Wiggle150: Benchmarking Density Functionals and Neural Network Potentials on Highly Strained Conformers

Accurate benchmarks are key to assessing the accuracy and robustness of computational methods, yet most available benchmark sets focus on equilibrium geometries, limiting their utility for applications involving nonequilibrium structures such as ab initio molecular dynamics and automated reaction-path exploration. To address this gap, we introduce Wiggle150, a benchmark comprising 150 highly strained conformations of adenosine, benzylpenicillin, and efavirenz. These geometriesgenerated via metadynamics and scored using DLPNO–CCSD(T)/CBS reference energiesexhibit substantially larger deviations in bond lengths, angles, dihedrals, and relative energies than other conformer benchmarks. We evaluate a diverse array of computational methods, including density-functional theory, composite quantum chemical methods, semiempirical models, neural network potentials, and force fields, on predicting relative energies for this challenging benchmark set. The results highlight multiple methods along the speed–accuracy Pareto frontier and identify AIMNet2 as particularly robust among the NNPs surveyed. We anticipate that Wiggle150 will be used to validate computational protocols involving nonequilibrium systems and guide the development of new density functionals and neural network potentials.