Capturing the potential energy landscape of large size molecular clusters from atomic interactions up to a 4-body system using deep learning.

Exploring the structure and properties of molecular clusters with accuracy using the methods is a resource intensive task due to the increasing cost of the methods and the number of distinct conformers as the size increases. The energy landscape of methanol clusters has been previously explored using computationally efficient empirical models to collect a database of structurally distinct minima, followed by re-optimization using methods. In this work, we propose a new method that utilizes the database of stable conformers and borrow the fragmentation concept of many-body-expansion (MBE) methods in methods to train a deep-learning machine learning (ML) model using SchNet. Picking 684 local minima of (CHOH) to (CHOH) from the existing database, we can generate ∼51 000 data points of one-body, two-body, three-body and four-body molecular systems to train an ML model to reach a mean absolute error (MAE) of 3.19 kJ mol (in energy) and 2.48 kJ mol Å (in forces) tested against calculations up to (CHOH). This ML model is then used to create a database of low energy isomers of (CHOH) ( = 15-20). The proposed scheme can be applied to other hydrogen bonded molecular clusters with an accuracy of first-principles methods and computational speed of empirical force-fields.