The MD17 Datasets from the Perspective of Datasets for Gas-Phase "Small" Molecule Potentials

Abstract

There has been great progress in developing methods for machine-learned potential energy surfaces. There have also been important assessments of these methods by comparing so-called learning curves on datasets of electronic energies and forces, notably the MD17 database. The dataset for each molecule in this database generally consists of tens of thousands of energies and forces obtained from DFT direct dynamics at 500 K. We contrast the datasets from this database for three "small" molecules, ethanol, malonaldehyde, and glycine, with datasets we have generated with specific targets for the PESs in mind: a rigorous calculation of the zero-point energy and wavefunction, the tunneling splitting in malonaldehyde and in the case of glycine a description of all eight low-lying conformers. We found that the MD17 datasets are too limited for these targets. We also examine recent datasets for several PESs that describe small-molecule but complex chemical reactions. Finally, we introduce a new database, "QM-22", which contains datasets of molecules ranging from 4 to 15 atoms that extend to high energies and a large span of configurations.

References in corpus (19)

• Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• Machine Learning of Accurate Energy-Conserving Molecular Force Fields
• PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges
• Nuclear quantum effects enter the mainstream
• Efficient stochastic thermostatting of path integral molecular dynamics
• sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning
• Perspective on integrating machine learning into computational chemistry and materials science
• Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions
• Structure-based Sampling and Self-correcting Machine Learning for Accurate Calculations of Potential Energy Surfaces and Vibrational Levels
• Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning
• Molecular Force Fields with Gradient-Domain Machine Learning: Construction and Application to Dynamics of Small Molecules with Coupled Cluster Forces
• Relation of centroid molecular dynamics and ring-polymer molecular dynamics to exact quantum dynamics
• Towards empirical force fields that match experimental observables
• Permutationally invariant polynomial regression for energies and gradients, using reverse differentiation, achieves orders of magnitude speed-up with high precision compared to other machine learning methods
• Challenges for Machine Learning Force Fields in Reproducing Potential Energy Surfaces of Flexible Molecules
• Improved semiclassical dynamics through adiabatic switching trajectory sampling
• Perspective: Non-conventional Force Fields for Applications in Spectroscopy and Chemical Reaction Dynamics
• Representing Molecular Ground and Excited Vibrational Eigenstates with Nuclear Densities obtained from Semiclassical Initial Value Representation Molecular Dynamics

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• No Headache for PIPs: A PIP Potential for Aspirin Outperforms Other Machine-Learned Potentials
• Orders-of-coupling representation with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization
• Tell machine learning potentials what they are needed for: Simulation-oriented training exemplified for glycine