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.
Potential energy landscape of a coarse grained model for water. ML-BOP / Neophytou, Andreas; Sciortino, Francesco. - In: THE JOURNAL OF CHEMICAL PHYSICS. - ISSN 0021-9606. - 160:11(2024), pp. 1-9. [10.1063/5.0197613]
Potential energy landscape of a coarse grained model for water. ML-BOP
Neophytou, Andreas;Sciortino, Francesco
2024
Abstract
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.File | Dimensione | Formato | |
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