Machine learning potentials for complex aqueous systems made simple.

ABSTRACT: Simulation techniques based on accurate and efficient representations of potential energy surfaces are urgently needed for the understanding of complex systems such as solid-liquid interfaces. Here we present a machine learning framework that enables the efficient development and validation of models for complex aqueous systems. Instead of trying to deliver a globally optimal machine learning potential, we propose to develop models applicable to specific thermodynamic state points in a simple and user-friendly process. After an initial ab initio simulation, a machine learning potential is constructed with minimum human effort through a data-driven active learning protocol. Such models can afterward be applied in exhaustive simulations to provide reliable answers for the scientific question

SUBMITTER: Schran C 

PROVIDER: S-EPMC8463804 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature