Smart energy models for atomistic simulations using a DFT-driven multifidelity approach
This work addresses the efficiency problem for researchers in computational materials science, offering an incremental improvement over existing machine-learning techniques.
The paper tackles the computational expense of high-fidelity energy models in atomistic simulations by introducing a multifidelity approach that predicts high-fidelity outcomes from low-fidelity estimates, reducing the need for extensive training data compared to neural networks; it demonstrates this method on vacancy diffusion in iron-copper alloys, showing competitive performance.
The reliability of atomistic simulations depends on the quality of the underlying energy models providing the source of physical information, for instance for the calculation of migration barriers in atomistic Kinetic Monte Carlo simulations. Accurate (high-fidelity) methods are often available, but since they are usually computationally expensive, they must be replaced by less accurate (low-fidelity) models that introduce some degrees of approximation. Machine-learning techniques such as artificial neural networks are usually employed to work around this limitation and extract the needed parameters from large databases of high-fidelity data, but the latter are often computationally expensive to produce. This work introduces an alternative method based on the multifidelity approach, where correlations between high-fidelity and low-fidelity outputs are exploited to make an educated guess of the high-fidelity outcome based only on quick low-fidelity estimations, hence without the need of running full expensive high-fidelity calculations. With respect to neural networks, this approach is expected to require less training data because of the lower amount of fitting parameters involved. The method is tested on the prediction of ab initio formation and migration energies of vacancy diffusion in iron-copper alloys, and compared with the neural networks trained on the same database.