Improved accuracy and transferability of molecular-orbital-based machine learning: Organics, transition-metal complexes, non-covalent interactions, and transition states

Abstract

Molecular-orbital-based machine learning (MOB-ML) provides a general framework for the prediction of accurate correlation energies at the cost of obtaining molecular orbitals. We demonstrate the importance of preserving physical constraints, including invariance conditions and size consistency, when generating the input for the machine learning model. Numerical improvements are demonstrated for different data sets covering total and relative energies for thermally accessible organic and transition-metal containing molecules, non-covalent interactions, and transition-state energies. MOB-ML requires training data from only 1% of the QM7b-T data set (i.e., only 70 organic molecules with seven and fewer heavy atoms) to predict the total energy of the remaining 99% of this data set with sub-kcal/mol accuracy. This MOB-ML model is significantly more accurate than other methods when transferred to a data set comprised of thirteen heavy atom molecules, exhibiting no loss of accuracy on a size intensive (i.e., per-electron) basis. It is shown that MOB-ML also works well for extrapolating to transition-state structures, predicting the barrier region for malonaldehyde intramolecular proton-transfer to within 0.35 kcal/mol when only trained on reactant/product-like structures. Finally, the use of the Gaussian process variance enables an active learning strategy for extending MOB-ML model to new regions of chemical space with minimal effort. We demonstrate this active learning strategy by extending a QM7b-T model to describe non-covalent interactions in the protein backbone-backbone interaction data set to an accuracy of 0.28 kcal/mol.

References in corpus (26)

• Scikit-learn: Machine Learning in Python
• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• Quantum-Chemical Insights from Deep Tensor Neural Networks
• Intrinsic atomic orbitals: An unbiased bridge between quantum theory and chemical concepts
• PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges
• Big Data meets Quantum Chemistry Approximations: The $\Delta$-Machine Learning Approach
• By-passing the Kohn-Sham equations with machine learning
• SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
• FCHL revisited: faster and more accurate quantum machine learning
• Hierarchical modeling of molecular energies using a deep neural network
• A Transferable Machine-Learning Model of the Electron Density
• OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features
• New Benchmark Set of Transition-Metal Coordination Reactions for the Assessment of Density Functionals
• Transferability in Machine Learning for Electronic Structure via the Molecular Orbital Basis
• Predicting Electronic Structure Properties of Transition Metal Complexes with Neural Networks
• Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions
• Operators in Machine Learning: Response Properties in Chemical Space
• A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Molecules
• Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures
• Calculation of Ligand Dissociation Energies in Large Transition-Metal Complexes
• Ground state energy functional with Hartree-Fock efficiency and chemical accuracy
• Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learning
• Learning the energy curvature versus particle number in approximate density functionals
• Analytical Gradients for Molecular-Orbital-Based Machine Learning

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