(202a) Machine Learning Approach to First-Principles Database for Designing Active Nanomaterials for Electrochemical Energy Convergence
AIChE Annual Meeting
2022
2022 Annual Meeting
Nanoscale Science and Engineering Forum
Nanomaterials for Energy Conversion
Monday, November 14, 2022 - 3:30pm to 4:05pm
Machine learning approach has been intensively applied to nanomaterials of high activity for energy convergence, due to its evident power to identify a key correlation in database. The success of the method, therefore, is critically dependent on the reliability and accuracy of accumulated data. Here, the acquisition of crystal-clear structure-performance relation in heterogeneous catalysis of sluggish electrochemical reactions is demonstrated through three-dimensional tracking of nanoparticles in liquid medium and over thermal treatment. For typical catalyst design process using colloidal synthesis we acquire atomic positions of ligand-protected platinum nanoparticles using first-principles density functional theory calculations and in-situ TEM observation. The structural transformation in time domain is shown by decoupling and tracking of each atomic coordinate using molecular dynamics simulations standing on machine-learning potentials. Fast and accurate acquisition of structural data is integrated with a computational catalysis framework to identify a clear structure-activity correlation in the CO oxidation reaction as an example.