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Toward High-level Machine Learning Potential for Water Based on Quantum Fragmentation and Neural Networks
Jinfeng Liu1,2, Jinggang Lan3, Xiao He2,4
1Department of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 210009, China.
The Journal of Physical Chemistry. A
|June 9, 2022
Summary
Researchers developed a deep machine learning potential (DP-MP2) for simulating liquid water. This method accurately predicts water
Area of Science:
- Computational Chemistry
- Materials Science
- Quantum Mechanics
Background:
- Simulating liquids like water with high-level wave function theories is computationally expensive.
- Accurate simulations are crucial for understanding liquid properties.
Purpose of the Study:
- To develop a computationally efficient deep machine learning potential for liquid water simulations.
- To achieve accuracy comparable to high-level wave function theories.
Main Methods:
- Developed a deep machine learning potential (DP-MP2) using neural networks.
- Based the potential on fragment-based second-order Møller-Plesset perturbation theory.
- Investigated nuclear quantum effects (NQEs).
Main Results:
- DP-MP2 potential accurately predicts structural, dynamical, and thermodynamic properties of liquid water.
- Results show improved agreement with experimental data compared to DFT methods.
- NQEs were found to significantly impact water's properties under ambient conditions.
Conclusions:
- The DP-MP2 potential offers a computationally efficient framework for simulating condensed-phase systems.
- Achieves high accuracy, approaching that of wave function theories.
- Enables significant computational savings over traditional ab initio simulations.
