An Easy, Simple, and Accessible Web-based Machine Learning Platform, SimPL-ML

• Published in: Integrating materials and manufacturing innovation 2022-03, Vol.11 (1), p.85-94
• Main Authors: Jang, Seunghun, Na, Gyoung S., Lee, Jungho, Shin, Jung Ho, Kim, Hyun Woo, Chang, Hyunju
• Format: Article
• Language: English
• Subjects: Characterization and Evaluation of Materials; Chemistry and Materials Science; Datasets; Energy gap; Inorganic materials; Machine learning; Material properties; Materials Science; Metallic Materials; Nanotechnology; Optimization; Prediction models; Structural Materials; Surfaces and Interfaces; Technical Article; Thin Films
• ISSN: 2193-9764; 2193-9772
• DOI: 10.1007/s40192-022-00250-x

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.