This paper describes a generalizable framework for creating context-aware wall-time prediction models for HPC applications. This framework: (a) cost-effectively generates comprehensive application-specific training data, (b) provides an application-independent machine learning pipeline that trains different regression models over the training datasets, and (c) establishes context-aware selection criteria for model selection. We explain how most of the training data can be generated on commodity or contention-free cyberinfrastructure and how the predictive models can be scaled to the production environment with the help of a limited number of resource-intensive generated runs (we show almost seven-fold cost reductions along with better performance). Our machine learning pipeline does feature transformation, and dimensionality reduction, then reduces sampling bias induced by data imbalance. Our context-aware model selection algorithm chooses the most appropriate regression model for a given target application that reduces the number of underpredictions while minimizing overestimation errors.
Index Terms—AI4CI, Data Science Workflow, Custom ML Models, HPC, Data Generation, Scheduling, Resource Estimations
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Predicting the formation of fractionally doped perovskite oxides by a function-confined machine learning method
Abstract Fractionally doped perovskites oxides (FDPOs) have demonstrated ubiquitous applications such as energy conversion, storage and harvesting, catalysis, sensor, superconductor, ferroelectric, piezoelectric, magnetic, and luminescence. Hence, an accurate, cost-effective, and easy-to-use methodology to discover new compositions is much needed. Here, we developed a function-confined machine learning methodology to discover new FDPOs with high prediction accuracy from limited experimental data. By focusing on a specific application, namely solar thermochemical hydrogen production, we collected 632 training data and defined 21 desirable features. Our gradient boosting classifier model achieved a high prediction accuracy of 95.4% and a high F1 score of 0.921. Furthermore, when verified on additional 36 experimental data from existing literature, the model showed a prediction accuracy of 94.4%. With the help of this machine learning approach, we identified and synthesized 11 new FDPO compositions, 7 of which are relevant for solar thermochemical hydrogen production. We believe this confined machine learning methodology can be used to discover, from limited data, FDPOs with other specific application purposes.
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- Award ID(s):
- 2025541
- PAR ID:
- 10356865
- Date Published:
- Journal Name:
- Communications Materials
- Volume:
- 3
- Issue:
- 1
- ISSN:
- 2662-4443
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s43246-022-00269-9
