Benchmarking PES‐Learn's machine learning models predicting accurate potential energy surface for quantum scattering

Abstract

Abstract Machine learning (ML) models, neural networks, and Gaussian processes have been used to predict the potential energy surface taking C 2 ‐He (both static and dynamic scenario) and NCCN‐He collision systems. The surface is restricted to ∽125 points where traditional spline becomes inefficacious. Quantum dynamics is performed by solving close‐coupling equation to compute cross sections benchmarking the performance of the ML models. The current study forms a basis for any future investigation of larger molecules where conventional fitting fails due to sparser ab initio points and cuts down the computational time without compromising on the quality of the surface.

Topics & Concepts