Size and Quality of Quantum Mechanical Data Sets for Training Neural Network Force Fields for Liquid Water

Abstract

Molecular dynamics simulations have been used in different scientific fields to investigate a broad range of physical systems. However, the accuracy of calculation is based on the model considered to describe the atomic interactions. In particular, ab initio molecular dynamics (AIMD) has the accuracy of density functional theory (DFT), and thus is limited to small systems and relatively short simulation time. In this scenario, Neural Network Force Fields (NNFF) have an important role, since it provides a way to circumvent these caveats. In this work we investigate NNFF designed at the level of DFT to describe liquid water, focusing on the size and quality of the training data-set considered. We show that structural properties are less dependent on the size of the training data-set compared to dynamical ones (such as the diffusion coefficient), and a good sampling (selecting data reference for training process) can lead to a small sample with good precision.

References in corpus (26)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
• Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
• SchNet - a deep learning architecture for molecules and materials
• DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
• E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
• Machine Learning Force Fields
• Machine learning for molecular simulation
• Perspective: How good is DFT for water?
• An exchange functional that tests the robustness of the plasmon description of the van der Waals density functional
• Competing quantum effects in the dynamics of a flexible water model
• Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation
• Ab initio theory and modeling of water
• Isothermal-isobaric molecular dynamics using stochastic velocity rescaling
• The Phase Diagram of a Deep Potential Water Model
• Automatic Selection of Atomic Fingerprints and Reference Configurations for Machine-Learning Potentials
• sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning
• Deep Potentials for Materials Science
• Self-Consistent Determination of Long-Range Electrostatics in Neural Network Potentials
• Phase equilibrium of water with hexagonal and cubic ice using the SCAN functional
• Structural, Electronic, and Dynamical Properties of Liquid Water by ab initio Molecular Dynamics based on SCAN Functional within the Canonical Ensemble
• Quantum-mechanical exploration of the phase diagram of water
• Viscosity in water from first-principles and deep-neural-network simulations
• PANNA: Properties from Artificial Neural Network Architectures
• Benchmarking Structural Evolution Methods for Training of Machine Learned Interatomic Potentials

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