  DNA hybridization is a fundamental reaction with wide-ranging applications in
biotechnology. The nearest-neighbor (NN) model provides the most reliable
description of the energetics of duplex formation. Most DNA thermodynamics
studies have been done in melting experiments in bulk, of limited resolution
due to ensemble averaging. In contrast, single-molecule methods have reached
the maturity to derive DNA thermodynamics with unprecedented accuracy. We
combine single-DNA mechanical unzipping experiments using a temperature jump
optical trap with machine learning methods and derive the temperature-dependent
DNA energy parameters of the NN model. In particular, we measure the previously
unknown ten heat-capacity change parameters $\Delta C_p$, relevant for
thermodynamical predictions throughout the DNA stability range. Calorimetric
force spectroscopy establishes a groundbreaking methodology to accurately study
nucleic acids, from chemically modified DNA to RNA and DNA/RNA hybrid
structures.
