Abstract
Recent impressive achievements on artificial intelligence and its technologies have expected to bring our daily life to the yet-experienced new world. Such technologies have also applied in the literature of materials science especially on data-driven materials research so that they could reduce the computing resources and alleviate redundant simulations. Nevertheless, since these are still in the immature stage, most of the datasets are private and have made according to their own standards and policies, therefore, they are hard to be merged as well as analyzed together. We have developed the Scientific Data Repository platform to store various and complicated data including materials data, which can analyze such data on the web. As the second step, we develop a machine learning based materials properties prediction tool enabling the fast discovery of advanced materials by using the general-purpose high-precise formation energy prediction module that performs MAE 0.066 within 10 s on the web.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ward L et al (2016) A general-purpose machine learning framework for predicting properties of inorganic materials. Nat Partn J Comput Mater 2
Liu R et al (2016) Deep learning for chemical compound stability prediction. In: Proceedings of the ACM SIGKDD workshop on large-scale deep learning for data mining (DL-KDD), San Francisco, USA
Jain A et al (2013) Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Mater 1:011002
The NoMaD Repository. http://nomadrepository.eu
MatNavi. http://mits.nims.go.jp/index_en.html
Chollet et al. Keras, http://github.com/fchollet/keras
Abadi M et al (2016) Tensorflow: large-scale machine learning on heterogeneous distributed systems. Distrib Parallel Cluster Comput
Glorot X et al (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the 13th international conference on artificial intelligence and statistics
Hansen K et al (2013) Assessment and validation of machine learning methods for predicting molecular atomization energies. J Chem Theory Comput 9(8):3404–3419
Acknowledgements
This research was supported by the EDISON Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (No. NRF-2011-0,020,576), the KISTI Program (No. K-17-L01-C02).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Lee, J., Ahn, S., Kim, J., Lee, S., Cho, K. (2019). Machine Learning Based Materials Properties Prediction Platform for Fast Discovery of Advanced Materials. In: Park, J., Loia, V., Choo, KK., Yi, G. (eds) Advanced Multimedia and Ubiquitous Engineering. MUE FutureTech 2018 2018. Lecture Notes in Electrical Engineering, vol 518. Springer, Singapore. https://doi.org/10.1007/978-981-13-1328-8_21
Download citation
DOI: https://doi.org/10.1007/978-981-13-1328-8_21
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1327-1
Online ISBN: 978-981-13-1328-8
eBook Packages: EngineeringEngineering (R0)