The geometries to learn HAT energy barriers were generated in two ways: First, in a procedural approach from single amino acids, second by extracting reactive systems from a larger atomistic model simulated using MD. In the following, the structures from the procedural and MD approaches will be called synthetic systems and trajectory systems, respectively. Synthetic systems are pairs of amino acids arranged in a way that two hydrogen atoms are in a defined position to one another. As shown in Figure A, the translation distance between the hydrogens, the rotation, and the tilt angle are varied. The positions of these two central hydrogen atoms represent the start and end positions of a single hydrogen atom undergoing the HAT reaction. Furthermore, intramolecular reactions are generated from within single amino acids. Combinations of hydrogen atoms with less than 4 Å distance are considered. Systems with atoms closer than 0.8 Å to the transition path are removed. The generation of reactive systems from MD trajectories starts from a collagen model obtained from Colbuilder. The model is simulated using GROMACS 2020. In the resulting trajectory, possible reaction sites are identified by monitoring H-H distances. As energy barriers, E a , heavily depend on the translation distance, an emphasis is put on smaller translations when sampling. The HAT candidates are cut out together with their close surrounding from the bigger system, excluding solvent molecules. To generate chemically meaningful systems and to allow reference DFT calculations, the cut-out sections of the protein are capped using Nmethyl and acetyl groups. In Figure , the capping procedure is visualized.(Also See Figure B) For a given set of selected atoms in a trajectory, only the system with the smallest translation distance is kept, as otherwise a large amount of highly correlated systems would be generated. At this point, the reactive systems have been defined. Further preparation steps are applied to synthetic as well as trajectory systems. One of the two central hydrogen atoms is removed and therefore acts as the starting location of the radical. Then, the reaction path is estimated by interpolating the position of the remaining hydrogen atom from its starting position to the position of the removed hydrogen atom. We originally used 11 equally spaced interpolation increments, but after the first set of calculations of 1600 HAT reactions, all barriers were found to be located within steps 4-8, 1 or 11, so steps 2, 3, 9, and 10 are omitted in all later calculations and the data used here. This means, we define the reaction paths using seven structures. Along the reaction path, the energy of the system is calculated using the hybrid functional BMK together with the 6-31+G(2df,p) basis set, using Gaussian 09 (rev D.01). . Electronic energies were used throughout the study. The BMK functional was chosen, because it was designed for kinetic studies, and it has proven highly capable of accurately calculating HAT reaction barriers. More details on the building of the systems and the calculations are given in the ESI † . In many cases, the guess of the transition path by interpolating between the start and end position of the hydrogen whilst keeping the rest of the system frozen is reasonable, however, it introduces a significant error in conformationally flexible systems. Furthermore, since no free radical parameters are available in the MD simulations, all resulting geometries correspond to closed-shell species. (Figure ) To address this, a subset of the combined synthetic and trajectory data set is optimized at the same QM level as used for the energy calculations. More precisely, as a reaction is defined by its reactants, the transition state, and the end products, those three structures are being optimized to their energy minima or saddle point, respectively. For transition states, frequency calculations were performed to ensure the existence of a single imaginary frequency corresponding to the correct reaction. During optimization, we freeze all atom positions except the donor and acceptor atoms and directly bonded hydrogens, to reflect the embedding of the reactants into the structure of the material, here the protein backbone, and to prevent contributions to the calculated energy barrier from rearrangements in the reactants unrelated to the HAT. As the model is intended to be used on many MD trajectory snapshots, we want to restrict the DFT optimization to degrees of freedom unable to be sampled in MD, namely geometry changes due to closed-to open-shell electronic structure transition. Degrees of freedom that can be sampled in MD, like rearranging side chains, shall not be sampled during the DFT optimization, but in the preceding MD simulation. To achieve this, we tested differently sized optimization regions, details are described in the ESI † , including illustrations and statistics in Figure In this work, 4393 synthetic structures and 5261 structures from trajectories are generated. Of these structures, 10% are set aside randomly for testing. 803 structures form the optimized data set, most of them (725) originate from the trajectory data set, 78 are synthetic systems. They inherit the test/training set membership from their non-optimized versions, to ensure no structures from the training leak into the test set. Note that each structure has two associated energy barriers (one forward, one backward reaction), resulting in twice the amount of energy barriers for training/testing. In summary, 17370 energy barriers are used for training, 1938 for testing. Figure shows the distribution between the data sets.