we present ANI-Al, our ANI model for aluminum in both crystal and melt phases.

Our training data consists of DFT calculations, evaluated on “interesting” atomic configurations, as identified by the active learning procedure (Sec. \[meth:activelearn\]). Section 1.1 of the supplemental information describes our selected DFT/PBE parameters in detail. The input to ANI is an atomic configuration (nuclei positions and species). ANI first transforms the DFT data into a representation amenable to further processing. For this, ANI employs Behler and Parrinello [@Behler2007] type atomic descriptors, but with modified angular symmetry functions [@Smith2017]. Details of all model hyper-parameters are provided in the supplemental information Section 1.1. The output of ANI is a total energy prediction, computed as a sum over local contributions evaluated at each atom. Using backpropagation, one can efficiently calculate all forces as gradients of the predicted energy.

Each DFT calculation outputs the total system energy $E$ and the forces $\mathbf{f}_{j} = \mathrm{d} E / \mathrm{d} \mathbf{r}_j$ for all atoms $j=1\dots M$. The loss function, $$L = {\left(\hat{E}-E\right)^2+\frac{\ell_0}{M} \sum_{j=1}^{M}{\left(\hat{\mathbf f}_{j}-\mathbf f_{j}\right)^2}},$$ is a measure of disagreement between the ANI predictions for energy, $\hat{E}$, and forces, $\hat{\mathbf f}_{j} = \mathrm{d}\hat{E} / \mathrm{d} \mathbf{r}_j$, and the DFT reference data. Training ANI corresponds 