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Simulating liquid water accurately now uses machine learning potentials (MLPs) combined with local correlation approximations for coupled cluster theory [CCSD(T)] simulations. This practical approach achieves experimental agreement for water

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Quantum Mechanics

Background:

  • Accurate simulation of liquid water requires precise electronic structure methods and nuclear motion sampling.
  • Coupled cluster theory with single, double, and perturbative triple excitations [CCSD(T)] offers high accuracy but is computationally expensive for condensed phases.
  • Machine learning potentials (MLPs) have shown promise in achieving experimental agreement for liquid water simulations.

Purpose of the Study:

  • To develop a practical and routine approach for achieving CCSD(T)-level accuracy in liquid water simulations.
  • To combine machine learning potentials with local correlation approximations for efficient simulations.
  • To enable accurate prediction of both structural/transport properties and bulk properties like density maximum.

Main Methods:

  • Developed a practical approach combining machine learning potentials (MLPs) with local correlation approximations.
  • Enabled routine coupled cluster theory with single, double, and perturbative triple excitations [CCSD(T)]-level simulations for condensed phase systems.
  • Incorporated nuclear quantum effects and constant-pressure simulations for comprehensive property prediction.

Main Results:

  • Achieved agreement with experimental data for structural and transport properties of liquid water.
  • Successfully predicted isothermal-isobaric bulk properties, including water's density maximum, using MLP-based CCSD(T) models.
  • Demonstrated the practicality and routine applicability of the developed simulation approach.

Conclusions:

  • The combined approach of MLPs and local correlation approximations provides a practical blueprint for routine CCSD(T)-based simulations in condensed phases.
  • This methodology facilitates accurate prediction of liquid water properties, bridging the gap between theoretical accuracy and experimental data.
  • The work paves the way for broader application of high-accuracy electronic structure methods in condensed matter simulations.