Capturing long-range interaction with reciprocal space neural network

Understand

Machine Learning (ML) interatomic models and potentials have been widely employed in simulations of materials.

• Long-range interactions often dominate in some ionic systems whose dynamics behavior is significantly influenced.
• However, the long-range effect such as Coulomb and Van der Wales potential is not considered in most ML interatomic potentials.
• To address this issue, we put forward a method that can take long-range effects into account for most ML local interatomic models with the reciprocal space neural network.