ChemRxiv
These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
main.pdf (2.44 MB)

A Combined DFT/Machine Learning Framework for Materials Discovery: Application to Spinels and Assessment of Search Completeness and Efficiency

preprint
submitted on 08.10.2020 and posted on 12.10.2020 by Elif Ertekin, Joshua A. Schiller
It is challenging to evaluate machine learning approaches developed for accelerating materials search and discovery in a realistic way. Machine learning approaches to materials stability prediction are typically assessed by their ability to reproduce results from direct physical modeling, whereas ideally both machine learning and direct physical modeling should be assessed by their ability to reproduce reality. Additionally, traditional evaluation metrics do not directly reflect the experience of an experimental search for unknown compounds in a large candidate phase space, and often result in overly optimistic assessments. Here, we (i) present a framework that combines density functional theory and traditional supervised machine learning methods (ML/DFT), and (ii) introduce the concepts of search completeness – the fraction of discoverable compounds found relative to the fraction of search space explored – and search efficiency – the rate of discovery relative to the fraction of search space explored – to evaluate it. The ML/DFT framework is an iterative approach to predict stable chemistries of a fixed crystal structure (here, spinels) that uses DFT to generate a training set of unstable compounds. The training set of stable compounds is given by experimentally known spinels. The method is carried out using random forest, LASSO, and ridge regression to predict as-of-yet undiscovered spinel chemistries. TreeSHAP analysis is used to determine features that most contribute to stability/instability classification. While no single feature dominates, several emerge that align with chemical intuition. To estimate the efficacy of ML/DFT compared to pure DFT, we introduce a Bayesian description of DFT distribution of energies for stable and unstable spinels. The Bayesian model enables quantifying the search completeness and search efficiency of DFT, which is then compared to that of ML/DFT. ML/DFT achieves search completeness and efficiency on par with pure DFT, despite requiring fewer DFT simulations (∼300 vs. 14,200). More importantly, by quantitatively assessing ML approaches in ways that better reflect how they would be used in materials discovery experiments, we obtain key insights into the challenges that need to be overcome by such methods: that the small number of stable compounds to be found in a search space orders of magnitude larger places stringent demands on model accuracy to achieve good search efficiency. Finally, we report the top candidates of our spinel search, which may be of interest for synthesis experiments

History

Email Address of Submitting Author

ertekin@illinois.edu

Institution

Department of Mechanical Science and Engineering

Country

United States

ORCID For Submitting Author

0000-0002-7816-1803

Declaration of Conflict of Interest

None

Exports

Logo branding

Exports