Abstracts Book - IMRC 2018

• SD2-O017 Invited Talk
MACHINE (&HUMAN!) LEARNING IN CATALYST DISCOVERY
Hongliang Xin 1
1 Virginia Polytechnic Institute and State University, Chemical Engineering, United States.
Transition metal alloys and metal oxides have shown great potential for
catalyzing many chemical reactions. For a given catalyst, its surface reactivity can
be tailored by varying geometric characteristics, metal ligands, and extrinsic
factors (e.g., solvation, support) in the vicinity of active sites. With hierarchical
complexities in catalyst design, a priori estimation of chemical reactivity of
surface atoms is attractive. While all industrial catalysts used today were
discovered via costly and time-consuming empirical testing, recent
developments of density functional theory (DFT) have led to an unprecedented
atomic-scale understanding of the complex processes occurring on catalyst
surfaces and an identification of potentially improved catalysts first-principles.
However, the immense phase space of catalytic materials spanned by structural
and compositional degrees of freedom precludes thorough screening. In this
talk, we are presenting a machine-learning-augmented chemisorption model
that captures adsorption properties of active sites. To design effective features
that the machine-learning algorithms can use to ‘learn’ and predict properties of
catalyst surfaces, we applied a feature engineering process on the catalyst
database. Using deliberately selected electronic structure features, the
machine-learning model optimized with available ab initio adsorption energies
on catalyst surfaces can capture adsorption energies of various intermediates
on different materials (metals, metal oxides, metal-organic frameworks, etc)
with the root mean squared errors (RMSE) smaller than the DFT-GGA calculation
error of 0.2 eV.
Keywords: catalysis, machine learning, structure-activity relationships
Presenting authors email: hxin@vt.edu