Intra- and Intermolecular Interactions in Cellobiose: Benchmarking Accelerated Computational Methods for Cellulose

Access to accelerated computational methods at the electronic and atomistic scales is important in materials modeling, as it will allow more realistic system descriptions. Against this background, a systematic study of a range of electronic/atomistic methods applied to cellobiose monomers and dimers is presented, with the aim of identifying methods that achieve a significant acceleration of cellulose computations compared to hybrid DFT methods while retaining adequate accuracy. We benchmark such methods, namely the semiempirical quantum-mechanical tight-binding GFNn-xTB (n = 1,2) and G-xTB methods, the MACE-OMol-0 machine-learning potential, and the CHARMM36 force field, against three DFT references (ωB97M-V, B3LYP-D3, PBE-D3) for gas-phase cellobiose monomers and dimers, focusing on structures, energies, and vibrational properties. Cellobiose monomers and dimers, as presented in this work, do not represent the structural complexity of cellulose, but they provide tractable structural models for assessing the qualities of the selected computational models regarding cellobiose–cellobiose intermolecular interactions that are also present in cellulose. We find that (i) the chain–chain and sheet–sheet dimer interactions are of comparable magnitude, suggesting that both intermolecular dispersion and hydrogen bonding make important contributions to dimer stability, so accurate treatment of noncovalent interactions the calculations is essential; (ii) GFN2-xTB and G-xTB reproduce most conformations well, with a speed-up of two to three orders-of magnitude compared to the DFT calculations, but fail for puckered, strained conformations, artificially improving apparent dimer-energy agreement with DFT; (iii) MACE reaches near-DFT accuracy with a further order of magnitude speed-up, although in a few cases fails to reproduce the hybrid DFT conformations. This is especially true for the chain–chain dimer structure, which is also reflected in a significant deviation of the chain–chain energy-distance potential curve, which highlights the importance of system-dependent model validation (as was done here) and suggests a need for model fine-tuning for cellulose systems; (iv) CHARMM36 has some conformational and energetic limitations,but being 30,000× faster than G-xTB (itself orders of magnitude faster than DFT), remains practical for qualitative studies.