Comparing transferability in neural network approaches and linear models for machine-learning interaction potentials

21 March 2023, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


Atomic simulations using machine learning interatomic potential (MLIP) have gained a lot of popularity owing to their accuracy in comparison to conventional empirical potentials. However, the transferability of MLIP to systems outside the training-set poses a significant challenge. Here, we compare the transferability of three MLIP approaches: i) Neural Network Potentials (NNP), ii) Physical LassoLars Interactions Potential (PLIP) and iii) Linear Potentials with Belher-Parrinello descriptors, trained over a small but diverse configuration of zinc oxide polymorphs. We obtained good agreement between each MLIP models and density functional theory reference results for physical properties including bulk lattice parameters, surface energies, and vibrational density of states. However, the NNP model performed poorly when compared to the other two linear models for the structural optimization of nanoparticles and molecular dynamics simulation of liquid phases, which are systems outside the training-set. While providing less accurate prediction for solid Zinc Oxides phases, both linear models appear more transferable than NNP when testing for nanoscale systems and liquid phases


Neural networks
Zinc oxide
Machine learning


Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.