Predicting Properties of Periodic Systems from Cluster Data: A Case Study of Liquid Water

Abstract

The accuracy of the training data limits the accuracy of bulk properties from machine-learned potentials. For example, hybrid functionals or wave-function-based quantum chemical methods are readily available for cluster data but effectively out-of-scope for periodic structures. We show that local, atom-centred descriptors for machine-learned potentials enable the prediction of bulk properties from cluster model training data, agreeing reasonably well with predictions from bulk training data. We demonstrate such transferability by studying structural and dynamical properties of bulk liquid water with density functional theory and have found an excellent agreement with experimental as well as theoretical counterparts.

References in corpus (38)

• Adam: A Method for Stochastic Optimization
• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• On representing chemical environments
• Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
• Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
• Moment Tensor Potentials: a class of systematically improvable interatomic potentials
• Machine Learning of Accurate Energy-Conserving Molecular Force Fields
• PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges
• Less is more: sampling chemical space with active learning
• Perspective: How good is DFT for water?
• Machine Learning Molecular Dynamics for the Simulation of Infrared Spectra
• Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation
• How van der Waals interactions determine the unique properties of water
• SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
• The Phase Diagram of a Deep Potential Water Model
• FCHL revisited: faster and more accurate quantum machine learning
• Alchemical and structural distribution based representation for improved QML
• The Individual and Collective Effects of Exact Exchange and Dispersion Interactions on the Ab Initio Structure of Liquid Water
• Interatomic potentials for ionic systems with density functional accuracy based on charge densities obtained by a neural network
• Hierarchical modeling of molecular energies using a deep neural network
• Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation
• An Accurate and Transferable Machine Learning Potential for Carbon
• Equivariant message passing for the prediction of tensorial properties and molecular spectra
• Committee neural network potentials control generalization errors and enable active learning
• Machine learning of molecular properties: locality and active learning
• Automated Fitting of Neural Network Potentials at Coupled Cluster Accuracy: Protonated Water Clusters as Testing Ground
• Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials
• Uncertainty estimation for molecular dynamics and sampling
• PiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials
• Deep Learning for UV Absorption Spectra with SchNarc: First Steps Towards Transferability in Chemical Compound Space
• Efficient Training of ANN Potentials by Including Atomic Forces via Taylor Expansion and Application to Water and a Transition-Metal Oxide
• Comparing the Accuracy of High-Dimensional Neural Network Potentials and the Systematic Molecular Fragmentation Method: A Benchmark Study for all-trans Alkanes
• A Novel Approach to Describe Chemical Environments in High Dimensional Neural Network Potentials
• Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments
• Neural-Network Assisted Study of Nitrogen Atom Dynamics on Amorphous Solid Water. I. Adsorption & Desorption
• Transferability of machine learning potentials: Protonated water neural network potential applied to the protonated water hexamer
• Perspective: Non-conventional Force Fields for Applications in Spectroscopy and Chemical Reaction Dynamics
• Isomerization and Decomposition Reactions of Acetaldehyde Relevant to Atmospheric Processes from Dynamics Simulations on Neural Network-Based Potential Energy Surfaces

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