About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
|
Symposium
|
Synthesis, Characterization, Modeling and Applications of Functional Porous Materials
|
Presentation Title |
Porous Materials Design Using Machine Learning |
Author(s) |
Lan Li |
On-Site Speaker (Planned) |
Lan Li |
Abstract Scope |
Nanomaterials like molecular sieve, zeolites, metal-organic frameworks, and porous carbon are considered for post-combustion CO2 capture. Millions of hypothetical porous structures have been constructed. An effective approach that can effectively and quickly identify high-performance candidates with desired properties is crucial. Machine learning is a powerful approach that can quickly screen a large scale of datasets, narrow down potential candidates, and identify data patterns and trends that can guide materials development. Therefore, machine learning has been widely utilized in different segments to enhance the efficiency of carbon capture and storage. This presentation will summarize the machine learning methods commonly used in the porous materials design. Recommendations for future applications of machine learning will be also presented. |