This presentation will discuss how the high dimensionality of information in linking catalyst chemistry to properties can be addressed by the judicious use of manifold learning methods. Of particular focus, the talk will discuss how critical data associated with surfaces and interfaces can be derived through informatics and statistical learning methods.  This presentation will also provide an overview of different approaches to apply statistical learning tools to problems in catalysis that involve both computational and experimental studies

Statistical learning aided design of catalyst chemistry

253rd National Meeting (2017)

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