Machine learning the computational cost of quantum chemistry

Abstract

Computational quantum mechanics based molecular and materials design campaigns consume increasingly more high-performance compute resources, making improved job scheduling efficiency desirable in order to reduce carbon footprint or wasteful spending. We introduce quantum machine learning (QML) models of the computational cost of common quantum chemistry tasks. For 2D non-linear toy systems, single point, geometry optimization, and transition state calculations the out of sample prediction error of QML models of wall times decays systematically with training set size. We present numerical evidence for a toy system containing two functions and three commonly used optimizer and for thousands of organic molecular systems including closed and open shell equilibrium structures, as well as transition states. Levels of electronic structure theory considered include B3LYP/def2-TZVP, MP2/6-311G(d), local CCSD(T)/VTZ-F12, CASSCF/VDZ-F12, and MRCISD+Q-F12/VDZ-F12. In comparison to conventional indiscriminate job treatment, QML based wall time predictions significantly improve job scheduling efficiency for all tasks after training on just thousands of molecules. Resulting reductions in CPU time overhead range from 10% to 90%.

References in corpus (17)

• Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
• SchNet - a deep learning architecture for molecules and materials
• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• Quantum-Chemical Insights from Deep Tensor Neural Networks
• Big Data meets Quantum Chemistry Approximations: The $\Delta$-Machine Learning Approach
• Machine Learning Unifies the Modelling of Materials and Molecules
• Machine learning prediction errors better than DFT accuracy
• Machine Learning of Molecular Electronic Properties in Chemical Compound Space
• ANI-1: A data set of 20M off-equilibrium DFT calculations for organic molecules
• Alchemical and structural distribution based representation for improved QML
• Hierarchical modeling of molecular energies using a deep neural network
• Understanding molecular representations in machine learning: The role of uniqueness and target similarity
• Predicting Electronic Structure Properties of Transition Metal Complexes with Neural Networks
• Guest Editorial: Special Topic on Data-enabled Theoretical Chemistry
• Error-Controlled Exploration of Chemical Reaction Networks with Gaussian Processes
• Solid Harmonic Wavelet Scattering for Predictions of Molecule Properties
• Predictive Performance Modeling for Distributed Computing using Black-Box Monitoring and Machine Learning

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