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A great need exists for computationally efficient quantum simulation approaches that can achieve an accuracy similar to high-level theories at a fraction of the computational cost. In this regard, we have leveraged a machine-learned interaction potential based on Chebyshev polynomials to improve 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. Our model exhibits both transferability and extensibility through comparison to quantum chemical results for organic clusters, solid carbon phases, and molecular crystal phase stability rankings. Our efforts thus allow for high-throughput physical and chemical predictions with up to coupled-cluster accuracy for systems that are computationally intractable with standard approaches.

The spin-crossover behavior of the {Fe(pz)[Pt(CN)4]} metal–organic framework (MOF) upon pyrazine adsorption is investigated through hybrid Monte Carlo/molecular dynamics (MC/MD) simulations. In contrast to previous theoretical studies, which reported a transition temperature of ∼140 K, the present MC/MD simulations predict that the high-spin state is the most stable state at all temperatures, in agreement with the experimental observations. The MC/MD simulations also indicate that the pyrazine molecules adsorbed in the MOF pores lie nearly parallel but staggered by 60° relative to the pyrazine ligands of the framework. The analysis of the magnetization curve as a function of the temperature demonstrates that the staggered configuration assumed by the guest pyrazine molecules within the framework is responsible for the stabilization of the high-spin state. Both the guest pyrazine molecules and the pyrazine ligands of the framework are effectively locked into the minimum-energy configuration and do not disp...