Machine learning enhanced empirical potentials for metals and alloys
Interatomic potential (i.e. force-field) plays a vital role in atomistic simulation of materials. Empirical potentials like the embedded atom method (EAM) and its variant angular-dependent potential (ADP) have proven successful in many metals. In the past few years, machine learning has become a compelling approach for modeling interatomic interactions. Powered by big data and efficient optimizers, machine learning interatomic potentials can generally approximate to the accuracy of the first-principles calculations based on the quantum mechanics theory. In this works, we successfully developed a route to express EAM and ADP within machine learning framework in highly-vectorizable form and further incorporated several physical constraints into the training. As it is proved in this work, the performances of empirical potentials can be significantly boosted with few training data. For energy and force predictions, machine tuned EAM and ADP, can be almost as accurate as the computationally expensive spectral neighbor analysis potential (SNAP) on the fcc Ni, bcc Mo and Mo-Ni alloy systems. Machine learned EAM and ADP can also reproduce some key materials properties, such as elastic constants, melting temperatures and surface energies, close to the first-principles accuracy. Our results suggest a new and systematic route for developing machine learning interatomic potentials. All the new algorithms have been implemented in our program TensorAlloy.