Publication date: 10th April 2024
Screening with molecular simulations, and more recently with machine learning, is establishing an amazingly effective computational toolkit for materials discovery. In this talk I describe some of our recent work demonstrating that molecular simulation-based screening can discover new materials with outstanding oxygen kinetics mediated through both vacancy (e.g., BaFe0.125Co0.125Zr0.75O3-d)[1] and interstitial (e.g., La4Mn5Si4O22+d)[2] mechanisms. We have also shown that the ability to form the relatively less common interstitial oxygen diffusers depends on simple criteria of electron availability and structural flexibility. We believe that these criteria can form the foundation for new approaches to discovery of novel interstitial oxygen diffusers.
I then share some of our recent work on machine learning methods for predicting oxygen kinetics, with a focus on Area Specific Resistance (ASR) in solid oxide fuel cell electrodes. We demonstrate that,[3] for properly cleaned ASR data, machine learning predictions based on simple elemental properties are quite good (MAE ≈ 0.2 log units). Using a temporal cross-validation scheme we show that these machine learning models can be used for effective materials discovery. We also demonstrate that our ASR models are as good or better than correlations with traditional ab initio descriptors like oxygen p-bands, while being orders of magnitude faster to apply. Finally, we demonstrate that emerging data extraction methods based on large language models[4] may greatly reduce the time needed to develop databases for constructing these types of machine learning materials property models.
Aspects of this work were funded by
1. The United States Department of Energy (DOE), National Energy Technology Laboratory (NETL), in part, through a site support contract.
2. The DOE, Office of Science, Basic Energy Sciences (BES), under Award # DE-SC0020419.
3. The National Science Foundation (NSF) Cyberinfrastructure for Sustained Scientific Innovation (CSSI) Award No. 1931298.
This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by NSF Grant Number ACI-1548562.
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