The trade-off between predictive power and efficiency in molecular simulations remains a central challenge in computational chemistry. Correlated electronic structure methods are accurate but computationally expensive and, therefore, are only used for small molecules. On the other hand, force fields are computationally efficient and enable simulations of millions of atoms, but display limited accuracy and effectively lack any predictive power, often adopting oversimplified representations of molecular interactions. In the last years, we have introduced an alternative data-driven theoretical/computational framework for developing accurate, yet efficient, molecular models which are rigorously derived from the many-body expansion of the interaction energy. We demonstrated that, independently of the system size, our MB-pol model for water as well as our MB-nrg models of halide and alkali-metal ions, carbon dioxide, and methane are able to represent the underlying molecular interactions with chemical accuracy and thus enable predictive molecular simulations from the gas to the condensed phase. Here, we discuss the first step towards extending our MB-nrg models to biomolecules, presenting a fully transferable, data-driven many-body model of N-methyl-acetamide (NMA) derived from CCSD(T) , which serves as a prototype of the peptide bond and can thus be seen as a simplified unit of the protein backbone. We present analyses of the intramolecular distortion energies and harmonic frequencies of an isolated NMA molecule in the gas phase, as well as the solvation properties of NMA in water. Particular focus is placed on the importance of many-body effects in the hydration properties of NMA, which are assessed through systematic many-body decompositions of the interaction energies of small NMA(H2O)n clusters and calculations of the hydration free energies. Detailed comparisons with common force fields, both pairwise additive and polarizable, are also discussed.

Many-body models with chemical accuracy for N-methyl-acetamide in water

ACS Spring 2022

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