Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions

The free energy of a system is central to many material models. Although free energy data is not generally found directly, its derivatives can be observed or calculated. In this work, we present an Integrable Deep Neural Network (IDNN) that can be trained to derivative data obtained from atomic scal...

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