Machine Learning Diffusion Monte Carlo EnergiesClick to copy article linkArticle link copied!
- Kevin Ryczko*Kevin Ryczko*Email: [email protected]Good Chemistry Company, Vancouver, British ColumbiaV6E 4B1, CanadaMore by Kevin Ryczko
- Jaron T. KrogelJaron T. KrogelMaterials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee37831, United StatesMore by Jaron T. Krogel
- Isaac Tamblyn*Isaac Tamblyn*Email: [email protected]Department of Physics, University of Ottawa, Ottawa, OntarioK1N 6N5, CanadaVector Institute for Artificial Intelligence, Toronto, OntarioM5G 1M1, CanadaMore by Isaac Tamblyn
Abstract
We present two machine learning methodologies that are capable of predicting diffusion Monte Carlo (DMC) energies with small data sets (≈60 DMC calculations in total). The first uses voxel deep neural networks (VDNNs) to predict DMC energy densities using Kohn–Sham density functional theory (DFT) electron densities as input. The second uses kernel ridge regression (KRR) to predict atomic contributions to the DMC total energy using atomic environment vectors as input (we used atom-centered symmetry functions, atomic environment vectors from the ANI models, and smooth overlap of atomic positions). We first compare the methodologies on pristine graphene lattices, where we find that the KRR methodology performs best in comparison to gradient boosted decision trees, random forest, Gaussian process regression, and multilayer perceptrons. In addition, KRR outperforms VDNNs by an order of magnitude. Afterward, we study the generalizability of KRR to predict the energy barrier associated with a Stone–Wales defect. Lastly, we move from 2D to 3D materials and use KRR to predict total energies of liquid water. In all cases, we find that the KRR models are more accurate than Kohn–Sham DFT and all mean absolute errors are less than chemical accuracy.
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