Sign up now to have the right to view
50 tenders for free.
Project News Details
| Title |
Narrowing down 166 billion battery materials |
|||
|---|---|---|---|---|
You can sign up and unlock it for freeAccess to Public and Private Sector Business Opportunities for 200+ Countries |
||||
| Detail |
Researchers at the U.S. Argonne National Laboratory have applied a combination of machine learning and artificial intelligence to help narrow down a list of 166 billion molecules that could be used to form the basis of a battery electrolyte. The technique, say the researchers, offers a way to greatly reduce the cost of narrowing down such an enormous data set, while still providing a precise understanding of each molecule and its likely suitability.
|
|||
| Country | ||||
| Source | PV-Magazine | |||
| Regions | Africa Asia Australia Europe North America Oceania South America | |||
| Sectors | Automobiles and Auto Parts Construction | |||
| Published Date | 01.12.2019 | |||
| Original Url | Original Url | |||
| Share | ||||
International
| Title |
|---|
|
Narrowing down 166 billion battery materials
|
| Detail |
| Researchers at the U.S. Argonne National Laboratory have applied a combination of machine learning and artificial intelligence to help narrow down a list of 166 billion molecules that could be used to form the basis of a battery electrolyte. The technique, say the researchers, offers a way to greatly reduce the cost of narrowing down such an enormous data set, while still providing a precise understanding of each molecule and its likely suitability. |
| Country |
|
International
|
| Source |
| PV-Magazine |
| Regions |
| Africa Asia Australia Europe North America Oceania South America |
| Sectors |
| Automobiles and Auto Parts Construction |
| Published Date |
| 01.12.2019 |
| Original Url |
| Share |
To be notified
when a tender matching your filter is
published
To Follow
Tenders
Add New Tender Alert
To Follow
Contracts
Add New Contract Alert
To Follow
Fairs
Add New Fair Alert
