defected configurations. By training on all such automatically collected data, we produce ANI-Al, our new interatomic potential for aluminum. We demonstrate the remarkable transferability of ANI-Al by benchmarking against experimental data, e.g., the radial distribution function in melt, various properties of the stable face-centered cubic (FCC) crystal, and the coexistence curve between melt and FCC.'
author:
- 'Justin S. Smith'
- Benjamin Nebgen
- Nithin Mathew
- Jie Chen
- Nicholas Lubbers
- Leonid Burakovsky
- Sergei Tretiak
- Hai Ah Nam
- Timothy Germann
- Saryu Fensin
- Kipton Barros
bibliography:
- 'references.bib'
title: Automated discovery of a robust interatomic potential for aluminum
---

Introduction
============

Machine learning (ML) methods have become ubiquitous across many scientific disciplines. ML is capable of predicting physical phenomena at a high accuracy compared to reference data and with a relatively low computational cost, while also providing highly automated fitting procedures. ML-based models provide accurate and fast prediction of potential energies, forces, atomic charges and other properties of atomic systems [@Rupp2012; @Bleiziffer2018MachineCalculations; @Butler2018MachineScience; @Sifain2018]. Compared with classical potentials, the flexibility of ML potentials allows them to be fit to large and complex datasets, and suggests the possibility of unprecedented transferability. Applications to materials physics, 