In the quest to advance materials design, this thesis integrates Machine Learning (ML) techniques with Density Functional Theory (DFT) data. A novel representation called splashdown is formulated to capture long-range interactions, an aspect often neglected by material representations. A project known as ORGANIZER leads to the creation of a pivotal database, culminating in the discovery of a new organic solid-state lasing molecule that doubled the state-of-the-art emission gain cross-section. Concurrently, a monte-carlo based optimizer, aMC, is tested, demonstrating superior performance to gradient-based methods without the need for expensive gradient computation. Enhanced Graph Neural Networks (GNN)s predict High Entropy Alloy (HEA) catalysts for oxygen reduction reaction, halving necessary DFT computations and unveiling a new HEA catalyst with a 0.27V overpotential. The splashdown representation compares to state-of-the-art ones like MBTR and SOAP in predicting long-range interactions. Collectively, these efforts highlight the transformative potential of ML and some adjacent fields in materials science.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/45590 |
| Date | 27 October 2023 |
| Creators | Benlolo, Ian |
| Contributors | Tamblyn, Isaac |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/ |
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