|
|
|
# Combining AI and high-throughput experiments towards inorganic materials-by-design
|
|
|
|
|
|
|
|
|
|
|
|
`Name` <be> Dr. Jose Recatala-Gomez <br>
|
|
|
|
`Affiliation`<be> Department of Materials Science and Engineering, Nanyang Tech. Univ., Singapore
|
|
|
|
|
|
|
|
## Abstract
|
|
|
|
|
|
|
|
The integration of artificial intelligence (AI) and automation presents significant potential to revolutionize materials discovery. While AI-driven approaches have seen notable success in drug discovery, the identification of novel inorganic materials remains a complex challenge. This presentation will highlight several tools developed by the Materials-By-Design lab to address this issue. The discussion will begin with WyCryst, a symmetry-aware generative design framework for inorganic materials. Following this, rapid validation tools designed to experimentally confirm predicted compounds will be introduced, illustrated through two case studies: thermoelectric chalcogenides and perovskite oxides. |
|
|
\ No newline at end of file |
