You are here

Nanoparticle Catalytic Discovery using Machine Learning Models with Chemisorption Inputs

Award Information
Agency: Department of Energy
Branch: N/A
Contract: DE-SC0018875
Agency Tracking Number: 237275
Amount: $150,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: 06a
Solicitation Number: DE-FOA-0001771
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-07-02
Award End Date (Contract End Date): 2019-03-01
Small Business Information
526 S. Main Street Suite 821
Akron, OH 44311-4403
United States
DUNS: 969096952
HUBZone Owned: Yes
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Mesfin Tsige
 (330) 972-5631
Business Contact
 Anthony Felici
Phone: (234) 788-1080
Research Institution
 The University of Akron
 Mesfin Tsige
185 E. Mill Street Polymer SCience
Akron, OH 44325-3909
United States

 (330) 972-5631
 Nonprofit College or University

The mechanisms guiding nanocatalysis are poorly understood and extremely hard to model. Proposed effort attempts to provide a machine learning based platform that would guide the design of nanocatalytic materials for a variety of applications, beginning with the use of case of chloro-organic contaminant remediation. The current approaches either use conventional molecular dynamics coupled with ab initio approaches to uncover reaction kinetics or machine learning platforms that neglect or under-estimate the soft epitaxy between adsorbant and surface. Using reactive descriptors sourced from MD trajectories with validated potentials, the artificial neural networks will have molecular imprint information key to catalytic pathways. This would elucidate reactive sites using descriptors that eschew the more computationally expensive DFT approaches, leading to larger phase exploration and more translatable models. The artificial neural network models would be based on reactivity descriptors that are very specific to the molecular geometry and nanoparticle feature size. Sensitivity analyses would be done to improve the initial training of data sets for a similar set of contaminants. Robustness of the model would be tested by running the models on a dissimilar system. Finally, hypothetical nanomaterials and facets would be proposed to experimental collaborators.

* Information listed above is at the time of submission. *

US Flag An Official Website of the United States Government