Interatomic machine learning potentials for aluminium: application to solidification phenomena
Noel Jakse, Johannes Sandberg, Leon F. Granz, Anthony Saliou, Philippe, Jarry, Emilie Devijver, Thomas Voigtmann, J\"urgen Horbach, and Andreas Meyer

TL;DR
This paper develops a neural network interatomic potential for aluminium that accurately models solidification phenomena, enabling large-scale simulations of nucleation and phase transitions with near extit{ab initio} accuracy.
Contribution
It introduces a neural network potential trained on AIMD data that effectively simulates aluminium's solidification processes at larger scales.
Findings
Accurately reproduces structural, dynamic, and thermodynamic properties of aluminium in liquid and undercooled states.
Unveils nucleation mechanisms in aluminium with unprecedented accuracy close to AIMD.
Enables large-scale simulations of nucleation with one million atoms.
Abstract
In studying solidification process by simulations on the atomic scale, the modeling of crystal nucleation or amorphisation requires the construction of interatomic interactions that are able to reproduce the properties of both the solid and the liquid states. Taking into account rare nucleation events or structural relaxation under deep undercooling conditions requires much larger length scales and longer time scales than those achievable by \textit{ab initio} molecular dynamics (AIMD). This problem is addressed by means of classical MD simulations using a well established high dimensional neural network potential trained on a relevant set of configurations generated by AIMD. Our dataset contains various crystalline structures and liquid states at different pressures, including their time fluctuations in a wide range of temperatures considering only their energy labels. Applied to…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMachine Learning in Materials Science · Microstructure and mechanical properties · Aluminum Alloy Microstructure Properties
