Aditya Dilip Lele

Machine learning (ML) provides a great opportunity for the construction of models with improved accuracy in classical molecular dynamics (MD). However, the accuracy of a ML trained model is limited by the quality and quantity of the training data. Generating large sets of accurate ab initio training data can require significant computational resources.

Modelling the effect of surface charging on plasma synthesis of ammonia using DFT† Non-equilibrium plasma has been found to have a synergistic effect on catalytic synthesis of NH3. The non-equilibrium plasma and catalyst surface together could affect NH3 synthesis through several mechanisms. Charging of the catalyst surface in the presence of non-equilibrium plasma is one such mechanism.