Potential energy landscape of a coarse grained model for water: ML-BOP

We quantify the statistical properties of the potential energy landscape for a recently proposed machine learning coarse grained model for water, machine learning-bond-order potential Chan et al., Nat. Commun. 10, 379 (2019). We find that the landscape can be accurately modeled as a Gaussian landscape at all densities. The resulting landscape-based free-energy expression accurately describes the model properties in a very wide range of temperatures and densities. The density dependence of the Gaussian landscape parameters total number of inherent structures (ISs), characteristic IS energy scale, and variance of the IS energy distribution predicts the presence of a liquid–liquid transition located close to P = 1750 ± 100 bars and T = 181.5 ± 1 K.