Accurate Water Properties from an Efficient ab Initio Method.

Area of Science:

• Computational Chemistry
• Materials Science
• Physical Chemistry

Background:

• Accurate prediction of water properties using low-cost ab initio methods remains a significant challenge.
• Existing density functional approaches struggle to simultaneously model energies, structures, dynamics, and isomer stability of water.
• Incorporating long-range van der Waals interactions has not sufficiently improved the prediction of water isomer stability.

Purpose of the Study:

• To evaluate the performance of a revised Tao-Mo (revTM) semilocal meta-generalized gradient approximation functional for predicting water properties.
• To assess the accuracy of revTM for describing water clusters and ice phases.
• To compare the efficacy of revTM against other popular ab initio methods for water property prediction.

Main Methods:

• Utilized the revised Tao-Mo (revTM) semilocal meta-generalized gradient approximation exchange-correlation functional.
• Applied ab initio calculations to investigate properties of water clusters and ice phases.
• Compared the performance of revTM with several established ab initio methods.

Main Results:

• The revTM functional accurately predicts multiple properties of water clusters and ice phases.
• Demonstrated consistent improvement over several popular ab initio methods.
• Showcased the method's capability in accurately describing hydrogen bonding in water.

Conclusions:

• The revised Tao-Mo (revTM) functional provides a low-cost and accurate method for predicting diverse water properties.
• revTM offers a significant advancement over existing methods for computational studies of water.
• This approach enhances the understanding of water's behavior in clusters and condensed phases, particularly its hydrogen bonding network.