Abstract
Most machine learning (ML) platforms used in materials science provide prediction models built using a computational database. However, to provide more practical and accurate material property predictions, it is advantageous to build a prediction model with user’s own data. Here, we present a web-based ML platform, SimPL-ML (https://www.simpl-ml.org) that enables the user to build an ML prediction model through a simple process using their own data. Our platform, SimPL-ML, comprises four main parts: a dataset editor for dataset preprocessing, a model generator to perform actual model training, a predictor to provide a predicted target value for an arbitrary input, and a band-gap predictor (as an example case study) to predict the band gap of inorganic materials through several optimized band gap prediction models. In addition to its core functions, SimPL-ML provides additional functions such as atomic feature generation and hyper-parameter optimization for efficient ML research. We expect our platform to facilitate more accurate and efficient materials research through ML.
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Acknowledgements
This research was supported by the core KRICT project from the Korea Research Institute of Chemical Technology (SI2151-10) and the Nano-Material Technology Development Program through the National Research Foundation of Korea (NRF-2016M3A7B4025408).
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Jang, S., Na, G.S., Lee, J. et al. An Easy, Simple, and Accessible Web-based Machine Learning Platform, SimPL-ML. Integr Mater Manuf Innov 11, 85–94 (2022). https://doi.org/10.1007/s40192-022-00250-x
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DOI: https://doi.org/10.1007/s40192-022-00250-x