"Although ab initio calculations such as those involving many-body corrections can provide accurate energy-band results, the scope of such calculations is somewhat limited to about 1,000 strain points because of high computational cost. On the other hand, by discretizing ε with a regular grid comprising 20 nodes separated at each 1% strain interval over the strain range of −10 to +10%, the computational model would entail about 108 band structures, up to five orders of magnitude higher computational requirement than what can be reasonably achieved presently. To overcome these difficulties, we present here a general method that combines machine learning (ML) and ab initio calculations to identify pathways to ESE. This method invokes artificial neural networks (NNs) to predict, to a reasonable degree of accuracy, material properties as functions of the various input strain combinations on the basis of only a limited amount of data."
Further down:
"We aim to describe the electronic bandgap and band structure as functions of strain by training ML models on first-principles density-functional theory (DFT) data. This approach leads to reasonably accurate training with much fewer computed data than fine-grid ab initio calculations and a fast evaluation time."
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If I'm reading this correctly, it sounds like they already have a high-fidelity 1st-principles model that is computationally intractable to solve at scale, so they are using ML techniques to create an surrogate model that is computationally more tractable -- the AI modeling is a model reduction exercise.