Accelerating high-throughput searches for new alloys with active learning of interatomic potentials
By Konstantin Gubaev, Evgeny V. Podryabinkin, Gus L. W. Hart, Alexander V. Shapeev. Computational materials science, 2018
329 citations recorded by the source corpus.
We propose an approach to materials prediction that uses a machine-learning interatomic potential to approximate quantum-mechanical energies and an active learning algorithm for the automatic selection of an optimal training dataset. Our approach significantly reduces the amount of DFT calculations needed, resorting to DFT only to produce the training data, while structural optimization is performed using the interatomic potentials. Our approach is not limited to one (or a small number of) lattice types (as is the case for cluster expansion, for example) and can predict structures with lattice types not present in the training dataset. We demonstrate the effectiveness of our algorithm by predicting the convex hull for the following three systems: Cu-Pd, Co-Nb-V, and Al-Ni-Ti. Our method is three to four orders of magnitude faster than conventional high-throughput DFT calculations and explores a wider range of materials space. In all three systems, we found unreported stable structures compared to the AFLOW database. Because our method is much cheaper and explores much more of materials space than high-throughput methods or cluster expansion, and because our interatomic potentials have a systematically improvable accuracy compared to empirical potentials such as EAM, etc., it will have a significant impact in the discovery of new alloy phases, particularly those with three or more components.
Subfields: Biomedical Engineering, Materials Chemistry, Surfaces, Coatings and Films
View Paper: https://www.semanticscholar.org/paper/ecd9a8cb9365361bc5be1e1e6e74e65e0a85b271
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
- Konstantin Gubaev
- Zongrui Pei
- Xianglin Liu
- Jiaxin Zhang
- Ivan S. Novikov
- Alexander V. Shapeev
- Evgeny V. Podryabinkin
In Materials Chemistry
- Evgeny V. Podryabinkin
- Konstantin Gubaev
- Xianglin Liu
- Jiaxin Zhang
- Zongrui Pei
- Ivan S. Novikov
- Alexander V. Shapeev
- Gábor Csányi
In Surfaces, Coatings and Films
Citations in the corpus
- The MLIP package: moment tensor potentials with MPI and active learning (2020, methodology): “The coefficients 2, 4, and 1 in (5) were empirically found to be optimal on a number of tests done in [26] and are fixed in the MLIP package.”
- Machine Learning for High-entropy Alloys: Progress, Challenges and Opportunities (2022, background): “The so-called level of moments can be defined in terms of μ and ν [160], then up to the level cutoff, all tensor moments are included in the local energy model.”