Accelerating Catalytic Materials Discovery for Sustainable Nitrogen Transformations by Interpretable Machine Learning
| dc.contributor.author | Pillai, Hemanth Somarajan | en |
| dc.contributor.committeechair | Xin, Hongliang | en |
| dc.contributor.committeemember | Zhu, Huiyuan | en |
| dc.contributor.committeemember | Feng, Xiaofeng | en |
| dc.contributor.committeemember | Achenie, Luke E. | en |
| dc.contributor.department | Chemical Engineering | en |
| dc.date.accessioned | 2023-01-13T09:00:28Z | en |
| dc.date.available | 2023-01-13T09:00:28Z | en |
| dc.date.issued | 2023-01-12 | en |
| dc.description.abstract | Computational chemistry and machine learning approaches are combined to understand the mechanisms, derive activity trends, and ultimately to search for active electrocatalysts for the electrochemical oxidation of ammonia (AOR) and nitrate reduction (NO3RR). Both re- actions play vital roles within the nitrogen cycle and have important applications within tackling current environmental issues. Mechanisms are studied through the use of density functional theory (DFT) for AOR and NO3RR, subsequently a descriptor based approach is used to understand activity trends on a wide range of electrocatalysts. For AOR inter- pretable machine learning is used in conjunction with active learning to screen for active and stable ternary electrocatalysts. We find Pt3RuCo, Pt3RuNi and Pt3RuFe show great activity, and are further validated via experimental results. By leveraging the advantages of the interpretible machine learning model we elucidate the underlying electronic factors for the stronger *N binding which leads to the observed improved activity. For NO3RR an interpretible machine learning model is used to understand ways to bypass the stringent limitations put on the electrocatalytic activity due to the *N vs *NO3 scaling relations. It is found that the *N binding energy can be tuned while leaving the *NO3 binding energy unaffected by ensuring that the subsurface atom interacts strongly with the *N. Based on this analysis we suggest the B2 CuPd as a potential active electrocatalyst for this reaction, which is further validated by experiments | en |
| dc.description.abstractgeneral | The chemical reactions that makeup the nitrogen cycle have played a pivotal role in human society, consider the fact that one of the most impactful achievements of the 20th century was the conversion of nitrogen (N2) to ammonia (NH3) via the Haber-Bosch process. The key class of materials to facilitate such transformations are called catalysts, which provide a reactive surface for the reaction to occur at reasonable reaction rates. Using quantum chemistry we can understand how various reactions proceed on the catalyst surface and how the catalyst can be designed to maximize the reaction rate. Specifically here we are interested in the electrochemical oxidation of ammonia (AOR) and reduction of nitrate (NO3RR), which have important energy and environmental applications. The atomistic insight provided by quantum chemistry helps us understand the reaction mechanism and key hurdles in developing new catalysts. Machine learning can then be leveraged in various ways to find novel catalysts. For AOR machine learning finds novel active catalysts from a diverse design space, which are then experimentally tested and verified. Through the use of our machine learning algorithm (TinNet) we also provide new insights into why the catalysts are more active, and suggest novel physics that can help design active catalysts. For NO3RR we use machine learning as a tool to help us understand the hurdles in catalyst design better which then guides our catalyst discovery. It is shown that CuPd could be a potential candidate and is also verified via experimental synthesis and performance testing. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:35875 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/113156 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | DFT | en |
| dc.subject | Catalysis | en |
| dc.subject | nitrogen | en |
| dc.subject | machine learning | en |
| dc.title | Accelerating Catalytic Materials Discovery for Sustainable Nitrogen Transformations by Interpretable Machine Learning | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Chemical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
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