generative structure-property inverse computational co-design of materials
US11537898B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 24, 2020 |
| Grant date | Dec 27, 2022 |
| Priority date | — |
| Expiry date | Dec 2, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG16C60/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method and a system for material design utilizing machine learning are provided, where the underlying joint distribution p(S,P) of structure (S)-property (P) relationships is explicitly learned simultaneously and is utilized to directly generate samples (S,P) in a single step utilizing generative techniques, without any additional processing steps. The subspace of structures that meet or exceed the target for property P is then identified utilizing conditional generation of the distribution (e.g., p(P)), or through randomly generating a large number of samples (S,P) and filtering (e.g., selecting) those that meet target property criteria.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.