Finally, I examine commonly used methods developed for the domain of materials science (Figure ). As with software and databases, simulation methods (DFT-GGA , Molecular Dynamics 39 , DFT-Monkhorst Pack 40 , and Ab Initio Molecular Dynamics-AIMD ) make up much of the list. The next category of methods are descriptors for materialsi.e., methods that use crystal structure or composition as an input and produce a library of features that describe the input for use in machine learning techniques. Popular methods include Magpie descriptors , SOAP descriptors , Coloumb matrix , moment tensor potentials , Atom-centered symmetry , and PL fragment descriptors . A third category is methods for ML force fields, including Behler-Parrinello potentials , GAP potentials , SNAP potentials , and Deep potentials . Finally, two of the results are separate from the categories of simulation method, materials descriptor, or force field. The first is the crystal graph neural network which popularized a neural network based approach to structureproperty relationships by solving the problem of crystal representation as a periodic graph. The second is Gaussian Processes for iterative exploration , which is becoming an increasingly popular technique as machine learning is being integrated into for materials discovery campaigns.