High Accuracy Semi-Empirical Quantum Models Based on a Reduced Training Set

There exists a great need for computationally efficient quantum simulation approaches that can achieve an accuracy similar to high-level 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 organic materials. 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 ∼0.3% of data required for one advanced deep learning potential (ANI- 1x). In addition, the total number of data points in our training set is less than one half of that used for a recent DFTB-neural network model (trained on a separate dataset). Validation tests of our DFTB model against energy and vibrational data for gas-phase molecules for additional quantum datasets shows strong agreement with reference data from either hybrid density-functional theory, coupled-cluster calculations, or experiments. Preliminary testing on graphite and diamond successfully reproduce condensed phase structures. The models developed in this work, in principle, can retain most of the accuracy of quantum-based methods at any level of theory with relatively small training sets. Our efforts can thus allow for high throughput physical and chemical predictions with up to coupled-cluster accuracy for materials that are computationally intractable with standard approaches.