Abstract
We present OrbNet Denali, a machine learning model for an electronic structure that is designed as a drop-in replacement for ground-state density functional theory (DFT) energy calculations. The model is a message-passing graph neural network that uses symmetry-adapted atomic orbital features from a low-cost quantum calculation to predict the energy of a molecule. OrbNet Denali is trained on a vast dataset of 2.3 × 106 DFT calculations on molecules and geometries. This dataset covers the most common elements in biochemistry and organic chemistry (H, Li, B, C, N, O, F, Na, Mg, Si, P, S, Cl, K, Ca, Br, and I) and charged molecules. OrbNet Denali is demonstrated on several well-established benchmark datasets, and we find that it provides accuracy that is on par with modern DFT methods while offering a speedup of up to three orders of magnitude. For the GMTKN55 benchmark set, OrbNet Denali achieves WTMAD-1 and WTMAD-2 scores of 7.19 and 9.84, on par with modern DFT functionals. For several GMTKN55 subsets, which contain chemical problems that are not present in the training set, OrbNet Denali produces a mean absolute error comparable to those of DFT methods. For the Hutchison conformer benchmark set, OrbNet Denali has a median correlation coefficient of R2 = 0.90 compared to the reference DLPNO-CCSD(T) calculation and R2 = 0.97 compared to the method used to generate the training data (ωB97X-D3/def2-TZVP), exceeding the performance of any other method with a similar cost. Similarly, the model reaches chemical accuracy for non-covalent interactions in the S66x10 dataset. For torsional profiles, OrbNet Denali reproduces the torsion profiles of ωB97X-D3/def2-TZVP with an average mean absolute error of 0.12 kcal/molfor the potential energy surfaces of the diverse fragments in the TorsionNet500 dataset.
Highlights
Theoretical chemistry is based on the strategy of using approximate methods to make predictions for chemical problems
We demonstrate the performance and transferability of the trained model by benchmarking OrbNet Denali across the many diverse chemical problems in the well-established collection of benchmark sets, GMTKN55.32 we demonstrate the performance of OrbNet Denali on several other essential tasks, such as conformer scoring, non-covalent interactions, and torsional profiles.[33,34,35]
The dataset consists of 55 individual subsets with a total of 1505 relative energies based on 2462 single-point calculations
Summary
Theoretical chemistry is based on the strategy of using approximate methods to make predictions for chemical problems. Recent developments in machine learning models for chemistry have led to many new strategies for making energy predictions of molecules.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29] Relatively few of these, have sufficient accuracy and breadth of training to provide a viable replacement for quantum chemistry methods, such as DFT, without needing to retrain the model for specialized purposes. The difficulty of constructing such machine learning models that can make reliable predictions across a large fraction of chemical space is two-fold: (i) the model must capture the underlying physics, and (ii) relevant and well-curated training data covering the relevant chemical problems must be available
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