Improving the accuracy of Møller-Plesset perturbation theory with neural networks.
By R. McGibbon, Andrew G. Taube, A. Donchev, Karthik Siva, Felipe Hernández, Cory Hargus, Ka-Hei Law, John L. Klepeis, David E. Shaw. 2017
102 citations recorded by the source corpus.
Noncovalent interactions are of fundamental importance across the disciplines of chemistry, materials science, and biology. Quantum chemical calculations on noncovalently bound complexes, which allow for the quantification of properties such as binding energies and geometries, play an essential role in advancing our understanding of, and building models for, a vast array of complex processes involving molecular association or self-assembly. Because of its relatively modest computational cost, second-order Møller-Plesset perturbation (MP2) theory is one of the most widely used methods in quantum chemistry for studying noncovalent interactions. MP2 is, however, plagued by serious errors due to its incomplete treatment of electron correlation, especially when modeling van der Waals interactions and π-stacked complexes. Here we present spin-network-scaled MP2 (SNS-MP2), a new semi-empirical MP2-based method for dimer interaction-energy calculations. To correct for errors in MP2, SNS-MP2 uses quantum chemical features of the complex under study in conjunction with a neural network to reweight terms appearing in the total MP2 interaction energy. The method has been trained on a new data set consisting of over 200 000 complete basis set (CBS)-extrapolated coupled-cluster interaction energies, which are considered the gold standard for chemical accuracy. SNS-MP2 predicts gold-standard binding energies of unseen test compounds with a mean absolute error of 0.04 kcal mol-1 (root-mean-square error 0.09 kcal mol-1), a 6- to 7-fold improvement over MP2. To the best of our knowledge, its accuracy exceeds that of all extant density functional theory- and wavefunction-based methods of similar computational cost, and is very close to the intrinsic accuracy of our benchmark coupled-cluster methodology itself. Furthermore, SNS-MP2 provides reliable per-conformation confidence intervals on the predicted interaction energies, a feature not available from any alternative method.
Subfields: Atomic and Molecular Physics, and Optics, Computational Theory and Mathematics, Materials Chemistry
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Citations in the corpus
- Psi4 1.4: Open-source software for high-throughput quantum chemistry. (2020, methodology): “McGibbon and coworkers 217 applied a neural network trained on HF and MP2 IE and SAPT0 terms to predict system-specific scaling factors for MP2 same-and opposite-spin correlation energies to define the spin-network-scaled, SNS-MP2, method.”
- A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks (2019, background): “BDL has already been demonstrated to play a crucial role in applications such as medical diagnostics [30, 16, 5, 49] (see Figure 1), computer vision [21, 18, 17], in the sciences [31, 34], and autonomous driving [1, 15, 21, 18, 19].”