In the whole training process, only model parameters are exchanged and no any external access or connection to the local databases.
To attack the problem and make full use of the material data, we propose a new strategy of neural network training based on multi-source databases. This dilemma gradually leads to the “data island” problem, especially in material science. At the meanwhile, some data owners prefer to protect the data and keep their initiative in the cooperation.
However, most material data are scattered among various research institutions, and a big data transmission will consume significant bandwidth and tremendous time. The fourth paradigm of science has achieved great success in material discovery and it highlights the sharing and interoperability of data.
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