Neural-Network Predictive Modeling of Physical Properties in Binary Magnetic and Non-Magnetic Alloys

We present a deep learning (DL) approach to reproduce the first principles Density Functional Theory (DFT) based calculations pertaining to macroscopic physical properties of a non-magnetic (CuAu) and a magnetic (FePt) binary alloys. In this study, a neural network (NN) is developed and trained using thousands of theoretically possible lattice configurations obtained from the Locally Self-Consistent Multiple Scattering (LSMS) DFT code [1]. The intrinsic physical properties of alloys like composition ratio, unit-cell structure, spatial charge distributions, Coulombic interactions, etc. are inputted into the NN model structured by the “bag-of-bonds” representation [2]. The NN regression model is trained to capture the relationship between intrinsic parameters and the total energy of the alloys. Although NNs are complex and computationally expensive to train, they are flexible and can effectively pick up nonlinear relationships between inputs and outputs. Our results show that the trained NN model is orders-of-magnitude faster than DFT in inferring the total energy with comparable accuracy [3]. This demonstrates the potential of applying the NN formalism in accelerating the computational studies of condensed matter systems.

 

[1] LSMS. Computer software. https://www.osti.gov//servlets/purl/1420087. Vers. 00. USDOE. 1 Dec. 2017. Web.

[2] J. Phys. Chem. Lett. 6, 12, 2326–2331 (2015).

[3] J. Phys.: Condens. Matter 33, 084005 (2021).