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


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

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The updated version comprehensively analyses the transferability of Behler Parrinello Neural Network Potentials (BPNN), linear BPNNP, and linear Physical LassoLars Interaction Potentials (PLIP) for nanoparticles. In particular, you will find the following additional measurements: (1) Query by committee, (2) Information imbalance, (3) Convex hull inclusion and (4) Sampling density