Reducing Optimal Training Set Design with Many-Body Repulsive Potentials for High Accuracy Density-Functional Tight Binding Models

There exists a great need for computationally efficient simulation approaches that can achieve an accuracy like high-level quantum theories while exhibiting a wide degree of transferability. In this regard, we have leveraged a machine-learned force field based on Chebyshev polynomials to determine Density Functional Tight Binding (DFTB) models for gas-phase organic molecules. The benefit of our approach is two-fold: (1) many-body interactions can be corrected for in a systematic and rapidly tunable process, and (2) high-level quantum accuracy for a broad range of compounds can be achieved with less than 1% of data required for standard deep learning potentials. Validation tests of our DFTB model against energy and vibrational data for gas-phase molecules shows strong agreement with reference data from either hybrid density-functional theory (DFT), coupled-cluster calculations, or experiments. The transferability of our model for condensed phase simulations is then illustrated through results for several phases of carbon. The techniques discussed in this work can retain the accuracy of quantum chemical theory at any level with relatively small training sets. Our efforts can allow for high throughput physical and chemical predictions with up to coupled cluster accuracy for materials that are computationally intractable with standard approaches.